Proadrenomedullin as a marker for abnormal platelet levels

Abstract

The invention relates to a method for determining, diagnosis, prognosis, treatment guidance, treatment monitoring, risk assessment and/or risk stratification of patients with abnormal platelet levels comprising providing a sample of said patient, determining a level of proadrenomedullin (proADM) or fragment(s) thereof in said sample, wherein said level of proADM or fragment(s) thereof correlates with the abnormal platelet levels in said patient. In embodiments of the invention, a level of proADM or fragment(s) thereof of high severity indicates low platelet levels in the subject and subsequent initiating or modifying a treatment of the patient to improve said condition. In some embodiments of the invention the method comprises determining a level of one or more additional markers in a sample isolated from the patient, such as the level of platelets, the level of PCT or fragment(s) thereof, one or more markers of thrombocytopenia and/or one or more markers of an inflammatory response.

Claims

1. A method for reducing the risk of a patient developing thrombocytopenia, comprising a. receiving a sample of bodily fluid from said patient, b. determining a level of proadrenomedullin (proADM) or fragment(s) thereof in said sample, wherein said level of proADM or fragment(s) thereof indicates a risk of developing thrombocytopenia in said patient, and c. treating the patient to increase platelet levels, when the level of proADM or fragment(s) thereof in the patient sample is higher than or equal to a reference level (cut-off level) of proADM or fragment(s) thereof.

2. The method according to claim 1, comprising in step b) a prognosis, risk assessment and optionally risk stratification of thrombocytopenia and/or disseminated intravascular coagulation (DIC), based on a level of proADM or fragment(s) thereof in said sample.

3. The method according to claim 1, wherein the patient is, or has been diagnosed as, critically ill.

4. The method according to claim 3, wherein the sample is isolated from the patient at or after the time point of diagnosis as being critically ill.

5. The method according to claim 1, wherein the patient is diagnosed with an infectious disease.

6. The method according to claim 5, wherein the patient is diagnosed with sepsis, severe sepsis or septic shock.

7. The method according to claim 1, wherein the patient is diagnosed with one or more existing organ failure(s), and/or is a posttraumatic or postsurgical patient.

8. The method according to claim 1, wherein the patient is diagnosed with disseminated intravascular coagulation (DIC).

9. The method according to claim 1, wherein the level of proADM inversely correlates with the platelet level.

10. The method according to claim 1, wherein the sample is selected from the group consisting of a blood sample, a serum sample, a plasma sample and a urine sample.

11. The method according to claim 1, wherein determining a level of proADM or fragment(s) thereof comprises determining a level of midregion-proADM.

12. The method according to claim 1, wherein a. a level of proADM or fragment(s) thereof below a high severity level is indicative of normal or high platelet levels, or b. a level of proADM or fragment(s) thereof equal to or above a high severity level is indicative of, or likelihood of developing thrombocytopenia and optionally disseminated intravascular coagulation (DIC), and c. wherein a high severity level of proADM or fragments thereof is a level above 6.5 nmol/l±20%.

13. The method according to claim 12, wherein the level of proADM or fragment(s) thereof equal to or above the high severity level (cut-off value) indicates initiating or modifying a treatment of the patient to address the likelihood of developing thrombocytopenia and optionally disseminated intravascular coagulation (DIC).

14. The method according to claim 13, wherein the treatment is selected from the group consisting of blood or platelet transfusion, transfusion of blood components, administering drugs promoting the formation of thrombocytes and performing a splenectomy.

15. The method according to claim 12, wherein a level of proADM or fragment(s) thereof equal to or above a high severity level (or cut-off value) is indicative of an adverse event occurring.

16. The method according to claim 15, wherein an adverse event comprises one or more of thrombocytopenia, DIC, infection, organ failure, organ dysfunction and/or mortality.

17. The method according to claim 12, wherein the patients are intensive care unit (ICU)-patients, wherein a. the level of proADM or fragment(s) thereof below the high severity level (cut-off value) indicates discharging of said patient from ICU, and/or b. the level of proADM or fragment(s) thereof equal to or above the high severity level (cut-off value) indicates modifying the treatment of the patient in the ICU.

18. The method according to claim 1, comprising determining a level of one or more additional markers in a sample isolated from the patient.

19. The method according to claim 18, wherein the one or more additional markers comprises the level of platelets in a blood sample.

20. The method according to claim 18, wherein the one or more additional markers comprises PCT or fragment(s) thereof.

21. The method according to claim 18, wherein the one or more additional markers comprise one or more markers for disseminated intravascular coagulation (DIC).

22. The method according to claim 21, wherein the one or more markers for disseminated intravascular coagulation (DIC) are selected from the group consisting of membrane microparticle, sCD14-ST, prothrombinase, antithrombin and/or antithrombin activity, cationic protein 18 (CAP18), von Willebrand factor (vWF)-cleaving proteases, lipoproteins in combination with CRP, fibrinogen, fibrin, B2GP1, GPIIb-IIIa, non-denatured D-dimer of fibrin, platelet factor 4, histones and a prothrombin time (PT) Assay.

23. The method according to claim 1, comprising a. receiving a sample of bodily fluid from said patient, b. determining a level of proadrenomedullin (proADM) or fragment(s) thereof in said sample, and c. determining a level of procalcitonin (PCT) or fragment(s) thereof in said sample, d. wherein when a level of procalcitonin (PCT) or fragment(s) thereof of is >0.5 ng/ml it indicates the presence of, or increased risk of acquiring, sepsis, and wherein when a proADM level or fragment(s) thereof of is >2.75 nmol/L it indicates the presence of, or increased risk of acquiring, thrombocytopenia, and e. treating the patient wherein treating the patient comprises a treatment to increase platelet levels and a treatment of sepsis.

24. The method according to claim 23, wherein treating the patient comprises administering one or more agents selected from the group consisting of corticosteroids, a blood or platelet transfusion, a transfusion of blood components and drugs promoting the formation of thrombocytes, and additionally administering an antibiotic and/or anti-infective agent.

25. The method according to claim 1, wherein the treatment in step c) is a treatment of disseminated intravascular coagulation (DIC) comprising administering one or more agents selected from the group consisting of Factor V, Factor XII 1-AP, shingosine-1-phosphate (S1 P), thombomodulin, antibodies against tissue factor or tissue factor pre-mRNA splicing, reactive nitrogen inhibiting peptide (RNIP) fragment, TAFIa(i), Procoagulant Phospholipid, and thrombin inhibitor.

26. The method according to claim 1, wherein the treatment is selected from the group consisting of antibiotic treatment, invasive mechanical ventilation, non-invasive mechanical ventilation, renal replacement therapy, vasopressor use, fluid therapy, corticosteroids, blood or platelet transfusion, splenectomy, direct thrombin inhibitors (such as lepirudin or argatroban), blood thinners (such as bivalirudin and fondaparinux), discontinuation of heparin in case of heparin-induced thrombocytopenia, lithium carbonate, folate, extracorporal blood purification and organ protection.

27. The method according to claim 1, wherein the treatment comprises administering one or more agents selected from the group consisting of corticosteroids, a blood or platelet transfusion, a transfusion of blood components and drugs promoting the formation of thrombocytes, or performing a splenectomy.

28. The method according to claim 1, wherein a high severity level is equal to or above a cut-off value in the range of 6.5 nmol/l±20% to 12 nmol/l±20%.

29. The method according to claim 1, wherein a high severity level is equal to or above 10.9 nmol/l±20%, when the patient has been diagnosed with sepsis, severe sepsis or septic shock.

Description

DETAILED DESCRIPTION OF THE INVENTION

(1) The present invention is based on a finding that identified a correlation between proADM levels and platelet counts. The present methods enable determining, diagnosis, prognosis, treatment guidance, treatment monitoring, risk assessment and/or risk stratification of abnormal platelet levels in a patient, wherein said level of proADM or fragment(s) thereof correlates with the abnormal platelet levels in said patient.

(2) The present invention has the following advantages over the conventional methods: the inventive methods and the kits are fast, objective, easy to use and precise for therapy monitoring of critically ill patients. The methods and kits of the invention relate to markers and clinical scores that are easily measurable in routine methods in hospitals, because the levels of proADM, PCT, lactate, c-reactive protein, SOFA, APACHE II, SAPS II and/or markers for a dysregulation of the coagulation system, such as membrane microparticle, platelet count, mean platelet volume (MPV) sCD14-ST, prothrombinase, antithrombin and/antithrombin activity, cationic protein 18 (CAP18), von Willebrand factor (vWF)-cleaving proteases, lipoproteins in combination with CRP, fibrinogen, fibrin, B2GP1, GPIIb-IIIa, non-denatured D-dimer of fibrin, platelet factor 4, histones and a PT-Assay, can be determined in routinely obtained blood samples or further biological fluids or samples obtained from a subject.

(3) As used herein, the “patient” or “subject” may be a vertebrate. In the context of the present invention, the term “subject” includes both humans and animals, particularly mammals, and other organisms.

(4) In the context of the present invention, an “adverse event in the health of a patient” relates to events that indicate complications or worsening of the health state of the patient. Such adverse events include, without limitation, death of the patient, death of a patient within 28-90 days after diagnosis and treatment initiation, occurrence of an infection or a new infection, organ failure and deterioration of the patient's general clinical signs or symptoms, such as hypotension or hypertension, tachycardia or bradycardia, dysregulation of the coagulation system, disseminated intravascular coagulation, abnormal platelet levels, thrombocytopenia and dysregulated organ functions or organ failure associated with thrombocytopenia. Furthermore, examples of adverse events include situations where a deterioration of clinical symptoms indicates the requirement for therapeutic measures, such as a focus cleaning procedure, transfusion of blood products, infusion of colloids, invasive mechanical ventilation, platelet transfusion, non-invasive mechanical ventilation, emergency surgery, organ replacement therapy, such as renal or liver replacement, and vasopressor therapy. Furthermore, adverse events may include provision of corticosteroids, blood or platelet transfusion, transfusion of blood components, such as serum, plasma or specific cells or combinations thereof, drugs promoting the formation of thrombocytes, causative treatment or preforming a splenectomy.

(5) The patient described herein who has been diagnosed as being “critically ill” can be diagnosed as an intensive care unit (ICU) patient, a patient who requires constant and/or intense observation of his health state, a patient diagnosed with sepsis, severe sepsis or septic shock, a patient diagnosed with an infectious disease and one or more existing organ failure(s), a pre- or postsurgical patient, an intraoperative patient, a posttraumatic patient, a trauma patient, such as an accident patient, a burn patient, a patient with one or more open lesions. The subject described herein can be at the emergency department or intensive care unit, or in other point of care settings, such as in an emergency transporter, such as an ambulance, or at a general practitioner, who is confronted with a patient with said symptoms. Furthermore, in the context of the present invention critically ill may refer to a patient at risk of getting or having a dysregulated coagulation system. Therefore in the context of the present invention critically ill preferably refers to a patient at risk of getting or having a low platelet number (thrombocytopenia). More preferably in the context of the present invention critically ill refers to a patient at risk of getting or having a low platelet number (thrombocytopenia) secondary to a systemic infection, sepsis, severe sepsis or septic shock. Patients that are suspected to suffer from SIRS are not necessarily considered to be critically ill.

(6) The term “ICU-patient” patient relates, without limitation, a patient who has been admitted to an intensive care unit. An intensive care unit can also be termed an intensive therapy unit or intensive treatment unit (ITU) or critical care unit (CCU), is a special department of a hospital or health care facility that provides intensive treatment medicine. ICU-patients usually suffer from severe and life-threatening illnesses and injuries, which require constant, close monitoring and support from specialist equipment and medications in order to ensure normal bodily functions. Common conditions that are treated within ICUs include, without limitation, acute or adult respiratory distress syndrome (ARDS), trauma, organ failure and sepsis.

(7) As use herein, the term “coagulation system” refers to the components present in blood that enable coagulation. Coagulation (also known as clotting) is the process by which blood changes from a liquid to a gel, forming a blood clot. It potentially results in hemostasis, the cessation of blood loss from a damaged vessel, followed by repair. The mechanism of coagulation involves activation, adhesion, and aggregation of platelets along with deposition and maturation of fibrin. Disorders of coagulation are disease states which can result in bleeding (hemorrhage or bruising) or obstructive clotting (thrombosis). Coagulation begins almost instantly after an injury to the blood vessel has damaged the endothelium lining the vessel. Leaking of blood through the endothelium initiates two processes: changes in platelets, and the exposure of subendothelial tissue factor to plasma Factor VII, which ultimately leads to fibrin formation. Platelets immediately form a plug at the site of injury; this is called primary hemostasis. Secondary hemostasis occurs simultaneously: Additional coagulation factors or clotting factors beyond Factor VII respond in a complex cascade to form fibrin strands, which strengthen the platelet plug. Examples of coagulation factors comprise, without limitation, platelets, factor I (fibrinogen), factor II (prothrombin), factor III (tissue factor or tissue thromboplastin), factor IV Calcium, factor V (proaccelerin, labile factor), factor VI, factor VII (stable factor, proconvertin), factor VIII (Antihemophilic factor A), factor IX (Antihemophilic factor B or Christmas factor), factor X (Stuart-Prower factor), factor XI (plasma thromboplastin antecedent), factor XII (Hageman factor), factor XIII (fibrin-stabilizing factor), von Willebrand factor, prekallikrein (Fletcher factor), high-molecular-weight kininogen (HMWK) (Fitzgerald factor), fibronectin, antithrombin III, heparin cofactor II, protein C, protein S, protein Z, Protein Z-related protease inhibitor (ZPI), plasminogen, alpha 2-antiplasmin, tissue plasminogen activator (tPA), urokinase, plasminogen activator inhibitor-1 (PAI1), plasminogen activator inhibitor-2 (PAI2), cancer procoagulant.

(8) As used herein, the term “abnormal platelet levels” refers to a number or concentration of platelets in the blood of a patient that is unexpectedly high or low. The to be expected value depends on the status of the patient. In healthy individuals or individual without a known basic disease, predisposition or diagnosis, normal or to be expected platelet counts are in the range of about 150-450 billion platelets per L or 150,000 to 450,000 platelets per μl. This range may vary for example if it is known that a patient suffers from a condition that affects platelet numbers.

(9) Thrombocytopenia is a condition characterized by abnormally low levels of thrombocytes, also known as platelets, in the blood. A normal human platelet count ranges from 150,000 to 450,000 platelets per microliter of blood. These limits are determined by the 2.5th lower and upper percentile, so values outside this range do not necessarily indicate disease. Thrombocytopenia may require emergency treatment, especially if a platelet count below 50,000 per microliter is determined.

(10) In the context of the present invention, the term thrombocytopenia comprises all forms and/or causes leading to abnormally low levels of thrombocytes, such as abnormally low platelet production may be caused by dehydration, Vitamin B12 or folic acid deficiency, leukemia or myelodysplastic syndrome or aplastic anemia, decreased production of thrombopoietin by the liver in liver failure, sepsis, systemic viral or bacterial infection, leptospirosis, hereditary syndromes, such as congenital amegakaryocytic thrombocytopenia, thrombocytopenia absent radius syndrome, Fanconi anemia, Bernard-Soulier syndrome, (associated with large platelets), May-Hegglin anomaly, Grey platelet syndrome, Alport syndrome, Wiskott-Aldrich syndrome; abnormally high rates of platelet destruction may be due to immune or non-immune conditions, including immune thrombocytopenic purpura, thrombotic thrombocytopenic purpura, hemolytic-uremic syndrome, disseminated intravascular coagulation, paroxysmal nocturnal hemoglobinuria, antiphospholipid syndrome, systemic lupus erythematosus, post-transfusion purpura, neonatal alloimmune thrombocytopenia, hypersplenism, dengue fever, Gaucher's disease, zika virus; medication-induced thrombocytopenia, for example induced by valproic acid, methotrexate, carboplatin, interferon, isotretinoin, panobinostat, Heparin, H2 blockers and proton-pump inhibitors; and other causes such as snakebite, niacin toxicity, Lyme disease and thrombocytapheresis (also called plateletpheresis).

(11) The gold standard for measuring platelets/thrombocytes is the determination of (absolute) immature platelet counts ((A)IPC) by e.g. flow cytometry. However, this method is associated with the disadvantage that the technical validation of the platelet counts are sometimes difficult. Confounding factors make the results unreliable, leading to a requirement for an additional validation, which costs valuable time and staff. Analytical interferences can cause a pseudothrombocytopenia (e.g. by giant thrombocytes, reticulated thrombocytes, aggregation of thrombocytes or EDTA-incompatibility).

(12) The term “septic thrombocytopenia” relates to the associated presence of sepsis and low platelet levels.

(13) As used herein, “diagnosis” in the context of the present invention relates to the recognition and (early) detection of a clinical condition of a subject linked to an infectious disease. Also the assessment of the severity of the infectious disease may be encompassed by the term “diagnosis”.

(14) “Prognosis” relates to the prediction of an outcome or a specific risk for a subject based on an infectious disease. This may also include an estimation of the chance of recovery or the chance of an adverse outcome for said subject.

(15) The methods of the invention may also be used for monitoring. “Monitoring” relates to keeping track of an already diagnosed infectious disease, disorder, complication or risk, e.g. to analyze the progression of the disease or the influence of a particular treatment or therapy on the disease progression of the disease of a critically ill patient or an infectious disease in a patient.

(16) The term “therapy monitoring” or “therapy control” in the context of the present invention refers to the monitoring and/or adjustment of a therapeutic treatment of said subject, for example by obtaining feedback on the efficacy of the therapy.

(17) In the present invention, the terms “risk assessment” and “risk stratification” relate to the grouping of subjects into different risk groups according to their further prognosis. Risk assessment also relates to stratification for applying preventive and/or therapeutic measures. Examples of the risk stratification are the low, intermediate and high risk levels disclosed herein.

(18) As used herein, the term “therapy guidance” refers to application of certain therapies or medical interventions based on the value of one or more biomarkers and/or clinical parameter and/or clinical scores.

(19) It is understood that in the context of the present invention “determining the level of proADM or fragment(s) thereof” or the like refers to any means of determining proADM or a fragment thereof. The fragment can have any length, e.g. at least about 5, 10, 20, 30, 40, 50 or 100 amino acids, so long as the fragment allows the unambiguous determination of the level of proADM or fragment thereof. In particular preferred aspects of the invention, “determining the level of proADM” refers to determining the level of midregional proadrenomedullin (MR-proADM). MR-proADM is a fragment and/or region of proADM.

(20) The peptide adrenomedullin (ADM) was discovered as a hypotensive peptide comprising 52 amino acids, which had been isolated from a human phenochromocytome (Kitamura et al., 1993). Adrenomedullin (ADM) is encoded as a precursor peptide comprising 185 amino acids (“preproadrenomedullin” or “pre proADM”). An exemplary amino acid sequence of proADM is given in SEQ ID NO: 1.

(21) TABLE-US-00001 SEQ ID NO:1: amino acid sequence of pre-pro-ADM: 1 MKLVSVALMY LGSLAFLGAD TARLDVASEF RKKWNKWALS RGKRELRMSS 51 SYPTGLADVK AGPAQTLIRP QDMKGASRSP EDSSPDAARI RVKRYRQSMN 101 NFQGLRSFGC RFGTCTVQKL AHQIYQFTDK DKDNVAPRSK ISPQGYGRRR 151 RRSLPEAGPG RTLVSSKPQA HGAPAPPSGS APHFL

(22) ADM comprises the positions 95-146 of the pre-proADM amino acid sequence and is a splice product thereof. “Proadrenomedullin” (“proADM”) refers to pre-proADM without the signal sequence (amino acids 1 to 21), i.e. to amino acid residues 22 to 185 of pre-proADM. “Midregional proadrenomedullin” (“MR-proADM”) refers to the amino acids 42 to 95 of pre-proADM. An exemplary amino acid sequence of MR-proADM is given in SEQ ID NO: 2.

(23) TABLE-US-00002 SEQ ID NO:2: amino acid sequence of MR-pro-ADM  (AS 45-92 of pre-pro-ADM): ELRMSSSYPT GLADVKAGPA QTLIRPQDMK GASRSPEDSS  PDAARIRV

(24) It is also envisaged herein that a peptide and fragment thereof of pre-proADM or MR-proADM can be used for the herein described methods. For example, the peptide or the fragment thereof can comprise the amino acids 22-41 of pre-proADM (PAMP peptide) or amino acids 95-146 of pre-proADM (mature adrenomedullin, including the biologically active form, also known as bio-ADM). A C-terminal fragment of proADM (amino acids 153 to 185 of pre proADM) is called adrenotensin. Fragments of the proADM peptides or fragments of the MR-proADM can comprise, for example, at least about 5, 10, 20, 30 or more amino acids. Accordingly, the fragment of proADM may, for example, be selected from the group consisting of MR-proADM, PAMP, adrenotensin and mature adrenomedullin, preferably herein the fragment is MR-proADM.

(25) The determination of these various forms of ADM or proADM and fragments thereof also encompass measuring and/or detecting specific sub-regions of these molecules, for example by employing antibodies or other affinity reagents directed against a particular portion of the molecules, or by determining the presence and/or quantity of the molecules by measuring a portion of the protein using mass spectrometry.

(26) Any one or more of the “ADM peptides or fragments” described herein may be employed in the present invention.

(27) The methods and kits of the present invention can also comprise determining at least one further biomarker, marker, clinical score and/or parameter in addition to proADM.

(28) As used herein, a parameter is a characteristic, feature, or measurable factor that can help in defining a particular system. A parameter is an important element for health- and physiology-related assessments, such as a disease/disorder/clinical condition risk, preferably organ dysfunction(s). Furthermore, a parameter is defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. An exemplary parameter can be selected from the group consisting of Acute Physiology and Chronic Health Evaluation II (APACHE II), the simplified acute physiology score (SAPSII score), sequential organ failure assessment score (SOFA score), quick sequential organ failure assessment score (qSOFA), body mass index, weight, age, sex, IGS II, liquid intake, white blood cell count, sodium, platelet count, mean platelet volume (MPV), potassium, temperature, blood pressure, dopamine, bilirubin, respiratory rate, partial pressure of oxygen, World Federation of Neurosurgical Societies (WFNS) grading, and Glasgow Coma Scale (GCS).

(29) As used herein, terms such as “marker”, “surrogate”, “prognostic marker”, “factor” or “biomarker” or “biological marker” are used interchangeably and relate to measurable and quantifiable biological markers (e.g., specific protein or enzyme concentration or a fragment thereof, specific hormone concentration or a fragment thereof, or presence of biological substances or a fragment thereof) which serve as indices for health- and physiology-related assessments, such as a disease/disorder/clinical condition risk, preferably an adverse event. A marker or biomarker is defined as a characteristic that can be objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. Biomarkers may be measured in a sample (as a blood, serum, plasma, urine, or tissue test).

(30) The at least one further marker and/or parameter of said subject can be selected from the group consisting of a level of lactate in said sample, a level of procalcitonin (PCT) in said sample, the sequential organ failure assessment score (SOFA score) of said subject, the simplified acute physiology score (SAPSII) of said subject, the Acute Physiology and Chronic Health Evaluation II (APACHE II) score of said subject and a level of the soluble fms-like tyrosine kinase-1 (sFlt-1), Histone H2A, Histone H2B, Histone H3, Histone H4, calcitonin, Endothelin-1 (ET-1), Arginine Vasopressin (AVP), Atrial Natriuretic Peptide (ANP), Neutrophil Gelatinase-Associated Lipocalin (NGAL), Troponin, Brain Natriuretic Peptide (BNP), C-Reactive Protein (CRP), Pancreatic Stone Protein (PSP), Triggering Receptor Expressed on Myeloid Cells 1 (TREM1), Interleukin-6 (IL-6), Interleukin-1, Interleukin-24 (IL-24), Interleukin-22 (IL-22), Interleukin (IL-20) other ILs, Presepsin (sCD14-ST), Lipopolysaccharide Binding Protein (LBP), Alpha-1-Antitrypsin, Matrix Metalloproteinase 2 (MMP2), Metalloproteinase 2 (MMP8), Matrix Metalloproteinase 9 (MMP9), Matrix Metalloproteinase 7 (MMP7, Placental growth factor (PIGF), Chromogranin A, S100A protein, S100B protein and Tumor Necrosis Factor α (TNFα), Neopterin, Alpha-1-Antitrypsin, pro-arginine vasopressin (AVP, proAVP or Copeptin), procalcitonin, atrial natriuretic peptide (ANP, pro-ANP), Endothelin-1, CCL1/TCA3, CCL11, CCL12/MCP-5, CCL13/MCP-4, CCL14, CCL15, CCL16, CCL17/TARC, CCL18, CCL19, CCL2/MCP-1, CCL20, CCL21, CCL22/MDC, CCL23, CCL24, CCL25, CCL26, CCL27, CCL28, CCL3, CCL3L3, CCL4, CCL4L1/LAG-1, CCL5, CCL6, CCL7, CCL8, CCL9, CX3CL1, CXCL1, CXCL10, CXCL11, CXCL12, CXCL13, CXCL14, CXCL15, CXCL16, CXCL17, CXCL2/MIP-2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7/Ppbp, CXCL9, IL8/CXCL8, XCL1, XCL2, FAM19A1, FAM19A2, FAM19A3, FAM19A4, FAM19A5, CLCF1, CNTF, IL11, IL31, IL6, Leptin, LIF, OSM, IFNA1, IFNA10, IFNA13, IFNA14, IFNA2, IFNA4, IFNA7, IFNB1, IFNE, IFNG, IFNZ, IFNA8, IFNA5/IFNaG, IFNω/IFNW1, BAFF, 4-1BBL, TNFSF8, CD40LG, CD70, CD95L/CD178, EDA-A1, TNFSF14, LTA/TNFB, LTB, TNFa, TNFSF10, TNFSF11, TNFSF12, TNFSF13, TNFSF15, TNFSF4, IL18, IL18BP, IL1A, IL1B, IL1F10, IL1F3/IL1RA, IL1F5, IL1F6, IL1F7, IL1F8, IL1RL2, IL1F9, IL33 or a fragment thereof. Further markers comprise membrane microparticle, platelet count, mean platelet volume (MPV), sCD14-ST, prothrombinase, antithrombin and/antithrombin activity, cationic protein 18 (CAP18), von Willebrand factor (vWF)-cleaving proteases, lipoproteins in combination with CRP, fibrinogen, fibrin, B2GP1, GPIIb-IIIa, non-denatured D-dimer of fibrin, platelet factor 4, histones and a PT-Assay.

(31) Components of the coagulation system may also be considered as markers of biomarkers in the sense of the present invention and comprise, without limitation, platelets, factor I (fibrinogen), factor II (prothrombin), factor III (tissue factor or tissue thromboplastin), factor IV Calcium, factor V (proaccelerin, labile factor), factor VI, factor VII (stable factor, proconvertin), factor VIII (Antihemophilic factor A), factor IX (Antihemophilic factor B or Christmas factor), factor X (Stuart-Prower factor), factor XI (plasma thromboplastin antecedent), factor XII (Hageman factor), factor XIII (fibrin-stabilizing factor), von Willebrand factor, prekallikrein (Fletcher factor), high-molecular-weight kininogen (HMWK) (Fitzgerald factor), fibronectin, antithrombin III, heparin cofactor II, protein C, protein S, protein Z, Protein Z-related protease inhibitor (ZPI), plasminogen, alpha 2-antiplasmin, tissue plasminogen activator (tPA), urokinase, plasminogen activator inhibitor-1 (PAI1), plasminogen activator inhibitor-2 (PAI2), cancer procoagulant

(32) As used herein, “procalcitonin” or “PCT” relates to a peptide spanning amino acid residues 1-116, 2-116, 3-116, or fragments thereof, of the procalcitonin peptide. PCT is a peptide precursor of the hormone calcitonin. Thus the length of procalcitonin fragments is at least 12 amino acids, preferably more than 50 amino acids, more preferably more than 110 amino acids. PCT may comprise post-translational modifications such as glycosylation, liposidation or derivatisation. Procalcitonin is a precursor of calcitonin and katacalcin. Thus, under normal conditions the PCT levels in the circulation are very low (<about 0.05 ng/ml).

(33) The level of PCT in the sample of the subject can be determined by immunoassays as described herein. As used herein, the level of ribonucleic acid or deoxyribonucleic acids encoding “procalcitonin” or “PCT” can also be determined. Methods for the determination of PCT are known to a skilled person, for example by using products obtained from Thermo Fisher Scientific/B⋅R⋅A⋅H⋅M⋅S GmbH.

(34) It is understood that “determining the level of at least one histone” or the like refers to determining the level of at least one histone or a fragment of the at least one histone in the sample. In particular, the level of the histone H2B, H3, H2A, and/or H4 is determined in the sample. Accordingly, the at least one histone determined in the sample can be a free histone or the at least one histone determined in the sample can occur and can be assembled in a macromolecular complex, for example, in the octamer, nucleosome and/or NETs.

(35) The fragment of the at least one histone can have any length, e.g. at least about 5, 10, 20, 30, 40, 50 or 100 amino acids, so long as the fragment allows the unambiguous determination of the level of the particular histone. Various exemplary fragments of the histones are disclosed herein below that are suitable to determine the level of the histone in the sample of the subject. It is also herein understood that the level of the histones can be determined by determining a fragment spanning the N-terminal or C-terminal tail of the histones. In addition, the histone or the fragment thereof to be determined in the context of the present invention may also be modified, e.g. by post-translational modification. Exemplary post translational modifications can be acetylation, citrullination, deacetylation, methylation, demethylation, deimination, isomerization, phosphorylation and ubiquitination. Preferably, the histones or fragments thereof a circulating.

(36) In particular aspects of the invention, a level of a histone or a fragment thereof can be determined in the sample that is not assembled in a macromolecular complex, such as a nucleosome, octamer or a neutrophil extracellular trap (NET). Such histone(s) are herein referred to as “free histone(s)”. Accordingly, the level of the at least one histone may particularly be a level of at least one free histone.

(37) The level of such free histones can be determined by the detection of amino acid sequences or structural epitopes of histones that are not accessible in an assembled stoichiometric macromolecular complex, like a mono-nucleosome or an octamer. In such structures, particular regions of the histones are covered and are thus sterically inaccessible as shown for the neutrophil extracellular traps (“NETs”). In addition, in the octamer or nucleosome, regions of histones also participate in intramolecular interactions, such as between the individual histones. Accordingly, the region/peptide/epitope of the histone that is determined in the context of the invention may determine whether the histone is a free histone or a histone that is assembled in a macromolecular complex. For example, in an immunoassay based method, the utilized antibodies may not detect histones, e.g. H4, when they are part of the octameric core of nucleosomes as the epitopes are structurally inaccessible. Herein below, regions/peptides/epitopes of the histone are exemplified that could be employed to determine a free histone. For example, regions/peptides/epitopes of the N-terminal or C-terminal tail of the histones can be employed to determine histones independent of whether they are assembled in the macromolecular complex or are free histones according to the present invention.

(38) “Stoichiometric” in this context relates to intact complexes, e.g. a mononucleosome or an octamer. “Free histone proteins” can also comprise non-chromatin-bound histones. For example, “free histone proteins” may also comprise individual histone proteins or non-octameric histone complexes. Free histones may (e.g. transiently) be bound to individual histones, for instance, histones may form homo- or hetero-dimers. The free histones may also form homo- or hetero-tetramers. The homo- or heterotetramer may consist of four molecules of histones, e.g. H2A, H2B, H3 and/or H4. A typical heterotetramer is formed by two heterodimers, wherein each heterodimer consists of H3 and H4. It is also understood herein that a heterotetramer may be formed by H2A and H2B. It is also envisaged herein that a heterotetramer may be formed by one heterodimer consisting of H3 and H4, and one heterodimer consisting of H2A and H2B. Free histones are thus herein referred to as and can be monomeric, heterodimeric or tetrameric histone proteins, which are not assembled in a (“stoichiometric”) macromolecular complex consisting of the histone octamer bound to nucleic acid, e.g. a nucleosome. In addition, free histones may also be bound to nucleic acids, and wherein said free histones are not assembled in a (“stoichiometric”) macromolecular complex, e.g. an intact nucleosome. Preferably, the free histone(s) is/are essentially free of nucleic acids.

(39) Lactate, or lactic acid, is an organic compound with the formula CH.sub.3CH(OH)COOH, which occurs in bodily fluids including blood. Blood tests for lactate are performed to determine the status of the acid base homeostasis in the body. Lactic acid is a product of cell metabolism that can accumulate when cells lack sufficient oxygen (hypoxia) and must turn to a less efficient means of energy production, or when a condition causes excess production or impaired clearance of lactate. Lactic acidosis can be caused by an inadequate amount of oxygen in cells and tissues (hypoxia), for example if someone has a condition that may lead to a decreased amount of oxygen delivered to cells and tissues, such as shock, septic shock or congestive heart failure, the lactate test can be used to help detect and evaluate the severity of hypoxia and lactic acidosis.

(40) C-reactive protein (CRP) is a pentameric protein, which can be found in bodily fluids such as blood plasma. CRP levels can rise in response to inflammation. Measuring and charting CRP values can prove useful in determining disease progress or the effectiveness of treatments.

(41) As used herein, the “sequential organ failure assessment score” or “SOFA score” is one score used to track a patient's status during the stay in an intensive care unit (ICU). The SOFA score is a scoring system to determine the extent of a person's organ function or rate of failure. The score is based on six different scores, one each for the respiratory, cardiovascular, hepatic, coagulation, renal and neurological systems. Both the mean and highest SOFA scores being predictors of outcome. An increase in SOFA score during the first 24 to 48 hours in the ICU predicts a mortality rate of at least 50% up to 95%. Scores less than 9 give predictive mortality at 33% while above 14 can be close to or above 95%.

(42) As used herein, the quick SOFA score (qSOFA) is a scoring system that indicates a patient's organ dysfunction or mortality risk. The score is based on three criteria: 1) an alteration in mental status, 2) a decrease in systolic blood pressure of less than 100 mm Hg, 3) a respiration rate greater than 22 breaths per minute. Patients with two or more of these conditions are at greater risk of having an organ dysfunction or to die.

(43) As used herein, “APACHE II” or “Acute Physiology and Chronic Health Evaluation II” is a severity-of-disease classification scoring system (Knaus et al., 1985). It can be applied within 24 hours of admission of a patient to an intensive care unit (ICU) and may be determined based on 12 different physiologic parameters: AaDO2 or PaO2 (depending on FiO2), temperature (rectal), mean arterial pressure, pH arterial, heart rate, respiratory rate, sodium (serum), potassium (serum), creatinine, hematocrit, white blood cell count and Glasgow Coma Scale.

(44) As used herein, “SAPS II” or “Simplified Acute Physiology Score II” relates to a system for classifying the severity of a disease or disorder (see Le Gall J R et al., A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA. 1993; 270(24):2957-63.). The SAPS II score is made of 12 physiological variables and 3 disease-related variables. The point score is calculated from 12 routine physiological measurements, information about previous health status and some information obtained at admission to the ICU. The SAPS II score can be determined at any time, preferably, at day 2. The “worst” measurement is defined as the measure that correlates to the highest number of points. The SAPS II score ranges from 0 to 163 points. The classification system includes the followings parameters: Age, Heart Rate, Systolic Blood Pressure, Temperature, Glasgow Coma Scale, Mechanical Ventilation or CPAP, PaO2, FiO2, Urine Output, Blood Urea Nitrogen, Sodium, Potassium, Bicarbonate, Bilirubin, White Blood Cell, Chronic diseases and Type of admission. There is a sigmoidal relationship between mortality and the total SAPS II score. The mortality of a subject is 10% at a SAPSII score of 29 points, the mortality is 25% at a SAPSII score of 40 points, the mortality is 50% at a SAPSII score of 52 points, the mortality is 75% at a SAPSII score of 64 points, the mortality is 90% at a SAPSII score of 77 points (Le Gall loc. cit.).

(45) As used herein, the term “sample” is a biological sample that is obtained or isolated from the patient or subject. “Sample” as used herein may, e.g., refer to a sample of bodily fluid or tissue obtained for the purpose of diagnosis, prognosis, or evaluation of a subject of interest, such as a patient. Preferably herein, the sample is a sample of a bodily fluid, such as blood, serum, plasma, cerebrospinal fluid, urine, saliva, sputum, pleural effusions, cells, a cellular extract, a tissue sample, a tissue biopsy, a stool sample and the like. Particularly, the sample is blood, blood plasma, blood serum, or urine.

(46) Embodiments of the present invention refer to the isolation of a first sample and the isolation of a second sample. In the context of the method of the present invention, the terms “first sample” and “second sample” relate to the relative determination of the order of isolation of the samples employed in the method of the present invention. When the terms first sample and second sample are used in specifying the present method, these samples are not to be considered as absolute determinations of the number of samples taken. Therefore, additional samples may be isolated from the patient before, during or after isolation of the first and/or the second sample, or between the first or second samples, wherein these additional samples may or may not be used in the method of the present invention. The first sample may therefore be considered as any previously obtained sample. The second sample may be considered as any further or subsequent sample.

(47) “Plasma” in the context of the present invention is the virtually cell-free supernatant of blood containing anticoagulant obtained after centrifugation. Exemplary anticoagulants include calcium ion binding compounds such as EDTA or citrate and thrombin inhibitors such as heparinates or hirudin. Cell-free plasma can be obtained by centrifugation of the anticoagulated blood (e.g. citrated, EDTA or heparinized blood), for example for at least 15 minutes at 2000 to 3000 g.

(48) “Serum” in the context of the present invention is the liquid fraction of whole blood that is collected after the blood is allowed to clot. When coagulated blood (clotted blood) is centrifuged serum can be obtained as supernatant.

(49) As used herein, “urine” is a liquid product of the body secreted by the kidneys through a process called urination (or micturition) and excreted through the urethra.

(50) In preferred embodiments of the present invention the patient has been diagnosed as suffering from sepsis. More particularly, the patient may have been diagnosed as suffering from severe sepsis and/or septic shock.

(51) “Sepsis” in the context of the invention refers to a systemic response to infection. Alternatively, sepsis may be seen as the combination of SIRS with a confirmed infectious process or an infection. Sepsis may be characterized as clinical syndrome defined by the presence of both infection and a systemic inflammatory response (Levy M M et al. 2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference. Crit Care Med. 2003 April; 31(4):1250-6). The term “sepsis” used herein includes, but is not limited to, sepsis, severe sepsis, septic shock.

(52) The term “sepsis” used herein includes, but is not limited to, sepsis, severe sepsis, septic shock. Severe sepsis in refers to sepsis associated with organ dysfunction, hypoperfusion abnormality, or sepsis-induced hypotension. Hypoperfusion abnormalities include lactic acidosis, oliguria and acute alteration of mental status. Sepsis-induced hypotension is defined by the presence of a systolic blood pressure of less than about 90 mm Hg or its reduction by about 40 mm Hg or more from baseline in the absence of other causes for hypotension (e.g. cardiogenic shock). Septic shock is defined as severe sepsis with sepsis-induced hypotension persisting despite adequate fluid resuscitation, along with the presence of hypoperfusion abnormalities or organ dysfunction (Bone et al., CHEST 101(6): 1644-55, 1992).

(53) The term sepsis may alternatively be defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. For clinical operationalization, organ dysfunction can preferably be represented by an increase in the Sequential Organ Failure Assessment (SOFA) score of 2 points or more, which is associated with an in-hospital mortality greater than 10%. Septic shock may be defined as a subset of sepsis in which particularly profound circulatory, cellular, and metabolic abnormalities are associated with a greater risk of mortality than with sepsis alone. Patients with septic shock can be clinically identified by a vasopressor requirement to maintain a mean arterial pressure of 65 mm Hg or greater and serum lactate level greater than 2 mmol/L (>18 mg/dL) in the absence of hypovolemia.

(54) The term “sepsis” used herein relates to all possible stages in the development of sepsis.

(55) The term “sepsis” also includes severe sepsis or septic shock based on the SEPSIS-2 definition (Bone et al., 1992). The term “sepsis” also includes subjects falling within the SEPSIS-3 definition (Singer et al., 2016). The term “sepsis” used herein relates to all possible stages in the development of sepsis.

(56) As used herein, “infection” within the scope of the invention means a pathological process caused by the invasion of normally sterile tissue or fluid by pathogenic or potentially pathogenic agents/pathogens, organisms and/or microorganisms, and relates preferably to infection(s) by bacteria, viruses, fungi, and/or parasites. Accordingly, the infection can be a bacterial infection, viral infection, and/or fungal infection. The infection can be a local or systemic infection. For the purposes of the invention, a viral infection may be considered as infection by a microorganism.

(57) Further, the subject suffering from an infection can suffer from more than one source(s) of infection simultaneously. For example, the subject suffering from an infection can suffer from a bacterial infection and viral infection; from a viral infection and fungal infection; from a bacterial and fungal infection, and from a bacterial infection, fungal infection and viral infection, or suffer from a mixed infection comprising one or more of the infections listed herein, including potentially a superinfection, for example one or more bacterial infections in addition to one or more viral infections and/or one or more fungal infections.

(58) As used herein “infectious disease” comprises all diseases or disorders that are associated with bacterial and/or viral and/or fungal infections.

(59) According to the present invention, critically ill patients, such as septic patients may need a very strict control, with respect of vital functions and/or monitoring of organ protection and may be under medical treatment.

(60) In the context of the present invention, the term “medical treatment” or “treatment” comprises various treatments and therapeutic strategies, which comprise, without limitation, anti-inflammatory strategies, administration of proADM-antagonists such as therapeutic antibodies, si-RNA or DNA, the extracorporal blood purification or the removal of harmful substances via apheresis, dialyses, adsorbers to prevent the cytokine storm, removal of inflammatory mediators, plasma apheresis, administration of vitamines such as vitamin C, ventilation like mechanical ventilation and non-mechanical ventilation, to provide the body with sufficient oxygen, for example, focus cleaning procedures, transfusion of blood products, infusion of colloids, renal or liver replacement, antibiotic treatment, invasive mechanical ventilation, non-invasive mechanical ventilation, renal replacement therapy, vasopressor use, fluid therapy, apheresis and measures for organ protection, provision of corticosteroids, blood or platelet transfusion, transfusion of blood components, such as serum, plasma or specific cells or combinations thereof, drugs promoting the formation of thrombocytes, source control, surgeries, causative treatment or performing a splenectomy.

(61) Further treatments of the present invention comprise the administration of cells or cell products like stem cells, blood or plasma, and the stabilization of the patients circulation and the protection of endothelial glycocalyx, for example via optimal fluid management strategies, for example to reach normovolemia and prevent or treat hypervolemia or hypovolemia. Moreover, vasopressors or e.g. catecholamine as well as albumin or heparanase inhibition via unfractionated heparin or N-desulfated re-N-acetylated heparin are useful treatments to support the circulation and endothelial layer.

(62) Additionally, medical treatments of the present invention comprise, without limitation, stabilization of the blood clotting, iNOS inhibitors, anti-inflammatory agents like hydrocortisone, sedatives and analgetics as well as insuline.

(63) “Renal replacement therapy” (RRT) relates to a therapy that is employed to replace the normal blood-filtering function of the kidneys. Renal replacement therapy may refer to dialysis (e.g. hemodialysis or peritoneal dialysis), hemofiltration, and hemodiafiltration. Such techniques are various ways of diverting the blood into a machine, cleaning it, and then returning it to the body. Renal replacement therapy may also refer to kidney transplantation, which is the ultimate form of replacement in that the old kidney is replaced by a donor kidney. The hemodialysis, hemofiltration, and hemodiafiltration may be continuous or intermittent and can use an arteriovenous route (in which blood leaves from an artery and returns via a vein) or a venovenous route (in which blood leaves from a vein and returns via a vein). This results in various types of RRT. For example, the renal replacement therapy may be selected from the group of, but not limited to continuous renal replacement therapy (CRRT), continuous hemodialysis (CHD), continuous arteriovenous hemodialysis (CAVHD), continuous venovenous hemodialysis (CVVHD), continuous hemofiltration (CHF), continuous arteriovenous hemofiltration (CAVH or CAVHF), continuous venovenous hemofiltration (CVVH or CVVHF), continuous hemodiafiltration (CHDF), continuous arteriovenous hemodiafiltration (CAVHDF), continuous venovenous hemodiafiltration (CVVHDF), intermittent renal replacement therapy (IRRT), intermittent hemodialysis (IHD), intermittent venovenous hemodialysis (IVVHD), intermittent hemofiltration (IHF), intermittent venovenous hemofiltration (IVVH or IVVHF), intermittent hemodiafiltration (IHDF) and intermittent venovenous hemodiafiltration (IVVHDF).

(64) Artificial and mechanical ventilation are effective approaches to enhance proper gas exchange and ventilation and aim to save life during severe hypoxemia. Artificial ventilation relates to assisting or stimulating respiration of the subject. Artificial ventilation may be selected from the group consisting of mechanical ventilation, manual ventilation, extracorporeal membrane oxygenation (ECMO) and noninvasive ventilation (NIV). Mechanical ventilation relates to a method to mechanically assist or replace spontaneous breathing. This may involve a machine called a ventilator. Mechanical ventilation may be High-Frequency Oscillatory Ventilation or Partial Liquid Ventilation.

(65) “Fluid management” refers to the monitoring and controlling of the fluid status of a subject and the administration of fluids to stabilize the circulation or organ vitality, by e.g. oral, enteral or intravenous fluid administration. It comprises the stabilization of the fluid and electrolyte balance or the prevention or correction of hyper- or hypovolemia as well as the supply of blood products.

(66) Surgical emergencies/Emergency surgery are needed if a subject has a medical emergency and an immediate surgical intervention may be required to preserve survival or health status. The subject in need of emergency surgery may be selected from the group consisting of subjects suffering from acute trauma, an active uncontrolled infection, organ transplantation, organ-preventive or organ-stabilizing surgery or cancer.

(67) Cleaning Procedures are hygienic methods to prevent subjects from infections, especially nosocomial infections, comprising desinfection of all organic and anorganic surfaces that could get in contact with a patient, such as for example, skin, objects in the patient's room, medical devices, diagnostic devices, or room air. Cleaning procedures include the use of protective clothes and units, such as mouthguards, gowns, gloves or hygiene lock, and actions like restricted patient visits. Furthermore, cleaning procedures comprise the cleaning of the patient itself and the clothes or the patient.

(68) In the case of critical illness, such as sepsis or severe infections it is very important to have an early diagnosis as well a prognosis and risk assessment for the outcome of a patient to find the optimal therapy and management. The therapeutic approaches need to be very individual and vary from case to case. A therapeutic monitoring is needed for a best practice therapy and is influenced by the timing of treatment, the use of combined therapies and the optimization of drug dosing. A wrong or omitted therapy or management will increase the mortality rate hourly.

(69) A medical treatment of the present invention may be an antibiotic treatment, wherein one or more “antibiotics” or “antiinfective agents” may be administered if an infection has been diagnosed or symptoms of an infectious disease have been determined.

(70) Furthermore, antibiotic agents comprise bacteriophages for treatment of bacterial infections, synthetic antimicrobial peptides or iron-antagonists/iron chelator. Also, therapeutic antibodies or antagonist against pathogenic structures like anti-VAP-antibodies, anti-resistant clone vaccination, administration of immune cells, such as in vitro primed or modulated T-effector cells, are antibiotic agents that represent treatment options for critically ill patients, such as sepsis patients. Further antibiotic agents/treatments or therapeutic strategies against infection or for the prevention of new infections include the use of antiseptics, decontamination products, anti-virulence agents like liposomes, sanitation, wound care, surgery.

(71) It is also possible to combine several of the aforementioned antibiotic agents or treatments strategies with fluid therapy, platelet transfusion or transfusion of blood products.

(72) According to the present invention proADM and optionally PCT and/or other markers or clinical scores are employed as markers for therapy monitoring, comprising prognosis, prognosis, risk assessment and risk stratification of a subsequent adverse event in the health of a patient which has been diagnosed as being critically ill.

(73) A skilled person is capable of obtaining or developing means for the identification, measurement, determination and/or quantification of any one of the above proADM molecules, or fragments or variants thereof, as well as the other markers of the present invention according to standard molecular biological practice.

(74) The level of proADM or fragments thereof as well as the levels of other markers of the present invention can be determined by any assay that reliably determines the concentration of the marker. Particularly, mass spectrometry (MS) and/or immunoassays can be employed as exemplified in the appended examples. As used herein, an immunoassay is a biochemical test that measures the presence or concentration of a macromolecule/polypeptide in a solution through the use of an antibody or antibody binding fragment or immunoglobulin.

(75) Methods of determining proADM or other the markers such as PCT used in the context of the present invention are intended in the present invention. By way of example, a method may be employed selected from the group consisting of mass spectrometry (MS), luminescence immunoassay (LIA), radioimmunoassay (RIA), chemiluminescence- and fluorescence-immunoassays, enzyme immunoassay (EIA), Enzyme-linked immunoassays (ELISA), luminescence-based bead arrays, magnetic beads based arrays, protein microarray assays, rapid test formats such as for instance immunochromatographic strip tests, rare cryptate assay, and automated systems/analyzers.

(76) Determination of proADM and optionally other markers based on antibody recognition is a preferred embodiment of the invention. As used herein, the term, “antibody” refers to immunoglobulin molecules and immunologically active portions of immunoglobulin (Ig) molecules, i.e., molecules that contain an antigen binding site that specifically binds (immuno reacts with) an antigen. According to the invention, the antibodies may be monoclonal as well as polyclonal antibodies. Particularly, antibodies that are specifically binding to at lest proADM or fragments thereof are used.

(77) An antibody is considered to be specific, if its affinity towards the molecule of interest, e.g. proADM, or the fragment thereof is at least 50-fold higher, preferably 100-fold higher, most preferably at least 1000-fold higher than towards other molecules comprised in a sample containing the molecule of interest. It is well known in the art how to develop and to select antibodies with a given specificity. In the context of the invention, monoclonal antibodies are preferred. The antibody or the antibody binding fragment binds specifically to the herein defined markers or fragments thereof. In particular, the antibody or the antibody binding fragment binds to the herein defined peptides of proADM. Thus, the herein defined peptides can also be epitopes to which the antibodies specifically bind. Further, an antibody or an antibody binding fragment is used in the methods and kits of the invention that binds specifically to proADM or proADM, particularly to MR-proADM.

(78) Further, an antibody or an antibody binding fragment is used in the methods and kits of the invention that binds specifically to proADM or fragments thereof and optionally to other markers of the present inventions such as PCT. Exemplary immunoassays can be luminescence immunoassay (LIA), radioimmunoassay (RIA), chemiluminescence- and fluorescence-immunoassays, enzyme immunoassay (EIA), Enzyme-linked immunoassays (ELISA), luminescence-based bead arrays, magnetic beads based arrays, protein microarray assays, rapid test formats, rare cryptate assay. Further, assays suitable for point-of-care testing and rapid test formats such as for instance immune-chromatographic strip tests can be employed. Automated immunoassays are also intended, such as the KRYPTOR assay.

(79) Alternatively, instead of antibodies, other capture molecules or molecular scaffolds that specifically and/or selectively recognize proADM may be encompassed by the scope of the present invention. Herein, the term “capture molecules” or “molecular scaffolds” comprises molecules which may be used to bind target molecules or molecules of interest, i.e. analytes (e.g. proADM, proADM, MR-proADM, and PCT), from a sample. Capture molecules must thus be shaped adequately, both spatially and in terms of surface features, such as surface charge, hydrophobicity, hydrophilicity, presence or absence of lewis donors and/or acceptors, to specifically bind the target molecules or molecules of interest. Hereby, the binding may, for instance, be mediated by ionic, van-der-Waals, pi-pi, sigma-pi, hydrophobic or hydrogen bond interactions or a combination of two or more of the aforementioned interactions or covalent interactions between the capture molecules or molecular scaffold and the target molecules or molecules of interest. In the context of the present invention, capture molecules or molecular scaffolds may for instance be selected from the group consisting of a nucleic acid molecule, a carbohydrate molecule, a PNA molecule, a protein, a peptide and a glycoprotein. Capture molecules or molecular scaffolds include, for example, aptamers, DARpins (Designed Ankyrin Repeat Proteins). Affimers and the like are included.

(80) In certain aspects of the invention, the method is an immunoassay comprising the steps of:

(81) a) contacting the sample with

(82) i. a first antibody or an antigen-binding fragment or derivative thereof specific for a first epitope of said proADM, and ii. a second antibody or an antigen-binding fragment or derivative thereof specific for a second epitope of said proADM; and
b) detecting the binding of the two antibodies or antigen-binding fragments or derivates thereof to said proADM.

(83) Preferably, one of the antibodies can be labeled and the other antibody can be bound to a solid phase or can be bound selectively to a solid phase. In a particularly preferred aspect of the assay, one of the antibodies is labeled while the other is either bound to a solid phase or can be bound selectively to a solid phase. The first antibody and the second antibody can be present dispersed in a liquid reaction mixture, and wherein a first labeling component which is part of a labeling system based on fluorescence or chemiluminescence extinction or amplification is bound to the first antibody, and a second labeling component of said labeling system is bound to the second antibody so that, after binding of both antibodies to said proADM or fragments thereof to be detected, a measurable signal which permits detection of the resulting sandwich complexes in the measuring solution is generated. The labeling system can comprise a rare earth cryptate or chelate in combination with a fluorescent or chemiluminescent dye, in particular of the cyanine type.

(84) In a preferred embodiment, the method is executed as heterogeneous sandwich immunoassay, wherein one of the antibodies is immobilized on an arbitrarily chosen solid phase, for example, the walls of coated test tubes (e.g. polystyrol test tubes; coated tubes; CT) or microtiter plates, for example composed of polystyrol, or to particles, such as for instance magnetic particles, whereby the other antibody has a group resembling a detectable label or enabling for selective attachment to a label, and which serves the detection of the formed sandwich structures. A temporarily delayed or subsequent immobilization using suitable solid phases is also possible.

(85) The method according to the present invention can furthermore be embodied as a homogeneous method, wherein the sandwich complexes formed by the antibody/antibodies and the marker, proADM or a fragment thereof, which is to be detected remains suspended in the liquid phase. In this case it is preferred, that when two antibodies are used, both antibodies are labeled with parts of a detection system, which leads to generation of a signal or triggering of a signal if both antibodies are integrated into a single sandwich. Such techniques are to be embodied in particular as fluorescence enhancing or fluorescence quenching detection methods. A particularly preferred aspect relates to the use of detection reagents which are to be used pair-wise, such as for example the ones which are described in U.S. Pat. No. 4,882,733, EP0180492 or EP0539477 and the prior art cited therein. In this way, measurements in which only reaction products comprising both labeling components in a single immune-complex directly in the reaction mixture are detected, become possible. For example, such technologies are offered under the brand names TRACE® (Time Resolved Amplified Cryptate Emission) or KRYPTOR®, implementing the teachings of the above-cited applications. Therefore, in particular preferred aspects, a diagnostic device is used to carry out the herein provided method. For example, the level of proADM or fragments thereof and/or the level of any further marker of the herein provided method, such as PCT, is determined. In particular preferred aspects, the diagnostic device is KRYPTOR®.

(86) The level of the marker of the present invention, e.g. the proADM or fragments thereof, PCT or fragments thereof, or other markers, can also be determined by a mass spectrometric (MS) based methods. Such a method may comprise detecting the presence, amount or concentration of one or more modified or unmodified fragment peptides of e.g. proADM or the PCT in said biological sample or a protein digest (e.g. tryptic digest) from said sample, and optionally separating the sample with chromatographic methods, and subjecting the prepared and optionally separated sample to MS analysis. For example, selected reaction monitoring (SRM), multiple reaction monitoring (MRM) or parallel reaction monitoring (PRM) mass spectrometry may be used in the MS analysis, particularly to determine the amounts of proADM or fragments thereof.

(87) Herein, the term “mass spectrometry” or “MS” refers to an analytical technique to identify compounds by their mass. In order to enhance the mass resolving and mass determining capabilities of mass spectrometry, the samples can be processed prior to MS analysis. Accordingly, the invention relates to MS detection methods that can be combined with immuno-enrichment technologies, methods related to sample preparation and/or chromatographic methods, preferably with liquid chromatography (LC), more preferably with high performance liquid chromatography (HPLC) or ultra high performance liquid chromatography (UHPLC). Sample preparation methods comprise techniques for lysis, fractionation, digestion of the sample into peptides, depletion, enrichment, dialysis, desalting, alkylation and/or peptide reduction. However, these steps are optional. The selective detection of analyte ions may be conducted with tandem mass spectrometry (MS/MS). Tandem mass spectrometry is characterized by mass selection step (as used herein, the term “mass selection” denotes isolation of ions having a specified m/z or narrow range of m/z's), followed by fragmentation of the selected ions and mass analysis of the resultant product (fragment) ions.

(88) The skilled person is aware how quantify the level of a marker in the sample by mass spectrometric methods. For example, relative quantification “rSRM” or absolute quantification can be employed as described above.

(89) Moreover, the levels (including reference levels) can be determined by mass spectrometric based methods, such as methods determining the relative quantification or determining the absolute quantification of the protein or fragment thereof of interest.

(90) Relative quantification “rSRM” may be achieved by:

(91) 1. Determining increased or decreased presence of the target protein by comparing the SRM (Selected reaction monitoring) signature peak area from a given target fragment peptide detected in the sample to the same SRM signature peak area of the target fragment peptide in at least a second, third, fourth or more biological samples.
2. Determining increased or decreased presence of target protein by comparing the SRM signature peak area from a given target peptide detected in the sample to SRM signature peak areas developed from fragment peptides from other proteins, in other samples derived from different and separate biological sources, where the SRM signature peak area comparison between the two samples for a peptide fragment are normalized for e.g to amount of protein analyzed in each sample.
3. Determining increased or decreased presence of the target protein by comparing the SRM signature peak area for a given target peptide to the SRM signature peak areas from other fragment peptides derived from different proteins within the same biological sample in order to normalize changing levels of histones protein to levels of other proteins that do not change their levels of expression under various cellular conditions.
4. These assays can be applied to both unmodified fragment peptides and to modified fragment peptides of the target proteins, where the modifications include, but are not limited to phosphorylation and/or glycosylation, acetylation, methylation (mono, di, tri), citrullination, ubiquitinylation and where the relative levels of modified peptides are determined in the same manner as determining relative amounts of unmodified peptides.

(92) Absolute quantification of a given peptide may be achieved by:

(93) 1. Comparing the SRM/MRM signature peak area for a given fragment peptide from the target proteins in an individual biological sample to the SRM/MRM signature peak area of an internal fragment peptide standard spiked into the protein lysate from the biological sample. The internal standard may be a labeled synthetic version of the fragment peptide from the target protein that is being interrogated or the labeled recombinant protein. This standard is spiked into a sample in known amounts before (mandatory for the recombinant protein) or after digestion, and the SRM/MRM signature peak area can be determined for both the internal fragment peptide standard and the native fragment peptide in the biological sample separately, followed by comparison of both peak areas. This can be applied to unmodified fragment peptides and modified fragment peptides, where the modifications include but are not limited to phosphorylation and/or glycosylation, acetylation, methylation (e.g. mono-, di-, or tri-methylation), citrullination, ubiquitinylation, and where the absolute levels of modified peptides can be determined in the same manner as determining absolute levels of unmodified peptides.
2. Peptides can also be quantified using external calibration curves. The normal curve approach uses a constant amount of a heavy peptide as an internal standard and a varying amount of light synthetic peptide spiked into the sample. A representative matrix similar to that of the test samples needs to be used to construct standard curves to account for a matrix effect. Besides, reverse curve method circumvents the issue of endogenous analyte in the matrix, where a constant amount of light peptide is spiked on top of the endogenous analyte to create an internal standard and varying amounts of heavy peptide are spiked to create a set of concentration standards. Test samples to be compared with either the normal or reverse curves are spiked with the same amount of standard peptide as the internal standard spiked into the matrix used to create the calibration curve.

(94) The invention further relates to kits, the use of the kits and methods wherein such kits are used. The invention relates to kits for carrying out the herein above and below provided methods. The herein provided definitions, e.g. provided in relation to the methods, also apply to the kits of the invention. In particular, the invention relates to kits for therapy monitoring, comprising the prognosis, risk assessment or risk stratification of a subsequent adverse event in the health of a patient, wherein said kit comprises detection reagents for determining the level proADM or fragment(s) thereof, and optionally additionally for determining the level of PCT, lactate and/or C-reactive protein or fragment(s) thereof, in a sample from a subject, and—detection reagents for determining said level of proADM in said sample of said subject, and reference data, such as a reference level, corresponding to high and/or low severity levels of proADM, wherein the low severity level is below 4 nmol/l, preferably below 3 nmol/l, more preferably below 2.7 nmol/l, and the high severity level is above 6.5 nmol/l, preferably above 6.95 nmol/l, more preferably above 10.9 nmol/l, and optionally PCT, lactate and/or C-reactive protein levels, wherein said reference data is preferably stored on a computer readable medium and/or employed in the form of computer executable code configured for comparing the determined levels of proADM or fragment(s) thereof, and optionally additionally the determined levels of PCT, lactate and/or C-reactive protein or fragment(s) thereof, to said reference data.

(95) As used herein, “reference data” comprise reference level(s) of proADM and optionally PCT, lactate and/or C-reactive protein. The levels of proADM and optionally PCT, lactate and/or C-reactive protein in the sample of the subject can be compared to the reference levels comprised in the reference data of the kit. The reference levels are herein described above and are exemplified also in the appended examples. The reference data can also include a reference sample to which the level of proADM and optionally PCT, lactate and/or C-reactive protein is compared. The reference data can also include an instruction manual how to use the kits of the invention.

(96) The kit may additionally comprise items useful for obtaining a sample, such as a blood sample, for example the kit may comprise a container, wherein said container comprises a device for attachment of said container to a canula or syringe, is a syringe suitable for blood isolation, exhibits an internal pressure less than atmospheric pressure, such as is suitable for drawing a pre-determined volume of sample into said container, and/or comprises additionally detergents, chaotropic salts, ribonuclease inhibitors, chelating agents, such as guanidinium isothiocyanate, guanidinium hydrochloride, sodium dodecylsulfate, polyoxyethylene sorbitan monolaurate, RNAse inhibitor proteins, and mixtures thereof, and/or A filter system containing nitro-cellulose, silica matrix, ferromagnetic spheres, a cup retrieve spill over, trehalose, fructose, lactose, mannose, poly-ethylen-glycol, glycerol, EDTA, TRIS, limonene, xylene, benzoyl, phenol, mineral oil, anilin, pyrol, citrate, and mixtures thereof.

(97) As used herein, the “detection reagent” or the like are reagents that are suitable to determine the herein described marker(s), e.g. of proADM, PCT, lactate and/or C-reactive protein. Such exemplary detection reagents are, for example, ligands, e.g. antibodies or fragments thereof, which specifically bind to the peptide or epitopes of the herein described marker(s). Such ligands might be used in immunoassays as described above. Further reagents that are employed in the immunoassays to determine the level of the marker(s) may also be comprised in the kit and are herein considered as detection reagents. Detection reagents can also relate to reagents that are employed to detect the markers or fragments thereof by MS based methods. Such detection reagent can thus also be reagents, e.g. enzymes, chemicals, buffers, etc, that are used to prepare the sample for the MS analysis. A mass spectrometer can also be considered as a detection reagent. Detection reagents according to the invention can also be calibration solution(s), e.g. which can be employed to determine and compare the level of the marker(s).

(98) The sensitivity and specificity of a diagnostic and/or prognostic test depends on more than just the analytical “quality” of the test, they also depend on the definition of what constitutes an abnormal result. In practice, Receiver Operating Characteristic curves (ROC curves), are typically calculated by plotting the value of a variable versus its relative frequency in “normal” (i.e. apparently healthy individuals not having an infection and “disease” populations, e.g. subjects having an infection. For any particular marker (like proADM), a distribution of marker levels for subjects with and without a disease/condition will likely overlap. Under such conditions, a test does not absolutely distinguish normal from disease with 100% accuracy, and the area of overlap might indicate where the test cannot distinguish normal from disease. A threshold is selected, below which the test is considered to be abnormal and above which the test is considered to be normal or below or above which the test indicates a specific condition, e.g. infection. The area under the ROC curve is a measure of the probability that the perceived measurement will allow correct identification of a condition. ROC curves can be used even when test results do not necessarily give an accurate number. As long as one can rank results, one can create a ROC curve. For example, results of a test on “disease” samples might be ranked according to degree (e.g. 1=low, 2=normal, and 3=high). This ranking can be correlated to results in the “normal” population, and a ROC curve created. These methods are well known in the art; see, e.g., Hanley et al. 1982. Radiology 143: 29-36. Preferably, a threshold is selected to provide a ROC curve area of greater than about 0.5, more preferably greater than about 0.7, still more preferably greater than about 0.8, even more preferably greater than about 0.85, and most preferably greater than about 0.9. The term “about” in this context refers to +/−5% of a given measurement.

(99) The horizontal axis of the ROC curve represents (1-specificity), which increases with the rate of false positives. The vertical axis of the curve represents sensitivity, which increases with the rate of true positives. Thus, for a particular cut-off selected, the value of (1-specificity) may be determined, and a corresponding sensitivity may be obtained. The area under the ROC curve is a measure of the probability that the measured marker level will allow correct identification of a disease or condition. Thus, the area under the ROC curve can be used to determine the effectiveness of the test.

(100) Accordingly, the invention comprises the administration of an antibiotic suitable for treatment on the basis of the information obtained by the method described herein.

(101) As used herein, the terms “comprising” and “including” or grammatical variants thereof are to be taken as specifying the stated features, integers, steps or components but do not preclude the addition of one or more additional features, integers, steps, components or groups thereof. This term encompasses the terms “consisting of” and “consisting essentially of”.

(102) Thus, the terms “comprising”/“including”/“having” mean that any further component (or likewise features, integers, steps and the like) can/may be present. The term “consisting of” means that no further component (or likewise features, integers, steps and the like) is present.

(103) The term “consisting essentially of” or grammatical variants thereof when used herein are to be taken as specifying the stated features, integers, steps or components but do not preclude the addition of one or more additional features, integers, steps, components or groups thereof but only if the additional features, integers, steps, components or groups thereof do not materially alter the basic and novel characteristics of the claimed composition, device or method.

(104) Thus, the term “consisting essentially of” means those specific further components (or likewise features, integers, steps and the like) can be present, namely those not materially affecting the essential characteristics of the composition, device or method. In other words, the term “consisting essentially of” (which can be interchangeably used herein with the term “comprising substantially”), allows the presence of other components in the composition, device or method in addition to the mandatory components (or likewise features, integers, steps and the like), provided that the essential characteristics of the device or method are not materially affected by the presence of other components.

(105) The term “method” refers to manners, means, techniques and procedures for accomplishing a given task including, but not limited to, those manners, means, techniques and procedures either known to, or readily developed from known manners, means, techniques and procedures by practitioners of the chemical, biological and biophysical arts.

(106) The present invention is further described by reference to the following non-limiting examples.

EXAMPLES

(107) Methods of the Examples:

(108) Study Design and Patients:

(109) This study is a secondary analysis of the Placebo-Controlled Trial of Sodium Selenite and Procalcitonin Guided Antimicrobial Therapy in Severe Sepsis (SISPCT), which was performed across 33 multidisciplinary intensive care units (ICUs) throughout Germany from November 2009 until February 2013 (26). Eligibility criteria included adult patients ≥18 years presenting with new onset severe sepsis or septic shock (≤24 hours), according to the SEPSIS-1 definition of the ACCP/SCCM Consensus Conference Committee, and further classified according to the 2016 definitions (sepsis-3 and septic shock-3) (4). Details of the study design, data collection and management were described previously (26). The ethics committee of Jena University Hospital and all other centres approved the study and written informed consent was obtained whenever necessary.

(110) Biomarker Measurements:

(111) Patients were enrolled up to 24 hours after diagnosis of severe sepsis or septic shock and PCT, CRP and lactate measured immediately thereafter. PCT was measured on devices with a measuring range of 0.02-5000 ng/ml, and a functional assay sensitivity and lower detection limit of at least 0.06 ng/ml and 0.02 ng/ml, respectively. Additional blood samples from all patients were collected and stored at the central study laboratory in Jena at −80° C. MR-proADM plasma concentrations were measured retrospectively (Kryptor®, Thermo Fisher Scientific, Germany) with a limit of detection of 0.05 nmol/L. Clinical severity scores including the Sequential Organ Failure Assessment (SOFA), Acute Physiological and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiological (SAPS) II score were taken upon study enrollment.

(112) Statistical Analysis:

(113) Differences in demographic and clinical characteristics with regards to 28 day mortality were assessed using the χ2 test for categorical variables, and Student's t-test or Mann-Whitney U test for continuous variables, depending on distribution normality. Normally and non-normally distributed variables were expressed as mean (standard deviation) and median [first quartile-third quartile], respectively. The association between mortality and each biomarker and clinical score at all time points was assessed using area under the receiver operating characteristic curves (AUROC) and Cox regression analysis, with multivariate analysis corrected for age and the presence of comorbidities and septic shock. Patients were further classified into three severity subgroups (low, intermediate and high) based on the calculation of two AUROC cut-offs across the total population for each biomarker and clinical score at each time point, with a predefined sensitivity and specificity of close to 90%. A subgroup clinically stable patients was subsequently identified with an absence of any ICU associated procedures or complications (including focus cleaning procedures, emergency surgery, the emergence of new infections, transfusion of blood products, infusion of colloids, invasive mechanical ventilation, renal/liver replacement or vasopressor therapy and a deterioration in the patient's general clinical signs and symptoms), and a further group identified with corresponding low MR-proADM concentrations which had not shown any increase since the previous measurement. Mortality rates and average lengths of stay were calculated in both groups and compared against the patient group who were discharged at each specific time point.

(114) Finally, two models stratifying patients with PCT changes of 20% (baseline to day 1, based on average PCT decreases observed over this time period) and 50% (baseline to day four, based on a previously constructed model (26)) were constructed. Patient subgroups were subsequently identified based on MR-proADM severity levels, and respective mortality rates calculated. The risk of mortality within each subgroup was calculated by Cox regression analysis and illustrated by Kaplan-Meier curves. The predicted risk of developing new infections and the requirement for focus cleaning procedures and emergency surgery over days 4 to 7 were subsequently investigated in the baseline to day 4 model. All data were analysed using the statistics software R (version 3.1.2).

Example 1: Patient Characteristics

(115) Patient characteristics upon study enrollment are summarized in Table 1.

(116) A total of 1089 patients with either severe sepsis (13.0%) or septic shock (87.0%) were analysed, with 445 (41.3%) and 633 (58.7%) patients also satisfying the criteria for sepsis-3 and septic shock-3, respectively. Enrolled patients had an average age of 65.7 (13.7) years and a mean SOFA score of 10.0 (3.3) points. The 28 day all-cause mortality rate (N=1076) was 26.9% (sepsis-3: 20.0%; septic shock-3: 32.1%), with a hospital mortality rate of 33.4% (sepsis-3: 24.4%; septic shock-3: 40.4%). Infections originating from a single focus were found in 836 patients (77.7%), with pneumological (N=324; 30.1%), intra-abdominal (N=252; 23.4%), urogenital (N=57; 5.3%) and bone/soft tissue (N=50; 4.6%) origins most prevalent. Corresponding mortality rates were 26.5%, 24.6%, 22.8% and 28.0%, respectively. Multiple origins of infection were found in 240 (22.3%) patients. The most common causes of mortality included sepsis induced multiple organ failure (N=132; 45.7%), refractory septic shock (N=54; 18.7%), death due to pre-existing illness (N=35; 12.1%) and acute respiratory insufficiency (N=17; 5.9%). Other causes such as cardiogenic and hemorrhagic shock, pulmonary embolism, cerebral oedema, myocardial infarction and cardiac arrhythmia accounted for a combined mortality rate of 8.6%. A limitation of therapy was applied to 3.4% of patients.

Example 2: Association of Baseline Biomarkers and Clinical Scores with Mortality

(117) Univariate and multivariate Cox regression analysis found that MR-proADM had the strongest association with 28 day mortality across the total patient population, as well as within the sepsis-3 and septic shock-3 subgroups (Table 2). Corresponding AUROC analysis found significant differences in all biomarker and clinical score comparisons with MR-proADM, apart from APACHE II (sepsis-3 patient subgroup).

(118) Similar results were also found for 7 day, 90 day, ICU and hospital mortality prediction (Table 3), with the addition of MR-proADM to all potential biomarkers and clinical score combinations (N=63) significantly increasing prognostic capability (Table 4).

Example 3: Identification of High-Risk Patients

(119) The total patient population was further stratified according to existing SOFA severity levels, and biomarker and clinical score performance in predicting 28 day mortality assessed in each subgroup. MR-proADM showed the highest accuracy of all parameters in the low (SOFA≤g) and moderate (8≤SOFA≤13) severity SOFA subgroups (Table 5; Table 6).

(120) Two corresponding MR-proADM cut-offs were subsequently calculated to identify low (≤2.7 nmol/L) and high (>10.9 nmol/L) severity subgroups at baseline. Compared to SOFA, a more accurate reclassification could be made at both low (MR-proADM vs. SOFA:N=265 vs. 232; 9.8% vs. 13.8% mortality) and high (MR-proADM vs. SOFA:N=161 vs. 155; 55.9% vs. 41.3%) severity cut-offs (Table 7).

(121) A subgroup of 94 patients (9.3%) with high MR-proADM concentrations and corresponding low or intermediate SOFA had 28 and 90 day mortality rates of 57.4% and 68.9%, respectively, compared to 19.8% and 30.8% in the remaining patient population with low and intermediate SOFA values. Similar patterns could be found for SAPS II, APACHE II and lactate, respectively (Tables 8-10).

Example 4: Identification of Low Risk Patients Throughout ICU Stay

(122) The study cohort comprises a subset of clinically stable patients that did not face ICU related procedures or complications, such as focus cleaning procedures, emergency surgery, new infections, transfusion of blood products, infusion of colloids, invasive mechanical ventilation, renal/liver replacement, deterioration in the patient's general clinical signs and symptoms. This group of clinically stable patients was categorized as low risk patients.

(123) MR-proADM showed the strongest association with 28 day mortality across all subsequent time points (Table 11), and could provide a stable cut-off of ≤2.25 nmol/L in identifying a low risk patient population, resulting in the classification of greater patient numbers with lower mortality rates compared to other biomarkers and clinical scores (Table 12). Accordingly, 290 low MR-proADM severity patients could be identified on day 4, of which 79 (27.2%) were clinically stable and had no increase in MR-proADM concentrations from the last measurement (Table 13). A continuously low MR-proADM concentration could be found in 51 (64.6%) patients, whilst a decrease from an intermediate to low level severity level could be observed in 28 (35.4%) patients. The average ICU length of stay was 8 [7-10] days, with a 28 and 90 day mortality rate of 0.0% and 1.4%, respectively. In comparison, only 43 patients were actually discharged from the ICU on day 4, with a 28 and 90 day mortality rate of 2.3% and 10.0%. Analysis of the MR-proADM concentrations within this group of patients indicated a range of values, with 20 (52.6%), 16 (42.1%) and 2 (5.3%) patients having low, intermediate and high severity concentrations, respectively. Similar results were found for patients remaining on the ICU on days 7 and 10.

(124) MR-proADM with a stable cut-off of ≤2.25 nmol/L could identify a greater number of low risk patients with lower mortality rates compared to other biomarkers and clinical scores. Based on that finding more patients could be discharged from the ICU compared to classifications without using ADM. By discharging more patients, the hospital can more efficiently occupy ICU beds and benefits from avoided costs.

Example 5: Additional Impact of MR-proADM on Procalcitonin Guided Therapy

(125) Time-dependent Cox regression analysis indicated that the earliest significant additional increase in prognostic information to MR-proADM baseline values could be observed on day 1, with subsequent single or cumulative measurements resulting in significantly stronger associations with 28 day mortality (Table 14). Hence two PCT guided algorithm models were constructed investigating PCT changes from baseline to either day 1 or day 4, with corresponding subgroup analysis based on MR-proADM severity classifications.

(126) Patients with decreasing PCT concentrations of ≥20% from baseline to day 1 (Table 15 and Table 16) or ≥50% from baseline to day 4 (Table 17 and Table 18) were found to have 28 day mortality rates of 18.3% (N=458) and 17.1% (N=557), respectively. This decreased to 5.6% (N=125) and 1.8% (N=111) when patients had continuously low levels of MR-proADM, although increased to 66.7% (N=27) and 53.8% (N=39) in patients with continuously high MR-proADM values (HR [95% CI]: 19.1 [8.0-45.9] and 43.1 [10.1-184.0]).

(127) Furthermore, patients with decreasing PCT values of ≥50% (baseline to day 4), but continuously high or intermediate MR-proADM concentrations, had a significantly greater risk of developing subsequent nosocomial infections (HR [95% CI]: high concentrations: 3.9 [1.5-10.5]; intermediate concentrations: 2.4 [1.1-5.1] vs. patients with continuously low concentrations; intermediate concentrations: 2.9 [1.2-6.8]) vs. decreasing intermediate to low concentrations), or requiring emergency surgery (HR [95% CI]: intermediate concentrations: 2.0 [1.1-3.7] vs. decreasing intermediate to low concentrations). Conversely, patients with increasing intermediate to high concentrations were more likely to require cleaning of the infectious origin compared to those with continuously intermediate (HR [95% CI]: 3.2 [1.3-7.6]), or decreasing (HR [95% CI]: intermediate to low: 8.7 [3.1-24.8]); high to intermediate: 4.6 [1.4-14.5]) values. When PCT levels failed to decrease by ≥50%, a significantly increased risk of requiring emergency surgery was observed if MR-proADM concentrations were either at a continuously high (HR [95% CI]: 5.7 [1.5-21.9]) or intermediate (HR [95% CI]: 4.2 [1.3-13.2]) level, as opposed to being continuously low.

Example 6: Association of Baseline Biomarkers and Clinical Scores with Mortality

(128) MR-proADM showed the strongest association in patients with pneumological and intra-abdominal infections, as well as in patients with Gram positive infections, irrespective of the infectious origin (Tables 19-20). When patients were grouped according to operative emergency, non-operative emergency and elective surgery history resulting in admission to the ICU, MR-proADM provided the strongest and most balanced association with 28 day mortality across all groups (Table 21).

Example 7: Correlation of Biomarkers and Clinical Scores with SOFA at Baseline and Day 1

(129) MR-proADM had the greatest correlation of all biomarkers with the SOFA score at baseline, which was significantly increased when baseline values were correlated with day 1 SOFA scores. The greatest correlation could be found between MR-proADM and SOFA on day 10, with differences between individual SOFA subscores found throughout (Tables 22-24).

Example 8: Identification of High-Risk Patients

(130) Similar results could be found in a subgroup of 124 patients (12.0%) with high MR-proADM concentrations and either low or intermediate SAPS II values (High MR-proADM subgroup: [54.8% and 65.6% mortality]; remaining SAPS II population [19.7% and 30.0% mortality]), as well as in 109 (10.6%) patients with either low or intermediate APACHE II values (High MR-proADM subgroup: [56.9% and 66.7% mortality]; remaining APACHE II population: [19.5% and 30.3% mortality]).

Example 9: Improved Procalcitonin (PCT) Guided Therapy by Combining PCT and ADM

(131) Two PCT guided algorithm models were constructed investigating PCT changes from baseline to either day 1 or day 4, with corresponding subgroup analysis based on MR-proADM severity classifications (Tables 25-30).

(132) The previous examples show an add-on value for ADM in patients having a PCT decrease at <20% or <50%, as well as in patients where PCT decreased by ≥20% or ≥50%. However, additional analysis demonstrates that ADM can be an add-on regardless of % of decrease or even increase of PCT. Decreasing PCT values could reflect patients where the antibiotic treatment appears to be working, therefore the clinician thinks they are on a good way to survival (i.e. kill the root cause of the sepsis—the bacteria—should result in the patient getting better).

(133) For example, some patients have decreasing PCT levels from baseline (day of admission) to day 1 with a 28d mortality rate of 19%. By additionally measuring ADM, you can conclude from patients with low ADM a much higher chance of survival or much lower probability to die (Table 25; compare 19% mortality rate decreasing PCT only vs. 5% mortality rate PCT+low ADM). By having a reduced risk of dying, patients could be discharged from ICU with more confidence, or fewer diagnostic tests are required (i.e. you know they are on a good path to recovery).

(134) On the other hand, new measures need to be considered for those with a high ADM value. They are at a much higher risk with regard to mortality (compare 19% mortality rate decreasing PCT only vs 58.8% mortality rate PCT+high ADM). The physician thinks the patient is getting better due to the decrease in PCT value, but in fact the ADM concentration remains the same. It can be therefore concluded that treatment isn't working, and needs to be adapted as soon as possible).

(135) In a similar way, ADM can help to stratify those patients with increasing PCT values (Table 25).

(136) Development of New Infections

(137) PCT and MR-proADM changes were analyzed in two models, either from baseline to day 1, or from baseline to day 4. Patients were grouped according to overall PCT changes and MR-proADM severity levels.

(138) The number of new infections over days 1, 2, 3 and 4 (Table 26) and over days 4, 5, 6 and 7 (Table 27) were subsequently calculated in each patient who was present on day 1 or day 4 respectively. In some cases, patients were discharged during the observation period. It is assumed that no new infections were developed after release. Patients with multiple infections over the observation days were counted as a single new infection.

(139) As a clinical consequence, patients with high MR-proADM concentrations should potentially be treated with a broad-spectrum antibiotic on ICU admission, in conjunction with others, in order to stop the development on new infections. Special care should be taken with these patients due to their high susceptibility to pick up new infections.

(140) Requirement for Focus Cleaning

(141) PCT and MR-proADM changes were analyzed in two models, either from baseline to day 1, or from baseline to day 4. Patients were grouped according to overall PCT changes and MR-proADM severity levels.

(142) The number of focus cleaning events over days 1, 2, 3 and 4 (Table 28) and over days 4, 5, 6 and 7 (Table 29) were subsequently calculated in each patient who was present on day 1 or day 4 respectively. In some cases, patients were discharged during the observation period.

(143) Requirement of Emergency Surgery

(144) PCT and MR-proADM changes were analyzed in two models, either from baseline to day 1, or from baseline to day 4. Patients were grouped according to overall PCT changes and MR-proADM severity levels.

(145) The number of emergency surgery requirements/events over days 1, 2, 3 and 4 (Table 30) were subsequently calculated in each patient who was present on day 1. In some cases, patients were discharged during the observation period.

Example 10: Requirement for Antibiotic Change or Modification

(146) When combined within a PCT guided antibiotic algorithm, MR-proADM can stratify those patients who will require a future change or modification in antibiotic therapy, from those who will not.

(147) PCT and MR-proADM changes were analyzed in two models, either from baseline to day 1, or from baseline to day 4. Patients were grouped according to overall PCT changes and MR-proADM severity levels.

(148) The percentage of antibiotic changes on day 4 required for each patient group was subsequently calculated (Tables 31 and 32).

(149) In Patients with Decreasing PCT Values ≥50%

(150) Patients with increasing MR-proADM concentrations, from a low to intermediate severity level, were more likely to require a modification in antibiotic therapy on day 4 than those who had continuously low levels (Odds Ration [95% CI]: 1.5 [0.6-4.1]).

(151) In Patients with Decreasing PCT Values <50%

(152) Patients with either increasing MR-proADM concentrations, from an intermediate to high severity level, or continuously high concentrations, were also more likely to require changes in their antibiotic therapy on day 4 than patients with continuously low MR-proADM concentrations (Odds Ratio [95% CI]:5.9 [1.9-18.1] and 2.9 [0.8-10.4], respectively).

(153) Conclusion

(154) Despite increasing PCT concentrations, either from baseline to day 1, or baseline to day 4, patients with continuously low MR-proADM concentrations had significantly lower modifications made to their prescribed antibiotic treatment than those with continuously intermediate or high concentrations. As a clinical consequence, when faced with increasing PCT concentrations, a physician should check the patient's MR-proADM levels before deciding on changing antibiotics. Those with low MR-proADM concentrations should be considered for either an increased dose or increased strength of the same antibiotic before changes are considered. Those with higher MR-proADM concentrations should be considered for earlier antibiotic changes (i.e. on days 1 to 3, as opposed to day 4).

Example 11: Identification of Patients with Abnormal Platelet Levels and Identification of High Risk Patients with Thrombocytopenia (Tables 33, 34 and 35)

(155) Proadrenomedullin and Procalcitonin levels were measured and analyzed with regard to thrombocyte count, mortality rate and platelet transfusion at baseline and day 1. Increasing proADM and PCT concentrations correlate with decreasing platelet numbers and platelet numbers (<150.000 per μl) that reflect thrombocytopenia. The strongest decrease of platelet count was observed in patients with the highest proADM levels at baseline. Moreover increased proADM and PCT concentrations were in line with patients who required a platelet transfusion therapy. It could also confirmed that a higher mortality rate is associated with patients having thrombocytopenia and increased proADM (>6 nmol/L) and PCT (>7 ng/ml) levels.

(156) Pro-ADM levels were investigated in patients who had normal thrombocyte levels at baseline to see if increasing proADM could predict thrombocytopenia. 39.4% of patients with continually elevated proADM levels at baseline and on day 1 (proADM>10.9 nmol/l) developed thrombocytopenia. 25.6% of patients with increased proADM levels at baseline (proADM>2.75 nmol/L) and on day 1 (proADM>9.5 nmol/L) developed thrombocytopenia. 14.7% of patients with continually low proADM level at baseline and on day 1 (proADM≤2.75 nmol/L) developed thrombocytopenia. The increased level of proADM correlated with the severity of the thrombocytopenic event and the associated increased mortality rate (proADM>10.9 nmol/L mortality rate of 51%; proADM≤2.75 nmol/L mortality rate 9.1%).

(157) Example 11 refers to tables 33-35.

Discussion of Examples

(158) An accurate and rapid assessment of disease severity is crucial in order to initiate the most appropriate treatment at the earliest opportunity. Indeed, delayed or insufficient treatment may lead to a general deterioration in the patient's clinical condition, resulting in further treatment becoming less effective and a greater probability of a poorer overall outcome (8, 27). As a result, numerous biomarkers and clinical severity scores have been proposed to fulfil this unmet clinical need, with the Sequential Organ Failure Assessment (SOFA) score currently highlighted as the most appropriate tool, resulting in its central role in the 2016 sepsis-3 definition (4). This secondary analysis of the SISPCT trial (26), for the first time, compared sequential measurements of conventional biomarkers and clinical scores, such as lactate, procalcitonin (PCT) and SOFA, with those of the microcirculatory dysfunction marker, MR-proADM, in a large patient population with severe sepsis and septic shock.

(159) Our results indicate that the initial use of MR-proADM within the first 24 hours after sepsis diagnosis resulted in the strongest association with short, mid and long-term mortality compared to all other biomarkers or scores. Previous studies largely confirm our findings (17, 28, 29), however conflicting results (30) may be explained in part by the smaller sample sizes analysed, as well as other factors highlighted within this study, such as microbial species, origin of infection and previous surgical history preceding sepsis development, all of which may influence biomarker performance, thus adding to the potential variability of results in small study populations. Furthermore, our study also closely confirms the results of a previous investigation (17), highlighting the superior performance of MR-proADM in low and intermediate organ dysfunction severity patients. Indeed, Andaluz-Ojeda et al. (17) place significant importance on the patient group with low levels of organ dysfunction, since “this group represents either the earliest presentation in the clinical course of sepsis and/or the less severe form of the disease”. Nevertheless, a reasonable performance could be maintained across all severity groups with respect to mortality prediction, which was also the case across both patient groups defined according to the sepsis-3 and septic shock-3 criteria.

(160) Analysis of the sequential measurements taken after onset of sepsis allowed for the identification of specific patients groups based on disease severity. The identification of both low and high-risk patients was of significant interest in our analysis. In many ICUs, the demand for ICU beds can periodically exceed availability, which may lead to an inadequate triage, a rationing of resources, and a subsequent decrease in the likelihood of correct ICU admission (32-35). Consequently, an accurate assessment of patients with a low risk of hospital mortality that may be eligible for an early ICU discharge to a step down unit may be of significant benefit. At each time point measured within our study, MR-proADM could identify a higher number of low severity patients with the lowest ICU, hospital and 28 day mortality rates. Further analysis of the patient group with a low severity and no further ICU specific therapies indicated that an additional 4 days of ICU stay were observed at each time point after biomarker measurements were taken. When compared to the patient population who were actually discharged at each time point, a biomarker driven approach to accurately identify low severity patients resulted in decreased 28 and 90 day mortality rates. Indeed, patients who were discharged had a variety of low, intermediate and high severity MR-proADM concentrations, which was subsequently reflected in a higher mortality rate. It is, however, unknown whether a number of patients within this group still required further ICU treatment for non-microcirculatory, non-life threatening issues, or that beds in a step down unit were available. Nevertheless, such a biomarker driven approach to ICU discharge in addition to clinician judgement may improve correct stratification of the patient, with accompanied clinical benefits and potential cost savings.

(161) Conversely, the identification of high-risk patients who may require early and targeted treatment to prevent a subsequent clinical deterioration may be of even greater clinical relevance. Substantial cost savings and reductions in antibiotic use have already been observed following a PCT guided algorithm in the SISPCT study and other trials (26, 36, 37), however relatively high mortality rates can still be observed even when PCT values appear to be decreasing steadily. Our study revealed that the addition of MR-proADM to the model of PCT decreases over subsequent ICU days allowed the identification of low, intermediate and high risk patient groups, with increasing and decreasing MR-proADM severity levels from baseline to day 1 providing a sensitive and early indication as to treatment success. In addition, the prediction of the requirement for future focus cleaning or emergency surgery, as well as the susceptibility for the development of new infections, may be of substantial benefit in initiating additional therapeutic and interventional strategies, thus attempting to prevent any future clinical complications at an early stage.

(162) The strength of our study includes the thorough examination of several different subgroups with low and high disease severities from a randomized trial database, adjusting for potential cofounders and including the largest sample size of patients with sepsis, characterized by both SEPSIS 1 and 3 definitions, and information on MR-proADM kinetics. In conclusion, MR-proADM outperforms other biomarkers and clinical severity scores in the ability to identify mortality risk in patients with sepsis, both on initial diagnosis and over the course of ICU treatment. Accordingly, MR-proADM may be used as a tool to identify high severity patients who may require alternative diagnostic and therapeutic interventions, and low severity patients who may potentially be eligible for an early ICU discharge in conjunction with an absence of ICU specific therapies.

(163) Tables

(164) TABLE-US-00003 TABLE 1 Patient characteristics at baseline for survival up to 28 days Non- Total Survivors Survivors (N = 1076) (N = 787) (N = 289) P value Age (years) (mean, S.D.) 65.7 (13.7) 64.3 (14.0) 69.5 (12.0) <0.0001 Male gender (n, %) 681 (63.3%) 510 (64.8%) 171 (59.2%) 0.0907 Definitions of sepsis and length of stay Severe sepsis (n, %) 139 (12.9%) 109 (13.9%) 30 (10.4%) 0.1251 Septic shock (n, %) 937 (87.1%) 678 (86.2%) 259 (89.6%) 0.1251 Sepsis-3 (n, %) 444 (41.3%) 356 (45.4%) 88 (30.4%) <0.0001 Septic shock-3 (n, %) 630 (58.7%) 429 (54.6%) 201 (69.6%) <0.0001 ICU length of stay (days) 12 [6-23] 13 [7-26] 8 [4-15] <0.0001 (median, IQR) Hospital length of stay (days) 28 [17-45] 34 [22-51] 14 [7-23] <0.0001 (median, IQR) Pre-existing comorbidities History of diabetes (n, %) 280 (26.0%) 188 (23.9%) 92 (31.8%) 0.0094 Heart failure (n, %) 230 (21.4%) 150 (19.1%) 80 (27.7%) 0.0027 Renal dysfunction (n, %) 217 (20.2%) 135 (17.2%) 82 (28.4%) <0.0001 COPD (n, %) 131 (12.2%) 90 (11.4%) 41 (14.2%) 0.2277 Liver cirrhosis (n, %) 50 (4.7%) 27 (3.4%) 23 (8.0%) 0.0030 History of cancer (n, %) 319 (29.7%) 224 (28.5%) 95 (32.9%) 0.1630 Immunosuppression (n, %) 46 (4.3%) 30 (3.8%) 16 (5.5%) 0.2271 Microbiology Gram positive (n, %) 146 (13.6%) 113 (14.4%) 33 (11.4%) 0.2050 Gram negative (n, %) 132 (12.3%) 95 (12.1%) 37 (12.8%) 0.7467 Fungal (n, %) 51 (4.7%) 37 (4.7%) 14 (4.8%) 0.9223 Gram positive and 183 (17.0%) 133 (16.9%) 50 (17.3%) 0.8767 negative (n, %) Gram positive and 92 (8.6%) 68 (8.6%) 24 (8.3%) 0.8610 fungal (n, %) Gram negative and 51 (4.7%) 35 (4.5%) 16 (5.5%) 0.4631 fungal (n, %) Gram positive and negative 115 (10.7%) 81 (10.3%) 34 (11.8%) 0.4922 and fungal (n, %) Origin of infection Pneumonia (n, %) 453 (43.7%) 327 (42.9%) 126 (46.0%) 0.3798 Upper or lower 44 (4.3%) 29 (3.8%) 15 (5.5%) 0.2523 respiratory (n, %) Thoracic (n, %) 44 (4.3%) 35 (4.6%) 9 (3.3%) 0.3444 Bones/soft tissue (n, %) 78 (7.5%) 56 (7.4%) 22 (8.0%) 0.7161 Gastrointestinal (n, %) 80 (7.7%) 68 (8.9%) 12 (4.4%) 0.0107 Catheter associated (n, %) 30 (2.9%) 18 (2.4%) 12 (4.4%) 0.1015 Surgical wound (n, %) 41 (4.0%) 31 (4.1%) 10 (3.7%) 0.7586 Intraabdominal (n, %) 375 (36.2%) 276 (36.2%) 99 (36.1%) 0.9790 Cardiovascular (n, %) 6 (0.6%) 4 (0.5%) 2 (0.7%) 0.7082 Urogenital (n, %) 99 (9.6%) 70 (9.2%) 29 (10.6%) 0.5039 Central nervous 3 (0.3%) 2 (0.3%) 1 (0.4%) 0.7916 system (n, %) Bacteremia (n, %) 31 (3.0%) 20 (2.6%) 11 (4.0%) 0.2611 Organ dysfunction Neurological (n, %) 348 (32.3%) 240 (30.5%) 108 (37.4%) 0.0340 Respiratory (n, %) 486 (45.2%) 350 (44.5%) 136 (47.1%) 0.4502 Cardiovascular (n, %) 829 (77.0%) 584 (74.2%) 245 (84.8%) 0.0002 Renal dysfunction (n, %) 382 (35.5%) 249 (31.6%) 133 (46.0%) <0.0001 Haematological (n, %) 156 (14.5%) 89 (11.3%) 67 (23.2%) <0.0001 Gastrointestinal (n, %) 387 (36.0%) 271 (34.4%) 116 (40.1%) 0.0855 Metabolic dysfunction (n, %) 718 (66.7%) 504 (64.0%) 214 (74.1%) 0.0017 Other organ dysfunction (n, %) 499 (46.4%) 380 (48.3%) 119 (41.2%) 0.0378 Treatment upon ICU admission Invasive mechanical 789 (73.3%) 567 (72.1%) 222 (76.8%) 0.1133 ventilation (n, %) Non-invasive mechanical 64 (5.9%) 46 (5.8%) 18 (6.2%) 0.8145 ventilation (n, %) Renal replacement 326 (30.8%) 158 (20.5%) 168 (58.1%) <0.0001 therapy (n, %) Vasopressor use (n, %) 980 (91.1%) 712 (90.5%) 268 (92.7%) 0.2391 Biomarker and severity scores MR-proADM (nmol/L) 5.0 [2.6-8.8] 4.0 [2.3-7.2] 8.2 [5.2-12.6] <0.0001 (median, IQR) PCT (ng/mL) (median, IQR) 7.4 [1.6-26.9] 6.6 [1.4-25.1] 9.3 [2.6-31.8] 0.0325 Lactate (mmol/L) 2.7 [1.6-4.7] 2.4 [1.5-4.0] 3.7 [2.1-7.2] <0.0001 (median, IQR) CRP (mg/L) 188 [120.9-282] 189 [120.5-277.4] 188 [122-287] 0.7727 (median, IQR) SOFA (points) 10.02 (3.33) 9.58 (3.18) 11.22 (3.43) <0.0001 (mean, S.D.) SAPS II (points) 63.27 (14.18) 61.08 (13.71) 69.24 (13.74) <0.0001 (mean, S.D.) APACHE II (points) 24.24 (7.60) 23.05 (7.37) 27.49 (7.28) <0.0001 (mean, S.D.) ICU: Intensive Care Unit; COPD: chronic obstructive pulmonary disease; MR-proADM, mid-regional proadrenomedullin; PCT: procalcitonin; CRP: C-reactive protein; SOFA: Sequential Organ Failure Assessment; SAPS II: Simplified Acute Physiological score; APACHE II: Acute Physiological and Chronic Health Evaluation. Data are presented as absolute number and percentages in brackets, indicating the proportion of surviving and non-surviving patients at 28 days.

(165) TABLE-US-00004 TABLE 2 Prediction of 28 day mortality following sepsis diagnosis Univariate Multivariate LR C- HR IQR LR C- HR IQR N Events AUROC χ.sup.2 index [95%] p χ.sup.2 index [95%] All MR- 1030 275 0.73 142.7 0.71 3.2 [2.6-3.9] <0.0001 161.69 0.72 2.9 [2.4-3.6] patients proADM PCT 1031 275 0.56 12.2 0.56 1.4 [1.2-1.7] 0.0005 70.28 0.64 1.4 [1.1-1.7] CRP 936 251 0.49 0.12 0.51 1.0 [0.9-1.2] 0.7304 50.54 0.62 1.1 [0.9-1.2] Lactate 1066 289 0.65 78.3 0.64 2.2 [1.8-2.5] <0.0001 122.72 0.69 2.1 [1.7-2.5] SOFA 1051 282 0.64 47.3 0.62 1.6 [1.4-1.8] <0.0001 96.05 0.67 1.6 [1.4-1.8] SAPS II 1076 289 0.67 70.5 0.65 1.8 [1.6-2.0] <0.0001 100.3 0.67 1.6 [1.4-1.9] APACHE II 1076 289 0.67 69.9 0.65 1.9 [1.6-2.2] <0.0001 99.21 0.67 1.7 [1.4-2.0] Sepsis-3 MR- 425 83 0.73 40.9 0.71 2.8 [2.0-3.8] <0.0001 61.4 0.74 2.6 [1.8-3.7] proADM PCT 425 83 0.56 4.6 0.56 1.4 [1.0-1.9] 0.0312 40.6 0.70 1.5 [1.1-2.1] CRP 382 81 0.55 2.1 0.54 0.9 [0.7-1.1] 0.1505 36.7 0.69 0.9 [0.7-1.1] Lactate 439 88 0.57 7.7 0.56 1.3 [1.1-1.6] 0.0057 45.0 0.69 1.3 [1.1-1.7] SOFA 428 86 0.58 3.2 0.56 1.2 [1.0-1.5] 0.0745 40.8 0.69 1.2 [1.0-1.5] SAPS II 439 88 0.62 14.5 0.61 1.7 [1.3-2.3] 0.0001 45.0 0.69 1.5 [1.1-2.0] APACHE II 439 88 0.70 30.8 0.68 2.1 [1.6-2.6] <0.0001 52.6 0.71 1.7 [1.3-2.3] Septic MR- 597 192 0.72 77.4 0.69 2.4 [2.0-3.0] <0.0001 93.5 0.71 2.3 [1.8-2.9] shock-3 proADM PCT 597 192 0.50 0.4 0.51 1.1 [0.9-1.3] 0.5264 35.7 0.62 1.1 [0.9-1.4] CRP 545 170 0.53 2.1 0.53 1.1 [1.0-1.3] 0.1498 31.7 0.63 1.1 [1.0-1.4] Lactate 627 201 0.64 52.2 0.64 2.0 [1.7-2.4] <0.0001 79.4 0.68 2.0 [1.7-2.4] SOFA 616 196 0.65 31.1 0.62 1.6 [1.4-1.9] <0.0001 56.5 0.66 1.6 [1.3-1.9] SAPS II 627 201 0.67 42.2 0.65 1.7 [1.4-1.9] <0.0001 59.8 0.66 1.6 [1.3-1.8] APACHE II 627 201 0.63 28.3 0.61 1.6 [1.3-1.9] <0.0001 50.7 0.65 1.5 [1.3-1.8] N: Number; AUROC: Area under the Receiver Operating Curve; LR χ.sup.2: HR: Hazard Ratio; IQR: Interquartile range. All multivariate analyses were associated by p <0.0001 to 28 day mortality.

(166) TABLE-US-00005 TABLE 3 Survival analysis for 7 day, 90 day, ICU and hospital mortality Univariate Multivariate Patients Mortality LR C- HR IQR p- LR C- HR IQR (N) (N) AUROC χ.sup.2 index [95% CI] value χ.sup.2 index [95% CI] 7 MR- 1037 131 0.72 71.6 0.71 3.3 [2.4-4.3] <0.0001 82.1 0.73 3.4 [2.5-4.6] day proADM PCT 1038 131 0.58 9.7 0.58 1.5 [1.2-2.0] 0.0019 28.4 0.64 1.6 [1.2-2.1] CRP 943 111 0.55 1.2 0.55 1.1 [0.9-1.4] 0.2843 16.6 0.62 1.2 [0.9-1.4] Lactate 1074 135 0.72 86.0 0.71 3.1 [2.4-3.9] <0.0001 99.1 0.73 3.1 [2.4-4.0] SOFA 1059 130 0.63 25.5 0.63 1.7 [1.4-2.0] <0.0001 41.0 0.67 1.7 [1.4-2.1] SAPS II 1085 135 0.66 38.5 0.66 1.8 [1.5-2.2] <0.0001 50.1 0.67 1.8 [1.5-2.2] APACHE II 1085 135 0.63 24.4 0.63 1.7 [1.4-2.1] <0.0001 37.8 0.65 1.7 [1.4-2.1] 90 MR- 1000 379 0.71 146.2 0.68 2.7 [2.3-3.2] <0.0001 194.1 0.71 2.4 [2.0-2.8] day proADM PCT 1000 379 0.55 11.8 0.55 1.3 [1.1-1.5] 0.0006 113.5 0.65 1.3 [1.1-1.5] CRP 909 348 0.51 0.2 0.51 1.0 [0.9-1.2] 0.6641 92.3 0.64 1.1 [0.9-1.2] Lactate 1037 399 0.64 83.2 0.63 2.0 [1.7-2.3] <0.0001 168.8 0.68 1.9 [1.6-2.2] SOFA 1021 388 0.62 48.1 0.61 1.5 [1.4-1.7] <0.0001 143.7 0.67 1.5 [1.3-1.7] SAPS II 1045 399 0.66 81.1 0.64 1.7 [1.5-1.9] <0.0001 144.4 0.67 1.5 [1.3-1.7] APACHE II 1045 399 0.67 86.4 0.64 1.8 [1.6-2.1] <0.0001 146.8 0.67 1.6 [1.4-1.8] ICU MR- 1023 264 0.73 136.4 0.73 4.0 [3.1-5.2] <0.0001 158.3 0.75 3.7 [2.8-4.9] proADM PCT 1024 264 0.58 18.0 0.58 1.6 [1.3-2.0] <0.0001 73.0 0.67 1.6 [1.3-2.1] CRP 928 237 0.54 2.5 0.54 1.1 [1.0-1.3] 0.1108 51.4 0.65 1.2 [1.0-1.4] Lactate 1059 277 0.66 75.2 0.66 2.4 [2.0-3.0] <0.0001 115.5 0.71 2.4 [1.9-2.9] SOFA 1044 270 0.64 48.6 0.64 1.8 [1.5-2.2] <0.0001 95.2 0.69 1.8 [1.5-2.2] SAPS II 1070 277 0.65 58.7 0.65 1.9 [1.6-2.3] <0.0001 91.2 0.68 1.8 [1.5-2.2] APACHE II 1070 277 0.66 62.5 0.66 2.1 [1.7-2.6] <0.0001 91.6 0.69 1.9 [1.5-2.3] Hospital MR- 980 323 0.73 152.0 0.74 4.0 [3.1-5.2] <0.0001 186.8 0.76 3.6 [2.7-4.6] proADM PCT 981 323 0.57 15.0 0.57 1.5 [1.2-1.9] 0.0001 96.2 0.68 1.5 [1.2-1.9] CRP 891 299 0.52 0.9 0.52 1.1 [0.9-1.3] 0.3480 76.0 0.67 1.1 [1.0-1.3] Lactate 1016 342 0.66 77.8 0.66 2.4 [2.0-2.9] <0.0001 146.2 0.72 2.3 [1.9-2.9] SOFA 1001 333 0.63 41.3 0.63 1.7 [1.4-2.0] <0.0001 118.9 0.70 1.7 [1.4-2.0] SAPS II 1027 342 0.65 59.1 0.65 1.9 [1.6-2.2] <0.0001 115.9 0.69 1.7 [1.4-2.0] APACHE II 1027 342 0.67 76.7 0.67 2.2 [1.9-2.7] <0.0001 127.1 0.71 1.9 [1.6-2.4] All multivariate p values <0.0001 apart from PCT and CRP for 7 day mortality (0.0015 and 0.0843, respectively).

(167) TABLE-US-00006 TABLE 4 Survival analysis for MR-proADM when added to individual biomarkers or clinical scores Bivariate Added value Multivariate Added value Patients Mortality LR C- HR IQR LR p- LR C- HR IQR LR p- (N) (N) χ.sup.2 index [95% CI] χ.sup.2 value χ.sup.2 index [95% CI] χ.sup.2 value 7 PCT 1037 131 76.5 0.72 4.0 [2.9-5.6] 66.8 <0.0001 86.2 0.73 4.2 [2.9-6.1] 57.8 <0.0001 day CRP 904 108 56.9 0.71 3.2 [2.3-4.3] 55.0 <0.0001 67.7 0.73 3.3 [2.3-4.7] 49.4 <0.0001 Lactate 1029 131 112.5 0.75 2.3 [1.7-3.1] 28.1 <0.0001 125.1 0.76 2.4 [1.7-3.3] 26.4 <0.0001 SOFA 1014 126 77.8 0.72 3.3 [2.3-4.6] 53.5 <0.0001 86.9 0.74 3.3 [2.3-4.7] 46.6 <0.0001 SAPS II 1037 131 83.1 0.73 2.8 [2.0-3.7] 48.1 <0.0001 93.5 0.74 2.9 [2.1-4.0] 46.7 <0.0001 APACHE II 1037 131 73.3 0.71 3.0 [2.2-4.1] 50.9 <0.0001 84.5 0.73 3.1 [2.2-4.2] 48.6 <0.0001 28 PCT 1030 275 163.0 0.73 4.3 [3.4-5.5] 150.7 <0.0001 174.9 0.73 3.9 [3.0-5.1] 105.0 <0.0001 day CRP 898 239 114.4 0.70 3.0 [2.5-3.8] 114.2 <0.0001 132.4 0.72 2.8 [2.2-3.6] 80.5 <0.0001 Lactate 1022 275 163.8 0.72 2.7 [2.2-3.3] 85.9 <0.0001 184.5 0.73 2.5 [2.0-3.1] 61.4 <0.0001 SOFA 1007 268 150.6 0.72 3.1 [2.5-3.9] 104.1 <0.0001 169.9 0.73 2.8 [2.2-3.6] 74.4 <0.0001 SAPS II 1030 275 163.4 0.72 2.7 [2.2-3.3] 97.1 <0.0001 176.5 0.73 2.6 [2.1-3.3] 79.1 <0.0001 APACHE II 1030 275 153.6 0.72 2.7 [2.2-3.4] 88.8 <0.0001 169.1 0.73 2.6 [2.1-3.3] 74.1 <0.0001 90 PCT 1000 379 170.8 0.70 3.6 [3.0-4.4] 159.0 <0.0001 208.2 0.71 3.1 [2.5-3.9] 94.8 <0.0001 day CRP 872 331 116.0 0.68 2.6 [2.2-3.1] 116.0 <0.0001 160.3 0.70 2.3 [1.9-2.8] 68.8 <0.0001 Lactate 993 379 169.4 0.69 2.3 [1.9-2.7] 86.6 <0.0001 217.5 0.71 2.0 [1.7-2.4] 50.2 <0.0001 SOFA 977 368 151.0 0.69 2.6 [2.1-3.1] 103.1 <0.0001 200.6 0.71 2.2 [1.8-2.7] 59.9 <0.0001 SAPS II 1000 379 173.7 0.70 2.3 [1.9-2.7] 94.7 <0.0001 208.4 0.71 2.2 [1.8-2.6] 67.6 <0.0001 APACHE II 1000 379 165.0 0.70 2.3 [1.9-2.7] 83.3 <0.0001 202.9 0.71 2.1 [1.8-2.6] 62.5 <0.0001 ICU PCT 1023 264 149.5 0.75 5.7 [4.1-7.9] 131.4 <0.0001 165.3 0.76 4.9 [3.5-7.0] 92.6 <0.0001 CRP 889 226 104.6 0.72 3.7 [2.8-4.8] 102.5 <0.0001 127.4 0.74 3.4 [2.5-4.6] 75.6 <0.0001 Lactate 1015 264 153.5 0.74 3.2 [2.4-4.2] 78.9 <0.0001 175.6 0.76 2.9 [2.2-3.9] 57.5 <0.0001 SOFA 1000 257 140.7 0.74 3.6 [2.7-4.8] 91.8 <0.0001 163.8 0.76 3.2 [2.4-4.4] 65.8 <0.0001 SAPS II 1023 264 152.5 0.75 3.4 [2.6-4.4] 94.4 <0.0001 169.2 0.76 3.3 [2.5-4.3] 77.7 <0.0001 APACHE II 1023 264 148.2 0.74 3.3 [2.5-4.4] 87.9 <0.0001 165.7 0.76 3.3 [2.5-4.3] 75.6 <0.0001 Hospital PCT 980 323 174.7 0.76 6.4 [4.6-8.8] 159.5 <0.0001 198.9 0.77 5.2 [3.6-7.3] 103.2 <0.0001 CRP 852 283 117.9 0.72 3.7 [2.9-4.8] 117.3 <0.0001 150.1 0.75 3.3 [2.5-4.3] 77.7 <0.0001 Lactate 972 323 167.4 0.75 3.3 [2.5-4.3] 89.2 <0.0001 202.5 0.76 2.8 [2.1-3.8] 57.6 <0.0001 SOFA 957 314 155.5 0.74 3.9 [3.0-5.2] 113.7 <0.0001 191.3 0.76 3.4 [2.5-4.5] 74.6 <0.0001 SAPS II 980 323 165.8 0.75 3.5 [2.7-4.5] 107.7 <0.0001 194.2 0.76 3.2 [2.4-4.2] 81.3 <0.0001 APACHE II 980 323 169.7 0.75 3.3 [2.6-4.3] 95.4 <0.0001 197.2 0.76 3.1 [2.4-4.1] 75.1 <0.0001 HR IQR [95% CI] indicates the hazard ratio for MR-proADM in each bivariate or multivariate model. 2 degrees of freedom in each bivariate model, compared to 11 in each multivariate model.

(168) TABLE-US-00007 TABLE 5 AUROC analysis for 28 day mortality prediction based on SOFA severity levels Univariate Multivariate LR C- HR IQR LR C- HR IQR N Events AUROC χ.sup.2 index [95%] p χ.sup.2 index [95%] p SOFA ≤7 MR- 232 32 0.74 25.1 0.72 3.6 [2.2-6.0] <0.0001 37.6 0.77 3.1 [1.7-5.6] <0.0001 proADM PCT 232 32 0.55 0.9 0.55 1.3 [0.8-2.2] 0.3519 22.4 0.72 1.2 [0.7-2.1] 0.0134 CRP 210 32 0.45 1.1 0.55 1.3 [0.8-2.0] 0.2881 17.5 0.69 1.3 [0.8-2.1] 0.0647 Lactate 236 35 0.62 5.5 0.61 1.8 [1.1-3.0] 0.0186 24.3 0.71 1.7 [1.0-2.8] 0.0069 SAPS II 240 35 0.65 9.3 0.50 2.0 [1.3-3.0] 0.0023 22.5 0.71 1.4 [0.8-2.5] 0.013 APACHE II 240 35 0.69 14.3 0.64 2.4 [1.5-3.9] 0.0002 24.6 0.71 1.7 [1.0-3.0] 0.0061 SOFA MR- 620 172 0.72 74.3 0.70 2.7 [2.1-3.3] <0.0001 89.3 0.72 2.3 [1.8-3.0] <0.0001 8-13 proADM PCT 620 172 0.54 3.9 0.54 1.3 [1.0-1.6] 0.0482 46.3 0.65 1.3 [1.0-1.6] <0.0001 CRP 572 161 0.51 0.1 0.52 1.0 [0.9-1.2] 0.7932 39.3 0.64 1.0 [0.9-1.2] <0.0001 Lactate 650 181 0.61 26.9 0.61 1.7 [1.4-2.0] <0.0001 61.6 0.67 1.6 [1.3-2.0] <0.0001 SAPS II 653 181 0.64 27.7 0.57 1.6 [1.3-1.9] 0.0014 53.9 0.64 1.4 [1.2-1.7] <0.0001 APACHE II 653 181 0.63 22.1 0.62 1.5 [1.3-1.8] <0.0001 49.3 0.65 1.3 [1.1-1.6] <0.0001 SOFA ≥14 MR- 155 64 0.67 14.9 0.65 2.0 [1.4-3.0] 0.0001 25.6 0.69 2.2 [1.4-3.3] 0.0043 proADM PCT 155 64 0.49 0.2 0.52 1.1 [0.8-1.5] 0.6944 11.5 0.62 1.2 [0.8-1.7] 0.3169 CRP 136 53 0.57 2.0 0.55 0.9 [0.7-1.1] 0.1569 14.9 0.64 2.6 [1.7-3.8] 0.0004 Lactate 158 66 0.69 22.6 0.68 2.5 [1.7-3.6] <0.0001 32.3 0.71 0.9 [0.7-1.1] 0.1370 SAPS II 158 66 0.54 2.8 0.56 1.3 [0.9-1.8] 0.0930 15.3 0.63 1.2 [0.8-1.7] 0.2958 APACHE II 158 66 0.54 1.8 0.54 1.3 [0.9-1.7] 0.1754 11.8 0.62 1.2 [0.9-1.7] 0.2487 N: Number; AUROC: Area under the Receiver Operating Curve; LR χ.sup.2: HR: Hazard Ratio; IQR: Interquartile range.

(169) TABLE-US-00008 TABLE 6 Survival analysis for MR-proADM within different organ dysfunction severity groups when combined with individual biomarkers or clinical scores Univariate Multivariate Patients Mortality LR C- HR IQR p- LR C- HR IQR p- (N) (N) χ.sup.2 index [95% CI] value χ.sup.2 index [95% CI] value SOFA ≤7 PCT 232 32 30.0 0.75  5.3 [2.8-10.1] <0.0001 41.8 0.78  5.0 [2.3-10.8] <0.0001 CRP 204 29 20.1 0.71 3.1 [1.8-5.3] <0.0001 30.5 0.75 2.7 [1.4-5.0] 0.0013 Lactate 229 32 25.1 0.72 3.5 [2.0-5.9] <0.0001 37.2 0.77 3.1 [1.7-5.7] 0.0001 SOFA 232 32 27.3 0.73 3.9 [2.3-6.7] <0.0001 40.4 0.78 3.5 [1.9-6.5] <0.0001 SAPS II 232 32 28.9 0.74 3.2 [1.9-5.4] <0.0001 38.4 0.78 3.1 [1.7-5.5] 0.0001 APACHE II 232 32 34.2 0.77 2.9 [1.7-4.9] <0.0001 41.4 0.79 3.0 [1.7-5.5] <0.0001 SOFA PCT 620 172 90.4 0.72 3.8 [2.8-5.0] <0.0001 98.0 0.72 3.2 [2.3-4.4] <0.0001 8-13 CRP 544 153 63.1 0.69 2.6 [2.0-3.3] <0.0001 78.6 0.71 2.4 [1.7-2.9] <0.0001 Lactate 617 172 81.4 0.70 2.4 [1.9-3.1] <0.0001 97.0 0.72 2.1 [1.6-2.7] <0.0001 SOFA 620 172 76.2 0.70 2.6 [2.0-3.2] <0.0001 90.7 0.72 2.3 [1.8-2.9] <0.0001 SAPS II 620 172 87.2 0.71 2.4 [1.9-3.1] <0.0001 97.2 0.72 2.3 [1.8-2.9] <0.0001 APACHE II 620 172 79.0 0.70 2.5 [1.9-3.1] <0.0001 90.9 0.72 2.3 [1.8-2.9] <0.0001 SOFA ≥14 PCT 155 64 16.3 0.66 2.2 [1.5-3.2] 0.0001 27.1 0.69 2.4 [1.5-3.9] 0.0001 CRP 134 52 13.4 0.65 1.9 [1.3-2.9] 0.0007 26.9 0.70 2.1 [1.3-3.3] 0.0007 Lactate 155 64 28.9 0.69 1.7 [1.1-2.5] 0.0063 38.1 0.71 1.8 [1.1-2.8] 0.0068 SOFA 155 64 15.3 0.65 2.0 [1.3-2.9] 0.0004 26.7 0.69 2.1 [1.3-3.2] 0.0004 SAPS II 155 64 17.0 0.65 2.1 [1.4-3.1] 0.0001 26.2 0.69 2.2 [1.4-3.3] 0.0001 APACHE II 155 64 15.1 0.64 2.0 [1.3-2.9] 0.0002 25.7 0.69 2.1 [1.4-3.3] 0.0002

(170) TABLE-US-00009 TABLE 7 Corresponding 28 day SOFA and MR-proADM disease severity groups SOFA severity groups Low severity Intermediate severity High severity (≤7 points) (≤8 points ≤13) (≥14 points) N = 232, N = 620, 27.7% N = 155, 41.3% 13.8% mortality mortality mortality MR- Low severity N = 111 (41.9%) N = 139 (52.8%) N = 15 (5.7%) proADM (≤2.7 nmol/L) 7.2% mortality 10.8% mortality 20.0% mortality severity N = 265, 9.8% groups mortality Intermediate severity N = 114 (19.6%) N = 394 (68.0%) N = 73 (12.6%) (<2.7 nmol/L ≤10.9) 15.8% mortality 27.7% mortality 34.2% mortality N = 581, 26.2% mortality High severity N = 7 (4.3%) N = 87 (53.4%) N = 67 (41.6%) (>10.9 nmol/L) 85.7% 55.2% 53.7% N = 161 55.9% mortality mortality mortality mortality MR-proADM: mid-regional proadrenomedullin; SOFA: Sequential Organ Failure Assessment

(171) TABLE-US-00010 TABLE 8 Corresponding 28 day SAPS II and MR-proADM disease severity groups SAPS II severity groups Low severity Intermediate High severity (≤53 points) severity (≥80 points) N = 235, (≤54 points ≤79) N = 139, 11.5% N = 656, 29.3% 40.3% mortality mortality mortality MR- Low severity N = 108 (39.9%) N = 143 (52.8%) N = 20 (7.4%) proADM (≤2.7 nmol/L) 7.4% mortality 11.2% mortality 20.0% mortality severity N = 271, 10.3% groups mortality Intermediate severity N = 118 (19.9%) N = 398 (67.0%) N = 78 (13.1%) (<2.7 nmol/L ≤10.9) 13.6% morality 27.9% mortality 38.5% mortality N = 594, 26.4% mortality High severity N = 9 (5.5%) N = 115 (69.7%) N = 41 (24.8%) (>10.9 nmol/L) 33.3% mortality 56.5% mortality 53.7% mortality N = 165, 54.5% mortality MR-proADM: mid-regional proadrenomedullin; SAPS II: Simplified Acute Physiological II

(172) TABLE-US-00011 TABLE 9 Corresponding 28 day APACHE II and MR-proADM disease severity groups APACHE II severity groups Low severity Intermediate High severity (≤19 points) severity (≥33 points) N = 287, (≤20 points ≤32) N = 152, 11.5% N = 591, 30.3% 41.4% mortality mortality mortality MR- Low severity N = 122 (45.0%) N = 137 (50.6%) N = 12 (4.4%) proADM (≤2.7 nmol/L) 7.4% mortality 10.9% mortality 33.3% mortality severity N = 271, 10.3% groups mortality Intermediate severity N = 154 (25.9%) N = 356 (59.9%) N = 84 (14.1%) (<2.7 nmol/L ≤10.9) 12.3% morality 30.1% mortality 36.9% mortality N = 594, 26.4% mortality High severity N = 11 (6.7%) N = 98 (59.4%) N = 56 (33.9%) (>10.9 nmol/L) 45.5% mortality 58.2% mortality 50.0% mortality N = 165, 54.5% mortality MR-proADM: mid-regional proadrenomedullin; APACHE II: Acute Physiological and Chronic Health Evaluation II

(173) TABLE-US-00012 TABLE 10 Corresponding 28 day lactate and MR-proADM disease severity groups Lactate severity groups Low severity Intermediate High severity (≤1.4 mmol/L) severity (>6.4 mmol/L) N = 196, (<1.4 mmol/L ≤6.4) N = 158, 15.8% N = 668, 24.1% 52.5% mortality mortality mortality MR- Low severity N = 99 (37.1%) N = 154 (57.7%) N = 14 (5.2%) proADM (≤2.7 nmol/L) 8.1% mortality 9.1% mortality 42.9% mortality severity N = 267, 10.5% groups mortality Intermediate severity N = 90 (15.2%) N = 421 (71.2%) N = 80 (13.5%) (<2.7 nmol/L ≤10.9) 21.1% morality 25.2% mortality 40.0% mortality N = 591, 26.6% mortality High severity N = 7 (4.3%) N = 93 (56.7%) N = 64 (39.0%) (>10.9 nmol/L) 57.1% mortality 44.1% mortality 70.3% mortality N = 164, 54.9% mortality MR-proADM: mid-regional proadrenomedullin

(174) TABLE-US-00013 TABLE 11 Biomarker and SOFA association with 28 day mortality at days 1, 4, 7 and 10 Patients Mortality LR C- HR IQR p- LR C- HR IQR p- (N) (N) AUROC χ.sup.2 index [95% CI] value χ.sup.2 index [95% CI] value Day MR- 993 242 0.76 152.5 0.73 3.3 [2.8-4.0] <0.0001 173.2 0.74 3.2 [2.6-4.0] <0.0001 1 proADM PCT 993 242 0.59 23.1 0.59 1.6 [1.3-2.0] <0.0001 74.6 0.65 1.6 [1.3-2.0] <0.0001 CRP 919 226 0.54 6.2 0.54 0.9 [0.8-1.0] 0.0128 61.2 0.65 0.9 [0.8-1.0] <0.0001 Lactate 1041 265 0.73 206.4 0.72 2.4 [2.2-2.7] <0.0001 253.9 0.75 2.5 [2.2-2.8] <0.0001 SOFA 1011 260 0.74 143.8 0.72 2.5 [2.2-2.9] <0.0001 192.8 0.75 2.6 [2.2-3.0] <0.0001 Day MR- 777 158 0.76 100.5 0.73 3.2 [2.5-4.0] <0.0001 123.7 0.75 3.0 [2.3-3.8] <0.0001 4 proADM PCT 777 158 0.62 22.6 0.61 1.7 [1.4-2.1] <0.0001 69.3 0.68 1.8 [1.4-2.2] <0.0001 CRP 708 146 0.48 0.7 0.52 1.1 [0.9-1.3] 0.3925 45.8 0.65 1.1 [0.9-1.4] <0.0001 Lactate 803 166 0.69 60.6 0.68 1.8 [1.6-2.0] <0.0001 100.9 0.71 1.7 [1.5-2.0] <0.0001 SOFA 767 162 0.75 111.5 0.72 3.0 [2.4-3.6] <0.0001 155.9 0.76 3.1 [2.5-3.8] <0.0001 Day MR- 630 127 0.78 93.7 0.76 3.4 [2.6-4.3] <0.0001 117.8 0.76 3.3 [2.5-4.3] <0.0001 7 proADM PCT 631 128 0.72 62.3 0.70 2.6 [2.1-3.3] <0.0001 101.6 0.74 2.7 [2.1-3.4] <0.0001 CRP 583 121 0.56 3.5 0.55 1.3 [1.0-1.6] 0.0606 47.1 0.67 1.3 [1.0-1.7] <0.0001 Lactate 658 138 0.68 69.4 0.68 2.0 [1.7-2.3] <0.0001 112.2 0.73 2.0 [1.7-2.4] <0.0001 SOFA 617 128 0.75 107.7 0.73 2.7 [2.3-3.3] <0.0001 140.2 0.77 2.8 [2.3-3.4] <0.0001 Day MR- 503 82 0.78 72.6 0.76 4.3 [3.0-6.1] <0.0001 90.9 0.78 3.8 [2.6-5.5] <0.0001 10 proADM PCT 503 82 0.75 52.0 0.74 2.8 [2.2-3.7] <0.0001 90.4 0.78 3.1 [2.3-4.2] <0.0001 CRP 457 80 0.61 10.0 0.60 1.6 [1.2-2.2] <0.0001 51.2 0.71 1.8 [1.3-2.6] <0.0001 Lactate 516 88 0.61 19.8 0.61 1.6 [1.3-2.0] <0.0001 54.7 0.70 1.6 [1.3-2.0] <0.0001 SOFA 490 84 0.76 85.8 0.75 3.3 [2.6-4.3] <0.0001 107.8 0.78 3.1 [2.4-4.1] <0.0001

(175) TABLE-US-00014 TABLE 12 Low and high risk severity groups and corresponding mortality rates throughout ICU treatment Low severity patient population High severity patient population Patients Mortality Optimal Patients Mortality Optimal (N) (N, %) cut-off Sensitivity Specificity (N) (N, %) cut-off Sensitivity Specificity Day MR- 304 24 (7.9%) 2.80 0.90 0.37 162 87 (53.7%) 9.5 0.36 0.90 1 proADM PCT 203 25 (12.3%) 1.02 0.90 0.24 115 40 (34.8%) 47.6 0.17 0.90 CRP 101 32 (31.7%) 99 0.90 0.14 88 18 (4.8%) 373 0.08 0.90 Lactate 310 33 (10.6%) 1.22 0.88 0.36 185 109 (58.9%) 3.5 0.43 0.89 SOFA 435 49 (11.3%) 8.0 0.88 0.40 165 87 (52.7%) 14 0.33 0.90 Day MR- 290 16 (5.5%) 2.25 0.90 0.44 120 58 (48.3%) 7.7 0.37 0.90 4 proADM PCT 147 16 (10.9%) 0.33 0.90 0.21 87 25 (28.7%) 14.08 0.16 0.90 CRP 65 9 (13.8%) 32.7 0.90 0.06 51 15 (29.4%) 276.5 0.06 0.90 Lactate 124 15 (12.1%) 0.89 0.91 0.17 136 65 (47.8%) 2.15 0.39 0.89 SOFA 213 15 (7.0%) 5.5 0.91 0.33 137 67 (48.9%) 12.75 0.41 0.88 Day MR- 252 14 (5.6%) 2.25 0.89 0.47 104 54 (51.9%) 6.95 0.43 0.90 7 proADM PCT 184 14 (7.6%) 0.31 0.89 0.34 85 35 (41.2%) 4.67 0.27 0.90 CRP 62 12 (19.4%) 27.4 0.90 0.11 69 23 (37.7%) 207 0.19 0.90 Lactate 104 15 (14.4%) 0.84 0.89 0.17 102 51 (50.0%) 2.10 0.37 0.90 SOFA 207 16 (7.7%) 5.5 0.88 0.39 91 48 (52.7%) 12.5 0.38 0.91 Day MR- 213 8 (3.8%) 2.25 0.90 0.49 78 35 (44.9%) 7.45 0.43 0.90 10 proADM PCT 177 9 (5.1%) 0.30 0.89 0.40 74 32 (43.2%) 2.845 0.39 0.90 CRP 69 8 (11.6%) 32.1 0.90 0.16 52 14 (26.9%) 204 0.18 0.90 Lactate 47 7 (14.9%) 0.68 0.92 0.09 65 24 (36.9%) 2.15 0.27 0.90 SOFA 116 9 (7.8%) 4.5 0.89 0.26 85 42 (49.4%) 11.5 0.50 0.89

(176) TABLE-US-00015 TABLE 13 Mortality and duration of ICU therapy based on MR-proADM concentrations and ICU specific therapies Length 28 day 90 day Patient of stay mortality mortality severity group N SOFA (days) (N, %) (N, %) Day 4 Total 777 8.4 (4.3) 16 [10-27] 158 (20.3%) 256 (33.9%) patient population Clinically 145 4.5 (2.4) 8 [6-11] 10 (6.9%) 22 (15.8%) stable Clinically 79 3.6 (1.5) 8 [7-10] 0 (0.0%) 1 (1.4%) stable and low MR-proADM Actual 43 3.6 (2.1) — 1 (2.3%) 4 (10.0%) day 4 discharges* Day 7 Total 630 8.0 (4.2) 19 [13-31] 127 (20.2%) 214 (34.9%) patient population Clinically 124 3.9 (1.7) 11.5 [9-16] 9 (7.3%) 17 (13.9%) stable Clinically 78 3.4 (1.6) 11 [9-14] 1 (1.3%) 4 (5.3%) stable and low MR-proADM Actual 36 3.6 (2.6) — 2 (5.6%) 5 (13.9%) day 7 discharges* Day 10 Total 503 7.6 (4.0) 23.5 [17-34.25] 82 (16.3%) 159 (32.6%) patient population Clinically 85 3.5 (1.8) 15 [13-22] 9 (10.6%) 14 (17.3%) stable Clinically 57 3.2 (1.3) 14 [12.25-19] 1 (1.8%) 2 (3.8%) stable and low MR-proADM Actual 29 4.0 (2.6) — 5 (17.2%) 7 (24.1%) day 10 discharges* *excludes same or next day mortalities

(177) TABLE-US-00016 TABLE 14 Time dependent Cox regressions for single and cumulative additions of MR-proADM Univariate model Multivariate model LR Added Added p- LR Added Added p- χ.sup.2 DF LR χ.sup.2 DF value χ.sup.2 DF LR χ.sup.2 DF value Addition of single days to baseline values MR-proADM baseline 144.2 1 Reference 163.0 10 Reference +Day 1 169.8 2 25.6 1 <0.001 190.6 11 27.6 1 <0.001 +Day 4 161.9 2 17.7 1 <0.001 180.4 11 17.4 1 <0.001 +Day 7 175.7 2 31.5 1 <0.001 195.1 11 32.1 1 <0.001 +Day 10 179.8 2 35.6 1 <0.001 197.9 11 34.9 1 <0.001 Addition of consecutive days to baseline values MR-proADM baseline 144.2 1 Reference 163.0 10 Reference +Day 1 169.8 2 25.6 1 <0.001 190.6 11 27.6 1 <0.001 +Day 1 + Day 4 174.9 3  5.1 1 0.0243 195.4 12  4.8 1 0.0280 +Day 1 + Day 4 + 188.7 4 13.9 1 <0.001 210.4 13 15.0 1 <0.001 Day 7 +Day 1 + Day 4 + 195.2 5  6.5 1 0.0111 216.6 14  6.2 1 0.0134 Day 7 + Day 10 MR-proADM: mid-regional proadrenomedullin; DF: Degrees of Freedom

(178) TABLE-US-00017 TABLE 15 28 and 90 day mortality rates following PCT and MR-proADM kinetics Biomarker Kinetics 28 day mortality 90 day mortality Baseline Day 1 N % HR IQR [95% CI] N % HR IQR [95% CI] PCT decrease ≥20% 458 18.3% 447 28.2% MR-proADM Low Low 125 5.6% 3.6 [1.6-8.1]* 121 13.2% 2.7 [1.6-4.8]* severity Intermediate Intermediate 204 19.1% 5.3 [3.0-9.3]** 201 32.3% 3.8 [2.3-6.3]** level High High 27 66.7% 19.1 [8.0-45.9]*** 27 70.4% 10.4 [5.3-20.2]*** Increasing Low Intermediate 2 50.0% — 2 50.0% — Intermediate High 10 40.0% 2.5 [0.9-7.0]†† 10 50.0% 1.9 [0.8-4.8]†† Decreasing High Intermediate 30 36.7% 0.4 [0.2-0.9]‡ 29 44.8% 0.5 [0.2-0.9]‡ High Low — — — — — — Intermediate Low 60 8.3% 0.4 [0.2-1.0]‡‡ 57 12.3% 0.3 [0.2-0.7]‡‡ PCT decrease <20% 522 29.7% 508 42.5% MR-proADM Low Low 106 10.4% 3.1 [1.7-5.9]* 105 16.2% 3.2 [1.9-5.3]* severity Intermediate Intermediate 229 29.7% 2.0 [1.3-2.9]** 221 43.4% 1.9 [1.3-2.6]** level High High 77 49.4% 6.2 [3.2-12.2]*** 75 64.0% 5.9 [3.4-10.3]*** Increasing Low Intermediate 29 17.2% 1.8 [0.6-5.2]† 27 44.4% 3.2 [1.5-6.7]† Intermediate High 45 53.3% 2.3 [1.4-3.6]†† 45 68.9% 2.1 [1.4-3.2]†† Decreasing High Intermediate 11 54.5% — 11 72.7% — High Low 1 0.0% — 1 100.0% — Intermediate Low 24 12.5% 0.4 [0.1-1.2]‡‡ 23 13.0% 0.2 [0.1-0.8]‡‡ Hazard ratios for patients with: *continuously intermediate vs. low values; **continuously high vs. intermediate values ***continuously high vs. low values; †Increasing low to intermediate vs. continuously low values; ††Increasing intermediate to high vs. continuously intermediate values; ‡decreasing high to intermediate vs. continuously high values; ‡‡Decreasing intermediate to low vs. increasing intermediate to high values. Kaplan Meier plots illustrate either individual patient subgroups, or grouped increasing or decreasing subgroups.

(179) TABLE-US-00018 TABLE 16 Mortality rates following changes in PCT concentrations and MR-proADM severity levels 7 day mortality ICU mortality Baseline Day 1 N % HR IQR [95% CI] N % HR IQR [95% CI] PCT decrease ≥20% 461 6.1% 456 16.7% MR-proADM Low Low 126 2.4% 1.9 [0.5-6.9]* 126 4.8% 3.9 [1.6-9.6]* severity Intermediate Intermediate 205 4.4% 8.2 [3.4-21.2]** 202 16.3% 8.7 [3.7-20.7]** level High High 27 29.6% 15.2 [4.0-57.3]*** 27 63.0% 34.0 [11.0-105.5]*** Increasing Low Intermediate 3 0.0% — 2 0.0% — Intermediate High 10 20.0% 4.7 [1.0-21.6]†† 10 30.0% 2.2 [0.5-8.9]†† Decreasing High Intermediate 30 16.7% 0.5 [0.2-1.6]‡ 29 37.9% 0.4 [0.1-1.1]‡ Intermediate Low 60 1.7% 0.4 [0.0-3.0]‡‡ 59 10.2% 0.6 [0.1-1.5]‡‡ PCT decrease <20% 526 13.7% 517 30.2% MR-proADM Low Low 107 5.6% 2.0 [0.8-4.9]* 107 10.3% 3.4 [1.7-6.8]* severity Intermediate Intermediate 230 10.9% 2.6 [1.5-4.7]** 225 28.0% 3.0 [1.8-5.2]** level High High 77 26.0% 5.3 [2.1-13.2]*** 74 54.1% 10.3 [4.7-22.3]*** Increasing Low Intermediate 30 13.3% 2.5 [0.7-8.9]† 29 31.0% 3.9 [1.4-10.7]† Intermediate High 46 28.3% 3.0 [1.5-5.8]†† 45 57.8% 3.3 [1.7-6.4]†† Decreasing High Intermediate 11 36.6% 0.5 [0.2-1.6]‡ 11 54.5% 1.0 [0.3-3.7]‡ High Low 1 0.0% — 1 0.0% — Intermediate Low 24 0.0% ? 24 4.2% 0.1 [0.0-0.8]‡‡ Hospital mortality Baseline Day 1 N % HR IQR [95% CI] PCT decrease ≥20% 439 24.1% MR-proADM Low Low 123 7.3% 4.9 [2.3-10.3]* severity Intermediate Intermediate 194 27.8% 6.2 [2.5-14.9]** level High High 27 70.4% 30.1 [10.3-87.6]*** Increasing Low Intermediate 2 0.0% — Intermediate High 10 50.0% 2.6 [0.7-9.3]†† Decreasing High Intermediate 28 46.4% 0.4 [0.1-1.1]‡ Intermediate Low 55 10.9% 0.3 [0.1-0.8]‡‡ PCT decrease <20% 493 36.9% MR-proADM Low Low 102 13.7% 3.6 [1.9-6.8]* severity Intermediate Intermediate 216 36.6% 2.4 [1.4-4.2]** level High High 72 58.3% 8.8 [4.2-18.3]*** Increasing Low Intermediate 27 37.0% 3.7 [1.4-9.7]† Intermediate High 43 65.1% 3.2 [1.6-6.4]†† Decreasing High Intermediate 10 80.0% — High Low 1 0.0% — Intermediate Low 22 4.5% 0.1 [0.0-0.6]‡‡ Hazard ratios for patients with: *continuously intermediate vs. low values; **continuously high vs. intermediate values ***continuously high vs. low values; †increasing low to intermediate vs. continuously low values; ††increasing intermediate to high vs. continuously intermediate values; ‡decreasing high to intermediate vs. continuously high values; ‡‡decreasing intermediate to low vs. continuously intermediate values

(180) TABLE-US-00019 TABLE 17 28 and 90 day mortality rates following changes in PCT concentrations and MR-proADM severity levels Biomarker Kinetics 28 day mortality 90 day mortality Baseline Day 4 N % HR IQR [95% CI] N % HR IQR [95% CI] PCT decrease ≥50% 557 17.1% 542 29.3% MR-proADM Low Low 111 1.8% 11.2 [2.7-46.4]* 107 7.5% 5.3 [2.5-10.9]* severity Intermediate Intermediate 209 18.7% 3.8 [2.3-6.5]** 206 33.5% 3.3 [2.1-5.1]** level High High 39 53.8% 43.1 [10.1-184.0]*** 39 71.8% 17.4 [7.9-38.2]*** Increasing Low Intermediate 24 25.0% 15.6 [3.1-77.2]† 24 41.7% 7.1 [2.8-17.9]† Intermediate High 23 43.5% 2.6 [1.3-5.3]†† 23 65.2% 2.6 [1.5-4.5]†† Decreasing High Intermediate 42 21.4% 0.3 [0.1-0.7]‡ 41 36.6% 0.3 [0.2-0.6]‡ High Low 3 0.0% — 2 50.0% — Intermediate Low 105 7.6% 0.4 [0.2-0.8]‡‡ 100 13.0% 0.3 [0.2-0.6]‡‡ PCT decrease <50% 210 29.5% 203 45.5% MR-proADM Low Low 56 7.1% 6.3 [2.2-18.1]* 55 12.7% 6.2 [2.8-13.9]* severity Intermediate Intermediate 70 38.6% 1.5 [0.8-3.0]** 68 57.4% 1.3 [0.7-2.3]** level High High 23 52.2% 9.5 [3.1-29.5]*** 22 63.6% 7.9 [3.2-19.5]*** Increasing Low Intermediate 17 17.6% 2.8 [0.6-12.5]† 15 53.3% 5.5 [2.0-15.2]† Low High 4 0.0% — 4 25.0% — Intermediate High 30 46.7% 1.4 [0.7-2.6]†† 30 66.7% 1.3 [0.8-2.2]†† Decreasing High Intermediate — — — — — — High Low — — — — — — Intermediate Low 10 20.0% — 9 33.4% — Hazard ratios for patients with: *continuously intermediate vs. low values; **continuously high vs. intermediate values ***continuously high vs. low values; †Increasing low to intermediate vs. continuously low values; ††Increasing intermediate to high vs. continuously intermediate values; ‡decreasing high to intermediate vs. continuously high values; ‡‡Decreasing intermediate to low vs. continuously intermediate values

(181) TABLE-US-00020 TABLE 18 ICU and hospital mortality rates following changes in PCT concentrations and MR-proADM severity levels ICU mortality Hospital mortality Baseline Day 4 N % HR IQR [95% CI] N % HR IQR [95% CI] PCT decrease ≥50% 555 16.8% 532 24.1% MR-proADM Low Low 114 2.6% 6.9 [2.1-23.1]* 109 2.8% 13.3 [4.1-43.8]* severity Intermediate Intermediate 208 15.9% 8.1 [3.8-17.2]** 197 27.4% 5.1 [2.4-10.7]** level High High 38 60.5% 56.2 [15.0-210.2]*** 38 65.8% 67.9 [18.0-256.6]*** Low Intermediate 24 29.2% 15.1 [3.6-64.1]† 24 33.3% 17.7 [4.2-73.6]† Intermediate High 23 43.5% 4.1 [1.7-10.0]†† 23 56.5% 3.4 [1.4-8.3]†† High Intermediate 41 22.0% 0.2 [0.1-0.5]‡ 39 33.3% 1.3 [0.6-2.7]‡ High Low 3 0.0% — 2 50.0% — Intermediate Low 103 8.7% 0.5 [0.2-1.0]‡‡ 99 11.1% 0.3 [0.2-0.7]‡‡ PCT decrease <50% 204 28.9% 194 30.4% MR-proADM Low Low 56 1.8% 28.1 [3.7-216.3]* 54 7.4% 10.1 [3.3-31.2]* severity Intermediate Intermediate 68 33.8% 1.8 [0.7-4.8]** 65 44.6% 1.9 [0.7-5.2]** level High High 21 47.6% 50.0 [5.8-431.5]*** 20 60.0% 18.8 [4.8-72.7]*** Low Intermediate 16 43.7% 42.8 [4.7-390.2]† 14 57.1% 16.7 [3.8-72.4]† Low High 4 0.0% — 4 25.0% — Intermediate High 29 58.6% 2.8 [1.1-6.8]†† 28 64.3% 2.2 [0.9-5.6]†† High Intermediate — — — — — High Low — — — — — Intermediate Low 10 10.0% — 9 33.3% — Hazard ratios for patients with: *continuously intermediate vs. low values; **continuously high vs. intermediate values ***continuously high vs. low values; †Increasing low to intermediate vs. continuously low values; ††Increasing intermediate to high vs. continuously intermediate values; ‡decreasing high to intermediate vs. continuously high values; ‡‡Decreasing intermediate to low vs. continuously intermediate values

(182) TABLE-US-00021 TABLE 19 Influence of infectious origin on 28 day mortality prediction Univariate Multivariate Patients Mortality LR C- HR IQR p- LR C- HR IQR p- (N) (N) AUROC χ.sup.2 index [95% CI] value χ.sup.2 index [95% CI] value Pneumological MR- 313 83 0.72 37.9 0.69 2.7 [2.0-3.7] <0.0001 45.1 0.71 2.5 [1.7-3.6] <0.0001 proADM PCT 313 83 0.59 6.4 0.58 1.6 [1.1-2.2] 0.0112 26.0 0.66 1.5 [1.1-2.2] 0.0038 CRP 267 65 0.46 0.8 0.53 0.9 [0.7-1.1] 0.3754 14.7 0.63 0.9 [0.7-1.1] 0.1422 Lactate 322 86 0.61 12.6 0.61 1.6 [1.2-2.1] 0.0004 30.1 0.67 1.5 [1.1-2.0] 0.0008 SOFA 315 83 0.63 12.4 0.62 1.7 [1.3-2.3] 0.0004 29.6 0.68 1.6 [1.1-2.2] 0.0010 SAPS II 324 86 0.63 13.2 0.62 1.6 [1.3-2.1] 0.0003 28.8 0.67 1.5 [1.1-1.9] 0.0014 APACHE II 324 86 0.63 19.5 0.64 1.9 [1.4-2.5] <0.0001 33.4 0.68 1.7 [1.3-2.3] 0.0002 Intraabdominal MR- 238 58 0.78 47.4 0.75 4.5 [2.9-7.1] <0.0001 55.7 0.76 4.8 [2.9-8.0] <0.0001 proADM PCT 238 58 0.52 0.4 0.52 1.1 [0.8-1.7] 0.5249 15.0 0.64 1.2 [0.8-1.9] 0.1312 CRP 233 59 0.48 0.1 0.53 1.0 [0.8-1.3] 0.7807 12.0 0.62 1.1 [0.8-1.4] 0.2864 Lactate 249 62 0.67 18.0 0.66 2.2 [1.5-3.0] <0.0001 28.2 0.70 2.1 [1.5-3.0] 0.0017 SOFA 248 62 0.66 8.9 0.63 1.5 [1.2-2.0] 0.0029 18.3 0.64 1.5 [1.1-2.0] 0.0494 SAPS II 252 62 0.68 17.9 0.66 1.9 [1.4-2.6] <0.0001 24.3 0.67 1.9 [1.3-2.6] 0.0069 APACHE II 252 62 0.68 14.6 0.65 1.8 [1.3-2.3] 0.0001 20.6 0.66 1.6 [1.2-2.2] 0.0241 MR-proADM AUROC values are significantly greater than all other parameters apart from APACHE II in pneumological origins of infection.

(183) TABLE-US-00022 TABLE 20 Influence of microbial species on 28 day mortality prediction Univariate Multivariate Patients Mortality LR C- HR IQR p- LR C- HR IQR p- (N) (N) AUROC χ.sup.2 index [95% CI] value χ.sup.2 index [95% CI] value Gram MR- 141 33 0.82 37.2 0.81 5.0 [2.9-8.6] <0.0001 50.0 0.84 5.0 [2.7-9.2] <0.0001 positive proADM PCT 142 33 0.64 7.9 0.64 2.4 [1.3-4.4] 0.0050 30.3 0.76 3.0 [1.5-5.7] 0.0008 CRP 131 31 0.54 0.2 0.51 0.9 [0.7-1.3] 0.6561 19.8 0.71 1.0 [0.7-1.4] 0.0309 Lactate 143 33 0.75 28.9 0.74 4.6 [2.6-8.1] <0.0001 44.9 0.83 5.0 [2.6-9.7] <0.0001 SOFA 143 32 0.66 8.8 0.65 1.9 [1.3-2.8] 0.0031 31.8 0.76 2.7 [1.6-4.6] 0.0004 SAPS II 146 33 0.72 16.8 0.71 2.9 [1.7-4.7] <0.0001 28.4 0.76 2.7 [1.5-4.9] 0.0016 APACHE II 146 33 0.73 17.3 0.71 2.4 [1.6-3.5] <0.0001 33.1 0.77 2.8 [1.7-4.7] 0.0003 Gram MR- 124 35 0.69 12.1 0.68 2.3 [1.4-3.8] 0.0005 26.0 0.75 2.2 [1.2-3.8] 0.0037 negative proADM PCT 124 35 0.54 0.6 0.54 1.2 [0.7-2.1] 0.4580 17.8 0.67 1.2 [0.7-2.3] 0.0580 CRP 110 30 0.57 0.4 0.56 1.2 [0.7-1.8] 0.5255 17.1 0.68 1.4 [0.9-2.2] 0.0727 Lactate 131 37 0.65 10.0 0.64 1.9 [1.3-2.8] 0.0016 23.4 0.71 1.7 [1.1-2.7] 0.0093 SOFA 129 37 0.65 9.0 0.64 1.8 [1.2-2.7] 0.0027 25.5 0.72 1.9 [1.2-2.9] 0.0045 SAPS II 132 37 0.67 9.9 0.65 1.9 [1.3-2.8] 0.0017 25.1 0.71 1.9 [1.2-3.0] 0.0051 APACHE II 132 37 0.69 7.9 0.66 1.7 [1.2-2.4] 0.0049 22.3 0.70 1.7 [1.1-2.6] 0.0139 Fungal MR- 50 14 0.74 7.9 0.69 2.5 [1.3-4.9] 0.0051 14.4 0.78 3.4 [1.1-10.7] 0.1548 proADM PCT 50 14 0.46 0.3 0.52 1.3 [0.5-3.0] 0.6104 8.5 0.72 1.1 [0.4-3.0] 0.5792 CRP 43 12 0.65 0.6 0.65 0.8 [0.5-1.3] 0.4404 14.7 0.81 0.5 [0.2-1.2] 0.1427 Lactate 51 14 0.60 2.7 0.59 2.0 [0.9-4.7] 0.1032 13.2 0.74 3.3 [1.0-1.0] 0.2128 SOFA 49 12 0.54 0.8 0.54 1.4 [0.7-2.8] 0.3668 7.1 0.73 1.1 [0.5-2.8] 0.7164 SAPS II 51 14 0.60 2.2 0.60 1.5 [0.9-2.6] 0.1412 10.0 0.75 1.4 [0.7-2.8] 0.4427 APACHE II 51 14 0.62 1.6 0.62 1.6 [0.8-3.3] 0.2053 10.1 0.76 1.7 [0.7-4.4] 0.4321

(184) TABLE-US-00023 TABLE 21 Influence of mode of ICU entry on 28 day mortality prediction Univariate Multivariate Patients Mortality LR C- HR IQR p- LR C- HR IQR p- (N) (N) AUROC χ.sup.2 index [95% CI] value χ.sup.2 index [95% CI] value Operative MR- 466 113 0.77 87.4 0.75 4.1 [3.0-5.6] <0.0001 106.4 0.77 3.8 [2.8-5.3] <0.0001 proADM PCT 466 113 0.60 11.8 0.59 1.6 [1.2-2.2] 0.0006 53.1 0.70 1.7 [1.3-2.4] <0.0001 CRP 421 106 0.48 1.2 0.52 1.1 [0.9-1.4] 0.2696 39.7 0.68 1.2 [0.9-1.4] <0.0001 Lactate 483 120 0.68 46.4 0.67 2.4 [1.9-3.1] <0.0001 73.7 0.71 2.3 [1.8-3.0] <0.0001 SOFA 482 118 0.68 34.9 0.65 2.0 [1.6-2.4] <0.0001 65.7 0.71 2.0 [1.6-2.5] <0.0001 SAPS II 489 120 0.71 50.5 0.68 2.2 [1.8-2.7] <0.0001 65.9 0.70 2.0 [1.6-2.5] <0.0001 APACHE II 489 120 0.71 47.8 0.68 2.3 [1.8-2.8] <0.0001 64.8 0.71 2.0 [1.6-2.5] <0.0001 Non- MR- 448 132 0.70 48.6 0.68 2.6 [2.0-3.4] <0.0001 56.5 0.69 2.4 [1.8-3.3] <0.0001 operative proADM PCT 449 132 0.52 0.8 0.52 1.1 [0.9-1.5] 0.3644 24.4 0.62 1.1 [0.8-1.4] 0.0066 CRP 424 121 0.50 0.2 0.49 1.0 [0.8-1.2] 0.6280 23.6 0.62 1.0 [0.8-1.2] 0.0088 Lactate 462 137 0.62 24.5 0.62 1.9 [1.5-2.4] <0.0001 43.7 0.67 1.8 [1.4-2.3] <0.0001 SOFA 450 132 0.62 15.9 0.61 1.7 [1.3-2.1] 0.0001 39.5 0.66 1.7 [1.3-2.2] <0.0001 SAPS II 466 137 0.65 25.4 0.64 1.6 [1.3-1.9] <0.0001 43.4 0.66 1.5 [1.3-1.8] <0.0001 APACHE II 466 137 0.64 23.9 0.63 1.7 [1.4-2.1] <0.0001 40.2 0.66 1.6 [1.3-2.0] <0.0001 Elective MR- 116 30 0.71 12.1 0.69 2.8 [1.6-5.2] 0.0005 17.3 0.72 2.3 [1.2-4.5] 0.0440 proADM PCT 116 30 0.59 3.3 0.59 1.6 [1.0-2.6] 0.0675 15.1 0.70 1.7 [1.0-2.8] 0.0873 CRP 91 24 0.51 0.0 0.50 1.0 [0.7-1.4] 0.8650 11.5 0.70 0.8 [0.5-1.3] 0.3219 Lactate 121 32 0.63 9.5 0.63 2.2 [1.4-3.6] 0.0020 21.0 0.72 2.2 [1.3-3.6] 0.0211 SOFA 119 32 0.58 0.9 0.56 1.2 [0.9-1.6] 0.3476 13.7 0.69 1.0 [0.7-1.3] 0.1860 SAPS II 121 32 0.60 1.4 0.59 1.3 [0.9-1.9] 0.2333 13.1 0.68 0.9 [0.6-1.5] 0.2177 APACHE II 121 32 0.57 1.1 0.57 1.3 [0.8-1.9] 0.2945 13.1 0.69 0.9 [0.6-1.5] 0.2164

(185) TABLE-US-00024 TABLE 22 Baseline biomarker and clinical score correlation with SOFA at baseline and day 1 Baseline SOFA Day 1 SOFA Patients Correlation Patients Correlation (N) [95% CI] p-value (N) [95% CI] p-value MR-proADM 1007 0.46 [0.41-0.51] <0.0001 MR-proADM* 969 0.47 [0.41-0.51] <0.0001 969 0.57 [0.52-0.61] <0.0001 PCT 1007 0.23 [0.17-0.29] <0.0001 969 0.22 [0.16-0.28] <0.0001 CRP 918 0.06 [0.00-0.13] 0.0059 885 0.04 [0.00-0.12] 0.2709 Lactate 1044 0.33 [0.27-0.38] <0.0001 1005 0.40 [0.35-0.45] <0.0001 SAPS II 1051 0.60 [0.56-0.64] <0.0001 1011 0.50 [0.45-0.54] <0.0001 APACHE II 1051 0.62 [0.58-0.65] <0.0001 1011 0.53 [0.48-0.57] <0.0001 *using the same patients on baseline as on day 1

(186) TABLE-US-00025 TABLE 23 Baseline MR-proADM correlations with SOFA subscores on baseline and day 1 Baseline SOFA Day 1 SOFA SOFA Patients Correlation Patients Correlation subscore (N) [95% CI] p-value (N) [95% CI] p-value Circulation 1022 0.18 [0.12-0.23] <0.0001 995 0.23 [0.17-0.29] <0.0001 Pulmonary 1025 0.12 [0.06-0.18] <0.0001 994 0.15 [0.09-0.21] <0.0001 Coagulation 1028 0.30 [0.25-0.36] <0.0001 1002 0.40 [0.35-0.45] <0.0001 Renal 1030 0.50 [0.45-0.54] <0.0001 1001 0.62 [0.58-0.66] <0.0001 Liver 1014 0.20 [0.14-0.26] <0.0001 993 0.36 [0.30-0.40] <0.0001 CNS 1030  0.03 [−0.03-0.09] 0.3856 1003 0.08 [0.02-0.14] 0.0089

(187) TABLE-US-00026 TABLE 24 Biomarker correlations with SOFA scores throughout ICU treatment MR-proADM PCT CRP Lactate Day 1 Patients (N) 960 960 894 1008 Correlation 0.51 0.24 −0.04 0.48 [95% CI] [0.46-0.55] [0.18-0.30] [−0.10-0.03] [0.43-0.53] p-value <0.0001 <0.0001 <0.0001 <0.0001 Day 4 Patients (N) 729 729 667 754 Correlation 0.58 0.13 0.14 0.36 [95% CI] [0.53-0.63] [0.06-0.20] [0.06-0.21] [0.29-0.42] p-value <0.0001 0.0003 0.0004 <0.0001 Day 7 Patients (N) 580 581 547 612 Correlation 0.58 0.05 0.15 0.43 [95% CI] [0.53-0.64] [−0.03-0.13] [0.07-0.23] [0.37-0.50] p-value <0.0001 0.2368 0.0004 <0.0001 Day 10 Patients (N) 473 473 429 483 Correlation 0.65 0.28 0.13 0.34 [95% CI] [0.59-0.70] [0.20-0.37] [0.03-0.22] [0.26-0.42] p-value <0.0001 <0.0001 0.0076 <0.0001

(188) TABLE-US-00027 TABLE 25 Mortalities based on MR-proADM severities and increasing or decreasing PCT concentrations - Baseline to day 1 28 day 90 day 7 day ICU Hospital mortality mortality mortality mortality mortality Baseline Day 1 N % N % N % N % N % Decreasing PCT 657 19.0% 636 28.9% 657 6.4% 650 11.6% 623 25.2 MR-proADM Low Low 161 5.0% 157 14.0% 163 2.5% 162 5.6% 157 8.3% severity Intermediate Intermediate 314 19.1% 308 31.8% 316 4.7% 310 17.1% 299 27.8% level High High 51 58.8% 50 64.0% 51 23.5% 51 54.9% 49 63.3% Increasing Low Intermediate 10 20.0% 10 30.0% 11 0.0% 11 18.2% 10 20.0% Intermediate High 17 35.3% 17 41.2% 17 17.6% 17 29.4% 17 41.2% Decreasing High Intermediate 35 40.0% 34 47.1% 35 20.0% 34 41.2% 32 50.0% High Low — — — — — — — — — — Intermediate Low 63 7.9% 60 10.0% 63 1.6% 63 7.9% 58 8.6% Increasing PCT 329 35.0% 319 46.6% 331 17.5% 324 35.8% 31 42.3% MR-proADM Low Low 66 13.6% 65 15.4% 66 7.6% 66 10.6% 64 14.1% severity Intermediate Intermediate 131 36.6% 126 51.6% 131 14.5% 128 35.2% 122 42.6% level High High 53 49.1% 52 67.3% 53 20.2% 50 58.0% 50 60.0% Increasing Low Intermediate 25 20.0% 23 47.8% 26 15.4% 25 32.0% 23 39.1% Low High — — — — — — — — — — Intermediate High 38 57.9% 38 76.3% 39 30.8% 39 61.5% 36 72.2% Decreasing High Intermediate 6 50.0% 6 66.7% 6 33.3% 6 50.0% 6 83.3% High Low 1 0.0% 1 100.0% 1 0.0% 1 0.0% 1 0.0% Intermediate Low 9 22.2% 8 25.0% 9 0.0% 9 0.0% 8 0.0%

(189) TABLE-US-00028 TABLE 26 PCT kinetics from baseline to day 1 - development of new infections over days 1, 2, 3, 4. New infections over Days 1, 2, 3, 4 Baseline Day 1 N % Decreasing PCT 652 9.7% MR-proADM Low Low 161 6.8% severity Intermediate Intermediate 315 11.7% level High High 51 11.8% Increasing Low Intermediate 10 0.0% Intermediate High 17 5.9% Decreasing High Intermediate 34 8.8% High Low — — Intermediate Low 63 7.9% Increasing PCT 329 18.5% MR-proADM Low Low 66 9.1% severity Intermediate Intermediate 131 18.3% level High High 53 22.6% Increasing Low Intermediate 25 24.0% Low High — — Intermediate High 38 18.4% Decreasing High Intermediate 6 50.0% High Low 1 0.0% Intermediate Low 9 33.3%

(190) TABLE-US-00029 TABLE 27 PCT kinetics from baseline to day 4 - development of new infections over days 4, 5, 6, 7. New infections over Days 4, 5, 6, 7 Baseline Day 4 N % Decreasing PCT 681 14.5% MR-proADM Low Low 144 8.3% severity Intermediate Intermediate 256 17.6% level High High 57 28.1% Increasing Low Intermediate 31 22.6% Intermediate High 36 13.9% Decreasing High Intermediate 42 11.9% High Low 3 0.0% Intermediate Low 111 8.1%

(191) TABLE-US-00030 TABLE 28 PCT kinetics from baseline to day 1 - requirement for focus cleaning over days 1, 2, 3, 4. Focus cleaning events over days days 1, 2, 3, 4 Baseline Day 1 N % Increasing PCT 329 21.0% MR-proADM Low Low 57 10.5% severity Intermediate Intermediate 113 20.4% level High High 58 19.0% Increasing Low Intermediate 31 32.3% Low High 3 33.3% Intermediate High 59 28.8% Decreasing High Intermediate 1 0.0% High Low 1 100.0% Intermediate Low 6 0.0%

(192) TABLE-US-00031 TABLE 29 PCT kinetics from baseline to day 4 - requirement for focus cleaning over days 4, 5, 6, 7. Focus cleaning events over days days 4, 5, 6, 7 Baseline Day 4 N % Decreasing PCT 681 22.0% MR-proADM Low Low 144 16.7% severity Intermediate Intermediate 256 24.2% level High High 57 31.6% Increasing Low Intermediate 31 32.3% Intermediate High 36 50.0% Decreasing High Intermediate 42 16.7% High Low 3 0.0% Intermediate Low 111 9.9%

(193) TABLE-US-00032 TABLE 30 PCT kinetics from baseline to day 1 - requirement of emergency surgery over days 1, 2, 3, 4. Emergency surgery requirement over days 1, 2, 3, 4 Baseline Day 1 N % Increasing PCT 329 23.7% MR-proADM Low Low 66 18.2% severity Intermediate Intermediate 131 26.0% level High High 53 28.3% Increasing Low Intermediate 25 16.0% Low High — — Intermediate High 38 31.6% Decreasing High Intermediate 6 0.0% High Low 1 100.0% Intermediate Low 9 0.0%

(194) TABLE-US-00033 TABLE 31 Increasing PCT from baseline to day 1 - antibiotic changes on day 4 Increasing PCT 259 21.6% MR-proADM Low Low 55 5.5% severity level Intermediate Intermediate 106 27.4% High High 39 25.6% Increasing Low Intermediate 20 25.0% Intermediate High 26 26.9% Decreasing High Intermediate 5 20.0% High Low 1 100.0% Intermediate Low 7 0.0%

(195) TABLE-US-00034 TABLE 32 Increasing PCT from baseline to day 4 - antibiotic changes on day 4 Increasing PCT 85 23.5% MR-proADM Low Low 23 8.7% severity level Intermediate Intermediate 22 36.4% High High 5 20.0% Increasing Low Intermediate 10 20.0% Intermediate High 17 41.2% Low High 4 0.0% Decreasing High Intermediate — — High Low — — Intermediate Low 4 0.0%

(196) TABLE-US-00035 TABLE 33 Biomarker levels based on platelet count Median Median Platelet Median Median Platelet Median Median count proADM PCT count proADM PCT Platelet level Patients Mortality (baseline; level level Platelet (day 1; level level (10.sup.3/μl) (N) (N, %) 10.sup.3/μl)) (baseline) (baseline) transfusion 10.sup.3/μl)) (day 1) (day 1) <20 3 1 (33.3%) 12 15.6 171.7 1 (33.3%) 15 9.8 58.1 .sup. 20 to <150 233 90 (38.6%) 109 6.6 9.0 24 (10.3%) 78 5.5 7.4 150 to 399 658 165 (25.1%) 249 4.5 7.0 11 (1.7%) 191 4.3 5.8 >399 177 32 (18.1%) 494 4.9 5.7 1 (0.6%) 342 3.7 4.3

(197) TABLE-US-00036 TABLE 34 Platelet count based on proADM levels Median Median % Platelet Platelet Platelet Median count count decrease proADM Day 1 MR-proADM Patients Mortality (baseline; Platelet (day 1; from level Thrombocytopenia (nmol/L) (N) (N, %) 10.sup.3/μl)) transfusion 10.sup.3/μl)) baseline (day 1) development ≤2.75 271 28 (10.3%) 251.5 3 (1.1%) 206 18% 1.7  73 (26.9%) >2.75 and ≤10.9 594 157 (26.4%)  246 14 (2.4%) 177 28% 5.0 249 (41.9%) >10.9 165 90 (54.5%) 178.5 19 (11.5%) 103.5 42% 11.8 102 (61.8%)

(198) TABLE-US-00037 TABLE 35 Development of Thrombocytopenia and proADM kinetics at baseline and day 1. Median Median % Platelet Platelet Platelet Median count count decrease proADM Day 1 MR-proADM Patients Mortality (baseline; Platelet (day 1; from level Thrombocytopenia (nmol/L) (N) (N, %) 10.sup.3/μl) transfusion 10.sup.3/μl) baseline (day 1) development ≤2.75 232 21 (9.1%) 274 1 (0.4%) 227 17.2% 1.75 34 (14.7%) >2.75 and ≤10.9 464 112 (24.1%) 281 5 (1.1%) 215 23.5% 4.9 119 (25.6%)  >10.9 104  53 (51.0%) 259 5 (4.8%) 162 37.5% 11.6 41 (39.4%)

REFERENCES

(199) 1. Martin G S, Mannino D M, Eaton S, Moss M. The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med. Apr. 17 2003; 348(16):1546-1554. 2. Kaukonen K M, Bailey M, Suzuki S, Pilcher D, Bellomo R. Mortality related to severe sepsis and septic shock among critically ill patients in Australia and New Zealand, 2000-2012. JAMA. Apr. 2, 2014; 311(13):1308-1316. 3. Vincent J L, Sakr Y, Sprung C L, et al. Sepsis in European intensive care units: results of the SOAP study. Crit Care Med. February 2006; 34(2):344-353. 4. Singer M, Deutschman C S, Seymour C W, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. Feb. 23, 2016; 315(8):801-810. 5. Kopterides P, Siempos, II, Tsangaris I, Tsantes A, Armaganidis A. Procalcitonin-guided algorithms of antibiotic therapy in the intensive care unit: a systematic review and meta-analysis of randomized controlled trials. Crit Care Med. November 2010; 38(11):2229-2241. 6. de Jong E, van Oers J A, Beishuizen A, et al. Efficacy and safety of procalcitonin guidance in reducing the duration of antibiotic treatment in critically ill patients: a randomised, controlled, open-label trial. Lancet Infect Dis. July 2016; 16(7):819-827. 7. Harbarth S, Holeckova K, Froidevaux C, et al. Diagnostic value of procalcitonin, interleukin-6, and interleukin-8 in critically ill patients admitted with suspected sepsis. Am J Respir Crit Care Med. Aug. 1, 2001; 164(3):396-402. 8. Andriolo B N, Andriolo R B, Salomao R, Atallah A N. Effectiveness and safety of procalcitonin evaluation for reducing mortality in adults with sepsis, severe sepsis or septic shock. Cochrane Database Syst Rev. Jan. 18, 2017; 1:CD010959. 9. Temmesfeld-Wollbruck B, Brell B, David I, et al. Adrenomedullin reduces vascular hyperpermeability and improves survival in rat septic shock. Intensive Care Med. April 2007; 33(4):703-710. 10. Muller-Redetzky H C, Will D, Hellwig K, et al. Mechanical ventilation drives pneumococcal pneumonia into lung injury and sepsis in mice: protection by adrenomedullin. Crit Care. 2014; 18(2):R73. 11. Vallet B. Endothelial cell dysfunction and abnormal tissue perfusion. Crit Care Med. May 2002; 30(5 Suppl):S229-234. 12. Gonzalez-Rey E, Chorny A, Varela N, Robledo G, Delgado M. Urocortin and adrenomedullin prevent lethal endotoxemia by down-regulating the inflammatory response. Am J Pathol. June 2006; 168(6):1921-1930. 13. Carrizo G J, Wu R, Cui X, Dwivedi A J, Simms H H, Wang P. Adrenomedullin and adrenomedullin-binding protein-1 downregulate inflammatory cytokines and attenuate tissue injury after gut ischemia-reperfusion. Surgery. February 2007; 141(2):245-253. 14. Brell B, Hippenstiel S, David I, et al. Adrenomedullin treatment abolishes ileal mucosal hypoperfusion induced by Staphylococcus aureus alpha-toxin—an intravital microscopic study on an isolated rat ileum. Crit Care Med. December 2005; 33(12):2810-2016. 15. Brell B, Temmesfeld-Wollbruck B, Altzschner I, et al. Adrenomedullin reduces Staphylococcus aureus alpha-toxin-induced rat ileum microcirculatory damage. Crit Care Med. April 2005; 33(4):819-826. 16. Vigue B, Leblanc P E, Moati F, et al. Mid-regional pro-adrenomedullin (MR-proADM), a marker of positive fluid balance in critically ill patients: results of the ENVOL study. Crit Care. Nov. 9, 2016; 20(1):363. 17. Andaluz-Ojeda D, Cicuendez R, Calvo D, et al. Sustained value of proadrenomedullin as mortality predictor in severe sepsis. J Infect. July 2015; 71(1):136-139. 18. Hartmann O, Schuetz P, Albrich W C, Anker S D, Mueller B, Schmidt T. Time-dependent Cox regression: serial measurement of the cardiovascular biomarker proadrenomedullin improves survival prediction in patients with lower respiratory tract infection. Int J Cardiol. Nov. 29, 2012; 161(3):166-173. 19. Albrich W C, Dusemund F, Ruegger K, et al. Enhancement of CURB65 score with proadrenomedullin (CURB65-A) for outcome prediction in lower respiratory tract infections: derivation of a clinical algorithm. BMC Infect Dis. 2011; 11:112. 20. Albrich W C, Ruegger K, Dusemund F, et al. Optimised patient transfer using an innovative multidisciplinary assessment in Kanton Aargau (OPTIMA I): an observational survey in lower respiratory tract infections. Swiss Med Wkly. 2011; 141:w13237. 21. Albrich W C, Ruegger K, Dusemund F, et al. Biomarker-enhanced triage in respiratory infections: a proof-of-concept feasibility trial. Eur Respir J. October 2013; 42(4):1064-1075. 22. Riera J, Senna A, Cubero M, Roman A, Rello J, Study Group Investigators TV. Primary Graft Dysfunction and Mortality Following Lung Transplantation: A Role for Proadrenomedullin Plasma Levels. Am J Transplant. Oct. 13, 2015. 23. Schoe A, Schippers E F, Struck J, et al. Postoperative pro-adrenomedullin levels predict mortality in thoracic surgery patients: comparison with Acute Physiology and Chronic Health Evaluation IV Score*. Crit Care Med. February 2015; 43(2):373-381. 24. Tyagi A, Sethi A K, Girotra G, Mohta M. The microcirculation in sepsis. Indian J Anaesth. June 2009; 53(3):281-293. 25. Hernandez G, Bruhn A, Ince C. Microcirculation in sepsis: new perspectives. Curr Vasc Pharmacol. Mar. 1, 2013; 11(2):161-169. 26. Bloos F, Trips E, Nierhaus A, et al. Effect of Sodium Selenite Administration and Procalcitonin-Guided Therapy on Mortality in Patients With Severe Sepsis or Septic Shock: A Randomized Clinical Trial. JAMA Intern Med. Sep. 1, 2016; 176(9):1266-1276. 27. Bloos F, Ruddel H, Thomas-Ruddel D, et al. Effect of a multifaceted educational intervention for anti-infectious measures on sepsis mortality: a cluster randomized trial. Intensive Care Med. May 2, 2017. 28. Enguix-Armada A, Escobar-Conesa R, La Torre A G, De La Torre-Prados M V. Usefulness of several biomarkers in the management of septic patients: C-reactive protein, procalcitonin, presepsin and mid-regional pro-adrenomedullin. Clin Chem Lab Med. Jun. 17, 2015. 29. Christ-Crain M, Morgenthaler N G, Struck J, Harbarth S, Bergmann A, Muller B. Mid-regional pro-adrenomedullin as a prognostic marker in sepsis: an observational study. Crit Care. 2005; 9(6):R816-824. 30. Suberviola B, Castellanos-Ortega A, Ruiz Ruiz A, Lopez-Hoyos M, Santibanez M. Hospital mortality prognostication in sepsis using the new biomarkers suPAR and proADM in a single determination on ICU admission.

(200) Intensive Care Med. November 2013; 39(11):1945-1952. 31. Gillmann H J, Meinders A, Larmann J, et al. Adrenomedullin Is Associated With Surgical Trauma and Impaired Renal Function in Vascular Surgery Patients. J Intensive Care Med. Jan. 1, 2017:885066616689554. 32. Garrouste-Orgeas M, Montuclard L, Timsit J F, Misset B, Christias M, Carlet J. Triaging patients to the ICU: a pilot study of factors influencing admission decisions and patient outcomes. Intensive Care Med. May 2003; 29(5):774-781. 33. Garrouste-Orgeas M, Montuclard L, Timsit J F, et al. Predictors of intensive care unit refusal in French intensive care units: a multiple-center study. Crit Care Med. April 2005; 33(4):750-755. 34. Mery E, Kahn J M. Does space make waste? The influence of ICU bed capacity on admission decisions. Crit Care. May 8, 2013; 17(3):315. 35. Orsini J, Blaak C, Yeh A, et al. Triage of Patients Consulted for ICU Admission During Times of ICU-Bed Shortage. J Clin Med Res. December 2014; 6(6):463-468. 36. Kip M M, Kusters R, MJ IJ, Steuten L M. A PCT algorithm for discontinuation of antibiotic therapy is a cost-effective way to reduce antibiotic exposure in adult intensive care patients with sepsis. J Med Econ. 2015; 18(11):944-953. 37. Wilke M H, Grube R F, Bodmann K F. The use of a standardized PCT-algorithm reduces costs in intensive care in septic patients—a DRG-based simulation model. Eur J Med Res. Dec. 2, 2011; 16(12):543-548. 38. Baughman R P, Lower E E, Flessa H C, Tollerud D J. Thrombocytopenia in the intensive care unit. Chest. 1993; 104(4):1243-7.7. 39. Drews R E, Weinberger S E. Thrombocytopenic disorders in critically ill patients. Am J Respir Crit Care Med. 2000; 162(2 Pt 1):347-51. 40. Vanderschueren S, De Weerdt A, Malbrain M, Vankersschaever D, Frans E, Wilmer A, et al. Thrombocytopenia and prognosis in intensive care. Crit Care Med. 2000; 28(6):1871-6. 9. 41. Strauss R, Wehler M, Mehler K, Kreutzer D, Koebnick C, Hahn E G. Thrombocytopenia in patients in the medical intensive care unit: bleeding prevalence, transfusion requirements, and outcome. Crit Care Med. 2002; 30(8):1765-71. 10. 42. Smith-Erichsen N. Serial determinations of platelets, leucocytes and coagulation parameters in surgical septicemia. Scand J Clin Lab Invest Suppl. 1985; 178:7-14. 11. 43. Akca S, Haji-Michael P, de Mendonca A, Suter P, Levi M, Vincent J L. Time course of platelet counts in critically ill patients. Crit Care Med. 2002; 30(4):753-6. 12. 44. Crowther M A, Cook D J, Meade M O, Grifth L E, Guyatt G H, Arnold D M, et al. Thrombocytopenia in medical-surgical critically ill patients: prevalence, incidence, and risk factors. J Crit Care. 2005; 20(4):348-53. 13. 45. Moreau D, Timsit J F, Vesin A, Garrouste-Orgeas M, de Lassence A, Zahar J R, et al. Platelet count decline: an early prognostic marker in critically ill patients with prolonged ICU stays. Chest. 2007; 131(6):1735-41. 14. 46. Hui P, Cook D J, Lim W, Fraser G A, Arnold D M. The frequency and clinical significance of thrombocytopenia complicating critical illness: a systematic review. Chest. 2011; 139(2):271-8.15. 47. Venkata C, Kashyap R, Farmer J C, Afessa B. Thrombocytopenia in adult patients with sepsis: incidence, risk factors, and its association with clinical outcome. J Intensive Care. 2013; 1(1):9. 48. Semple J W, Freedman J. Platelets and innate immunity. Cell Mol Life Sci. 2010; 67(4):499-511. 17. 49. Semple J W, Italiano J E Jr, Freedman J. Platelets and the immune continuum. Nat Rev Immunol. 2011; 11(4):264-74.18. 50. Vieira-de-Abreu A, Campbell R A, Weyrich A S, Zimmerman G A. Platelets: versatile efector cells in hemostasis, infammation, and the immune continuum. Semin Immunopathol. 2012; 34(1):5-30. 19. 51. Herter J M, Rossaint J, Zarbock A. Platelets in infammation and immunity. J Thromb Haemost. 2014; 12(11):1764-75.20. 52. Morrell C N, Aggrey A A, Chapman L M, Modjeski K L. Emerging roles for platelets as immune and inflammatory cells. Blood. 2014; 123(18):2759-67. 21. 53. Xu X R, Zhang D, Oswald B E, Carrim N, Wang X, Hou Y, et al. Platelets are versatile cells: new discoveries in hemostasis, thrombosis, immune responses, tumor metastasis and beyond. Crit Rev Clin Lab Sci. 2016; 53(6):409-30. 54. Yu-Min Shen et al. Evaluating Thrombocytopenia during heparin therapy. Jama. February 2018; Egede Johansen et al. The potential of antimicrobials to induce thrombocytopenia in critically ill patients: data from a a randomized controlled trial. PLOS one. November 2013; Vol. 8. 55. Van der Poll et al. Pathogenisis of DIC in sepsis. Sepsis. 1999; 103-109; Koyama et al. Time course of immature platelet count and its relation to thrombocytopenia and mortality in patients with sepsis. PLOS one. January 2018. 56. Guru et al. Association of Thrombocytopenia and Mortality in Critically III Patients on Continuous Renal Replacement Therapy. Nephron. 2016; 133:175-182. 57. Larkin et al. Sepsis-associated thrombocytopenia. Thrombosis Research. 2016; 58. Ali N., Auerbach H. (2017), New-onset acute thrombocytopenia in hospitalized patients: pathophysiology and diagnostic approach, Journal of Community Hospital Internal Medicine Perspectives, 7:3, 157-167. 59. Dewitte et al. Blood platelets and sepsis pathophysiology: A new therapeutic prospect in critical ill patients?. Ann. Intensive Care. 2017; 7:115.