PRO-ADRENOMEDULLIN FOR PROGNOSING DISEASE PROGRESSION IN SEVERE ACUTE RESPIRATORY SYNDROME (SARS)
20230160893 · 2023-05-25
Assignee
Inventors
Cpc classification
G01N33/74
PHYSICS
G01N2800/56
PHYSICS
International classification
Abstract
The invention relates to a method for prognosing disease progression in a patient that has or is at risk of developing a severe acute respiratory syndrome (SARS), wherein the method comprises determining a level of pro-adrenomedullin (proADM) or fragment(s) thereof in a sample from the patient, wherein said level indicates the severity of SARS progression. The method is in some embodiments configured for use when a patient exhibits symptoms of a severe acute respiratory syndrome (SARS), a patient exhibits symptoms of infection with a SARS-virus, the patient is infected with a SARS-virus, such as a SARS-coronavirus, such as SARS-CoV2.
Claims
1. A method for reducing the risk of disease progression in a patient that has or is at risk of developing a severe acute respiratory syndrome (SARS), wherein the method comprises determining a level of pro-adrenomedullin (proADM) or fragment(s) thereof in a sample from the patient, wherein said level indicates the severity of SARS progression, and treating said patient to reduce the risk of disease progression.
2. The method according to claim 1, comprising additionally therapy guidance, stratification and/or control for the patient, wherein the level of proADM or fragment(s) thereof indicates whether the patient is at risk of disease progression to a condition that requires intensified treatment and/or disease monitoring.
3. The method according to claim 1, wherein the patient exhibits symptoms of a severe acute respiratory syndrome (SARS) and/or symptoms of infection with a SARS-virus.
4. The method according to claim 1, wherein the patient is infected with a SARS-virus.
5. The method according to claim 4, wherein the patient is infected with SARS-CoV2.
6. The method according to claim 1, wherein the patient belongs to a patient group with an increased risk of an adverse event in case of developing a severe acute respiratory syndrome (SARS).
7. The method according to claim 1, wherein the method comprises: providing a sample from said patient, determining a level of proADM or fragment(s) thereof in said sample, comparing the level of MR-proADM to a cut-off value, wherein said cut-off value is 0.93 nmol/l±20%, wherein a level of proADM or fragment(s) thereof in said sample above the cut-off value indicates that the patient is at risk of disease progression to a condition that requires intensified treatment and/or disease monitoring, and the patient receives treatment to reduce the risk of disease progression.
8. The method according to claim 7, wherein a level of proADM level or fragment(s) thereof in said sample above the cut-off value indicates that the patient is at risk of a disease progression to a condition that requires hospitalization.
9. (canceled)
10. The method according to claim 1, wherein the patient is infected with a SARS-coronavirus and shows mild or no symptoms of SARS or of infection with a SARS-virus.
11. (canceled)
12. The method according to claim 1, wherein the method comprises prognosing a subsequent adverse event in the health of a patient that has been diagnosed with SARS, wherein a level of proADM or fragment(s) thereof indicates the likelihood of a subsequent adverse event in the health of said patient, and treating the patient to reduce the risk of the adverse event.
13. The method according to claim 12, wherein the adverse event in the health of said patient is death, a new infection, respiratory failure, and/or organ failure.
14. The method according to claim 12, wherein the treatment received by the patient comprises one or more of symptomatic treatment, treatment to reduce fever and/or pain, anti-inflammatory treatment, antiviral treatment, antibiotic treatment, oxygen support, invasive mechanical ventilation, non-invasive mechanical ventilation, renal replacement therapy, vasopressor use, fluid therapy, extracorporeal blood purification and/or organ protection.
15. The method according to claim 12, wherein a high severity level of proADM or fragment(s) thereof determined in the sample is indicative of a subsequent adverse event, wherein the high severity level is above 2.25 nmol/l±20%.
16. The method according to claim 15, wherein the patient is an intensive care unit (ICU)-patient and a high severity level of proADM or fragment(s) thereof indicates keeping said patient on the ICU and modifying the treatment of the patient in the ICU, or the patient is not an intensive care unit (ICU)-patient and the high severity level of proADM or fragment(s) thereof indicates transferring said patient to an ICU.
17. A kit for carrying out the method of claim 1, comprising: detection reagents for determining the level of proADM or fragment(s) thereof in a sample from a patient, and reference data for the risk of a patient as to whether disease progression to a condition that requires intensified treatment and/or disease monitoring will occur, comprising a risk threshold or cut-off value, wherein said reference data is 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 with the risk threshold or cut-off value; detection reagents for determining the presence of a SARS-virus infection; and optionally, detection reagents for determining the level of at least one additional parameter or biomarker or fragment(s) thereof, in a sample from a patient, and reference data comprising reference levels for said at least one additional biomarker, for a risk threshold or cut-off value, wherein said reference data is stored on a computer readable medium and/or employed in the form of computer executable code, configured for comparing the determined levels of the at least one additional biomarker or fragment(s) thereof with the threshold or cut-off value.
18. The method according to claim 1, wherein the treatment received by the patient comprises one or more of symptomatic treatment, treatment to reduce fever and/or pain, anti-inflammatory treatment, antiviral treatment, antibiotic treatment, oxygen support, invasive mechanical ventilation, non-invasive mechanical ventilation, renal replacement therapy, vasopressor use, fluid therapy, extracorporeal blood purification and/or organ protection.
19. The method according to claim 4, wherein the SARS-virus is a coronavirus.
20. The method according to claim 1, wherein the patient is infected with a SARS-Coronavirus, wherein the method comprises determining a level of mid-regional pro-adrenomedullin (MR-proADM) in a sample from the patient obtained within 24 hours of hospital admission, wherein a level of or above 0.93 nmol/l±20% indicates the patient is at risk of disease progression to a condition that requires intensified treatment and/or disease monitoring, and the patient receives treatment to reduce the risk of disease progression.
21. The method according to claim 12, wherein the patient is infected with SARS-CoV-2.
22. The method according to claim 12, wherein the treatment received by the patient comprises one or more of a focus cleaning procedure, transfusion of blood products, infusion of colloids, emergency surgery, invasive mechanical ventilation and/or renal or liver replacement.
23. The kit according to claim 17, wherein the detection reagents are for determining the presence of a SARS-CoV2 infection.
24. The kit according to claim 17, wherein the additional parameter or biomarker is procalcitonin (PCT), D-Dimer, Troponin, body weight and/or age.
Description
FIGURES
[0598] The invention is further described by the following figures. These are not intended to limit the scope of the invention, but represent preferred embodiments of aspects of the invention provided for greater illustration of the invention described herein.
[0599]
[0600]
[0601]
EXAMPLES
[0602] The invention is further described by the following examples. These are not intended to limit the scope of the invention, but represent preferred embodiments of aspects of the invention provided for greater illustration of the invention described herein.
Example 1
[0603] Pro-ADM (MR-proADM) values were measured in plasma samples of 90 patients that required hospitalization due to SARS-CoV-2 infection. Pro-ADM in samples was quantified by using the MR-proADM Kryptor assay (Thermo Fisher Scientific, BRAHMS GmbH).
[0604] Available samples were measured at different time points: time point 1 (within 24h (day 1) from hospital admission), time point 2 (day 3 or 4 from hospitalization), time point 3 (day 5 or 6 from hospitalization) and time point 4 (day 7 or 8 from hospitalization). During the course of clinical care, 24 patients were transferred to the intensive care unit (ICU). Pro-ADM values were related to in-hospital mortality of patients.
TABLE-US-00005 TABLE 1 Crude and adjusted association of overall pro ADM and cut-offs and in hospital mortality. Univariate Multivariate Cut Non- OR OR* Pro-ADM off Survivors Survivors (95% CI), (95% CI), Time Point nmol/l N = 70 N = 17 p-Value AUC p-value p-value Time Point 1 Pro ADM 0.8 1.3 <0.01 0.79 3.2 (1.3 5.3 (1.3 overall, (0.7 1.1) (1.1; 2.3) to 8.1), to 21.9), median (IQR) p = 0.012 p = 0.02 Pro ADM cut <0.75 20 (34%) 1 (7%) 0.02 offs n (%) >0.75 31 (53%) 7 (50%) 4.5 (0.5 2.7 (0.3 to 39.5), to 27.4), p = 0.17 p = 0.406 >1.5 8 (14%) 6 (43%) 15.0 (1.5 9.9 (0.7 to 145.2), to 150.2), p = 0.02 p = 0.09 <0.87 33 (56%) 1 (7%) >0.87 26 (44%) 13 (93%) <0.01 16.5 (2.02 9.2 (0.9 to 134.5), to 95.3), p = 0.009 p = 0.06 Time Point 2 ProADM 1.0 2.5 <0.01 0.85 3.1 (1.5 2.9 (1.2 overall, (0.8; 1.4) (1.4; 4.0) to 6.4), to 6.9), median (IQR) p = 0.002 p = 0.02 Pro ADM cut <0.75 11 (22%) 0 (0%) NA NA offs, n (%) >0.75 28 (55%) 4 (29%) NA NA >1.5 12 (24%) 10 (71%) NA NA <0.87 21 (41%) 6 (0%) NA NA >0.87 30 (59%) 14 (100%) <0.01 NA NA Pro ADM <1.1 32 (63%) 2 (14%) <0.01 median cut off, n (%) >1.1 19 (37%) 12 (100%) 10.1 (2.0 9.3 (1.4 to 50.1), to 60.7), p = 0.005 p = 0.02 Time Point 3 ProADM 0.9 3.8 <0.01 0.94 3.7 (1.6 3.5 (1.3 overall, (0.6; 1.3) (2.6; 8.3) to 8.9), to 10.1); median (IQR) p = 0.003 p = 0.02 Pro ADM cut <0.75 16 (38%) 0 (0%) <0.01 NA NA offs, n (%) >0.75 19 (45%) 1 (10%) NA NA >1.5 7 (17%) 9 (90%) NA NA <0.87 21 (50%) 0 (0%) NA NA >0.87 21 (50%) 10 (100%) <0.01 NA NA Pro ADM <1.1 28 (67%) 0 (0%) <0.01 NA NA median cut off, n (%) >1.1 14 (33%) 10 (100%) NA NA Time Point 4 Pro ADM 1.1 2.5 <0.01 0.86 3.7 (0.8 2.7 (0.9 overall, (0.7; 1.8) (1.6; 9.2) to 16.7); to 7.9); median (IQR) p = 0.08 p = 00.06 Pro ADM cut <0.75 8 (26%) 0 (0%) 0.06 NA NA offs, n (%) >0.75 13 (42%) 1 (17%) NA NA >1.5 10 (32%) 5 (83%) NA NA <0.87 10 (32%) 0 (0%) NA NA >0.87 21 (68%) 6 (100%) 0.1 NA NA Pro ADM <1.3 19 (61%) 1 (17%) 0.04 median cut off, n (%) >1.3 12 (39%) 5 (83%) 7.9 (0.8 3.8 (0.3 to 76.3); to 53.6), p = 0.07 p = 0.33 *adjusted for gender, age and age adjusted Carlson Index.
TABLE-US-00006 TABLE 2 Diagnostic Accuracy. Pro Positive Negative ADM cut predictive predictive offs Sensitivity Specificity value value 0.75 nmol/l 92,9 (95% Cl 33,9 (95% Cl 25.0 (95% Cl 95.2 (95% Cl 66.1 to 99.8) 22.1 to 47.4) 14.0 to 38.9) 76.2 to 99.9) 0.87 nmol/l 92,9 (95% CI 55.9 (95% Cl 33.3 (95% Cl 97.1 (95% Cl 66.1 to 99.8) 42.4 to 68.8) 19.1 to 50.2) 84.7 to 99.9) 1.5 nmol/l 42.9 (95% Cl 86.4 (95% Cl 42.9 (95% Cl 86.4% (95% Cl 17.7 to 71.1) 75.0 to 94.0) 17.7 to 71.1) 75.0 to 94.0) 2.5 nmol/l 21,4 (95% Cl 4.7 98.3 (95% Cl 98.3 (95% Cl 84.1 (95% Cl to 50.8) 90.9 to 100.0) 90.9 to 100.0) 73.3 to 91.8)
Example 2: Mid-Regional Pro-Adrenomedullin, a Marker of Permeability and Endothelial Stability, in Patients with Confirmed COVID-19 Infection: Results from an Observational Study
[0605] Summary
[0606] Introduction: Pro-adrenomedullin (MR-proADM) is a vasoactive peptide with key roles in reducing vascular hyperpermeability and improving endothelial stability during infection. MR-proADM has shown promise in risk stratification of patients with sepsis, but there is a lack of clinical data about this marker in patients with COVID-19 infection.
[0607] Methods: We included consecutive hospitalized adult patients with confirmed SARS-CoV-2 infection at the Cantonal Hospital Aarau (Switzerland) between February and April 2020. We investigated the association of initial and follow-up MR-proADM levels with in-hospital mortality in logistic regression analysis and area under the ROC curve (AUC).
[0608] Results: Mortality in the 89 included patients was 19% (n=17). Median admission MR-proADM levels (nmol/) were almost 2-fold increased in non-survivors compared to survivors (1.3 (IQR 1.1 to 2.3) vs. 0.8 (IQR 0.7 to 1.1)) and showed good discrimination (AUC 0.78). The association of initial MR-proADM levels and mortality was independent of other prognostic indicators including gender and age-adjusted Charlson Comorbidity Index (adjusted odds ratio 5.47 (95% CI 1.40 to 21.36, p=0.015). For admission MR-proADM levels the optimal threshold regarding mortality was at 0.93 nmol/L with a sensitivity of 93% (negative predictive value 97.3) and a specificity of 60%. Kinetics of MR-proADM over the subsequent days of in-hospital treatment provided further prognostic information.
[0609] Conclusion: Increased levels of MR-proADM are associated with mortality attributable to COVID-19 infection and may help to better risk stratify patients on admission and during the hospital stay.
[0610] Introduction
[0611] The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, is currently affecting millions of people in the world. Coronavirus disease 2019 (COVID-19) caused by SARS-Cov-2, was declared as global pandemic on the 11 Mar. 2020 by the World Health Organization. COVID-19 is highly contagious, which also explains its rapid and worldwide expansion (22). Key mechanisms that may have a role in the pathophysiology of multi-organ injury secondary to infection with SARS-CoV-2 include direct viral toxicity, endothelial cell damage and thrombo-inflammation, dysregulation of the immune response, and dysregulation of the renin-angiotensin-aldosterone system (23). The increased incidence of cardiovascular and thromboembolic complications, immune cell deactivation and sepsis-like multiple organ failure suggests the involvement of multiple pathways. Preliminary evidence suggests that while around 80% of the patients show only mild upper respiratory tract infection symptoms, 15% progress to severe lower respiratory manifestations requiring hospital admittance, and 5% need intensive care unit treatment due to severe diseases progression, primarily characterized by pulmonary failure, sometimes showing a high level inflammation, and multi-organ failure (24). Besides protective and preventive measures to reduce and minimize large-scale outbreaks, a timely assessment of the individual risk profile of a patient presenting to the emergency department may help to improve early decisions about site-of-care and initiation of COVID-19 specific treatments. Having objective measures of a patient's risk for mortality early are thus important to prompt such measures. Herein, prognostic biomarkers may help to estimate a patient's individual risk and provide objective and measurable results within short time of a patient's hospital admission. Such markers may also improve our understanding of the pathophysiology behind COVID-19 infection and its adverse outcomes.
[0612] Whilst different cytokines and blood markers have been compared in patients with different severities of COVID-19 no study today has investigated the potential role of adrenomedullin (ADM) during the host response to COVID-19 (25). ADM and its mid-regional prohormone fragment MR-proADM has been linked to endothelial dysfunction and the risk for organ dysfunction in patients with sepsis and infection of the lung as it is directly related to the status of the endothelium (26-29). Previous research found MR-proADM to be an accurate marker for risk assessment in patients with pneumonia and sepsis and thereby improving clinical scores such as the sequential organ failure assessment score (SOFA-Score) (30). Therefore, MR-proADM is mostly used to improve the identification of organ dysfunction and disease progression to sepsis or septic shock (31). MR-proADM has also been shown to be a good predictor for short and long term mortality in patients with low-respiratory-tract infections and sepsis (32-34). Previously the MR-proADM cut-off of 0.87 nmol/L has been proposed in a large multinational study to best classify patients as low or high risk for mortality (35). Saeed et al. demonstrated in two cohorts that using a MR-proADM cut-off in patients with suspected infection presenting to the emergency department of 0.87 nmol/L can increase out-patient treatment by 15% and 16.6%, with decreased readmission rates and no increase in mortality (35). Our aim was to investigate the association of initial and follow-up levels of MR-proADM levels with mortality in patients with confirmed COVID-19 infection.
[0613] Methods
[0614] Study Design and Setting: This prospective observational study included all consecutively hospitalized adult patients (≥18 years) with a confirmed SARS-CoV-2 infection at the Cantonal Hospital Aarau (Switzerland) between Feb. 26, 2020 and Apr. 30, 2020. The study was approved by the ethical committee (EKZN, 2020-01306). Baseline data of our cohort was previously published in order to understand specific characteristics of this illness in the Swiss population during the initial time of the pandemic (36). In brief, the definition for a confirmed COVID-19 infection were typical clinical symptoms (e.g., respiratory symptoms with or without fever, and/or pulmonary infiltrates and/or anosmia/dysgeusia) together with a positive real-time reverse transcription polymerase chain reaction (RT-PCR) taken from nasopharyngeal swabs or lower respiratory tract specimens, according to the WHO guidance (37). All analyzed data were assessed as part of the clinical routine during the hospitalization (from admission to discharge/death).
[0615] Data collection: Clinical information, including socio-demographics and comorbidities, home medications and COVID-19-specific inpatient medication were assessed until hospital discharge or death and exported from the hospital electronic clinical information system. Experimental treatment was offered to all patients and included, for hospital ward patients, Hydroxychloroquine only (first line) and, Tocilizumab. Azithromycin was also used in patients transferred from France. For all patients the age-adjusted Charlson comorbidity index (38) and the Clinical Frailty Score (up to 9 points) (39) were calculated as part of the clinical routine. Comorbidities were also assessed through chart review and based on the ICD10 code. Further, patient outcomes including in-hospital mortality, admission to the intensive care unit (ICU), length of hospital stay (LOS) as well as length of ICU stay were collected by chart review. Laboratory test results were available according to clinical routine.
[0616] Study objective and endpoint: The objective of this study is to investigate the ability of MR-proADM to predict mortality in confirmed COVID-19 infection patients, in order to classify patients at high or low risk for mortality. The primary endpoint is all cause in-hospital mortality.
[0617] Measurements of MR-proADM: Plasma and serum samples on admission were collected in BD Vacutainer® Heparin and SST tubes. Routine left-over samples were immediately frozen at −70° C. until assayed. Results from routine laboratory tests were recorded. Mid-regional pro-adrenomedullin (MR-proADM) was assessed in batch using a commercially available automated fluorescent sandwich immunoassay (KRYPTOR®, B.R.A.H.M.S Thermo Fisher Scientific. Germany), as described in detail elsewhere (40-41). Briefly, the immunoassay employs two polyclonal antibodies to the amino acids 45-92 of pre-pro-adrenomedullin, the MR-proADM and has a limit of detection (LOD) of 0.05 nmol/L (41). The functional assay sensitivity, defined as the MR-proADM concentration with an inter-assay coefficient of variation of <20%, was 0.25 nmol/L. Values for the analytes followed a Gaussian distribution in healthy individuals without significant differences between males and females (41). The laboratory technicians who measured MR-proADM were blinded to the characteristics of the patients and the characteristics of the study.
[0618] Different time points during hospitalization were analyzed, depending on the available data: [0619] T.sub.0 (hospital admission day.fwdarw.blood draw within 24h from admission) [0620] T.sub.1 (day 3/day 4) [0621] T.sub.2 (day 5/day 6) [0622] T.sub.3 (day 7/day 8)
[0623] Statistical Analysis: Discrete variables are expressed as frequency (percentage) and continuous variables as medians with interquartile ranges (IQR) or mean with standard deviation (SD). Multivariate logistic regression model were used to examine the association of MR-proADM levels with the primary endpoint. As predefined, regression models were adjusted for gender and age-adjusted Charlson comorbidity Index. Odds ratios (OR) and corresponding 95% confidence intervals (CI) were reported as a measure of association and C-Statistics (area under the operating receiver curve (ROC-AUC) as a measure of discrimination. We also validated the prognostic value of different pre-defined MR-proADM cut-offs based on previous studies in other populations, namely 0.75 (27, 42), 0.87 (35), 1.5 (27, 42). Survival analysis and log rank test was used to compare in-hospital mortality according to different MR-proADM-cutoffs. Additionally, sensitivity, specificity, positive and negative predictive values of MR-proADM in predicting in-hospital mortality for different potential cut-offs. A two-sided p-value of <0.05 was considered significant. Statistical analysis was performed using Stata 15.1 (StataCorp, College Station, Tex., USA).
[0624] Results
[0625] A total of 103 patients were hospitalized with of a confirmed COVID-19 infection at the Cantonal Hospital Aarau (Switzerland), whereby 29 were transferred from other hospitals (three cases from France, one case from the Canton Ticino, 25 cases from regional hospitals not accepting COVID-19 admissions or treatment at a tertiary care hospital was indicated). Several patients had to be excluded from the analysis due to declined general informed consent (n=4) or no available aliquots for biomarker analysis (n=9). One further patient had to be excluded from the analysis, since still hospitalized and the primary endpoint was not evaluable at the time point of the analysis.
[0626] Baseline Characteristics
[0627] Baseline characteristics including demographics, comorbidities as well as in-hospital treatment and in-hospital endpoints in the overall cohort and stratified according to the primary endpoint are summarized in Table 3. Median age was 67 years (IQR 58-74) and 35% (n=31) were female. A total of 22% (n=20) of patients were taking angiotensin-converting enzyme inhibitors at home, while 19% (n=17) had an angiotensin II receptor blockers prescription and few patients were taking corticosteroids or other immunosuppressive treatments. Patients had a high burden of comorbidities with a median age-adjusted Charlson comorbidity index of 3 points and a high median frailty score of 3 points. The most common comorbidities included hypertension (58%, n=52), chronic kidney disease (27%, n=24) and obesity (29%, n=26). Overall, 49% of patients received an experimental antiviral treatment (mostly Hydroxychloroquine, rarely Ritonavir-boosted Lopinavir). A total of 26% (n=23) of patients developed severe COVID-19 progression characterized by a need for ICU treatment, of whom 18 needed mechanical ventilation. Overall, patients hospitalized due to COVID-19 had a median length of stay (LOS) of 9.0 days (IQR 5.0-18.0). 19% (n=17) of the hospitalized patients reached the primary endpoint, defined as in-hospital mortality.
[0628] A majority of patients presented with high clinical severity, particularly regarding the respiratory system with a high respiratory rate and evidence of compromised oxygenation in blood gas analysis. There was also a modest increase in the inflammation marker C-reactive protein (CRP) (mean level at 88.5 mg/L) but low levels of procalcitonin (PCT) levels (0.11 μg/L).
[0629] Association of MR-proADM Levels and In-Hospital Mortality
[0630] Median admission MR-proADM levels were almost 2-fold increased in non-survivors compared to survivors (1.3 (IQR 1.1 to 2.3) vs. 0.8 (IQR 0.7 to 1.1). We found a strong association of MR-proADM levels at each of the analyzed time points and in-hospital mortality. These associations remained robust in the multivariate model adjusted for gender and age-adjusted Charlson comorbidity index (Table 4). Regarding discrimination, MR-proADM had high AUCs for each time point with highest results on time point 3 (day 5/6 of hospitalization) with an AUC of 0.92 compared with an AUC of 0.78 on admission.
[0631] Furthermore, diagnostic accuracy of MR-proADM in predicting in-hospital was analyzed for different cut-offs (Table 5). We found an optimal cut-off at 0.93 nmol/L which was close to the median within the analyzed cohort with a sensitivity of 92.9% (95% CI 66.1 to 99.8) and a specificity of 60% (95% CI 46.5 to 72.4). Further, the cut-off at 0.93 nmol/L showed an excellent negative predictive value with 97.3 (95% CI 85.5 to 99.9). Results were also similar for other previously proposed cut-offs (i.e., at 0.75 nmol/L and 0.87 nmol/L). The use of higher cut-offs such as 1.5 nmol/L and 2.5 nmol/L showed a low sensitivity (42.9% and 21.4%) but higher specificity and positive predictive values.
[0632] We also confirmed our analysis in a time to event survival analysis showing short time to death in patients with higher MR-proADM levels. We classified patients according to different cut-offs and the median of MR-proADM values (>1.5 nmol/l, >0.87 nmol/l and >0.93 nmol/l). Results were also significant in regard to log-rank testing (log-rank, p=0.014 for survival curve A, log-rank p=0.0014 for survival curve B and log-rank p=0.0004 for survival curve C in
[0633] Kinetics of Serial Measurements of MR-proADM
[0634] The kinetic of the MR-proADM according to the primary endpoint is illustrated in
[0635] Discussion
[0636] The results of this prospective study involving patients with COVID-19 infection in the early phase of the pandemic are twofold. First, we found that levels of admission MR-proADM, a marker that reflects permeability and endothelial stability, are twofold increased in patients with a fatal outcome and are thus strongly associated with in-hospital mortality, also in statistical models adjusted for age, gender and comorbid-burden. Second, when looking at the kinetics of MR-proADM, we also found a further increase in its level in non-survivors while survivors have lower levels that remain low during follow-up. These results suggest that MR-proADM may be helpful in the early risk stratification and monitoring of patients with COVID-19.
[0637] Early identification of patients at high risk for adverse outcomes with confirmed COVID-19 is crucial in order to predict severe diseases progression and reduce COVID-19 associated mortality. Yet, prediction rules for patients individual risk profile as well as the ability to predict outcomes are still lacking. Whilst several pro-inflammatory cytokines and prognostic markers have already been investigated in patients with COVID-19, no study has examined the prognostic value of MR-proADM measurements for the identification of COVID-19 patients at high risk for mortality. Yet, MR-proADM holds great promise as a biomarker in COVID-19 as it plays a key role in reducing vascular permeability and promotes endothelial stability and integrity following severe infection, and thus may help in identifying patients at risk of COVID-19 induced endotheliitis. Indeed, a recent study investigating gene upregulation in patients with systemic capillary leak syndrome (SCLS), characterized by plasma leakage into peripheral tissue and transient episodes of hypotensive shock and edema, found that ADM was not only one of the most upregulated genes, but that subsequent application to endothelial cells resulted in a protective effect on vascular barrier function. (25) Herein, this study evaluated the prognostic performance of initial and follow-up measurements of MR-proADM in confirmed COVID-19 patients treated in a tertiary care hospital in Aarau, Switzerland. The analyzed data showed that MR-proADM is a strong predictor for in-hospital mortality, with a high discriminatory ability in patients with confirmed COVID-19. This results suggest that MR-proADM levels in conjunction with clinical evaluation and other laboratory findings may help to identify and classify patients presenting to the emergency department with low and high risk for adverse outcome.
[0638] The prognostic relevance of MR-proADM was already analyzed and proved in several prior studies investigating patients with community acquired pneumonia (CAP) (43-45), chronic obstructive pulmonary disease (COPD) (46, 47) and cardiovascular diseases (48, 49). Our analysis is in line with this research and further expands the field to COVID-19 disease, which is also a very severe illness with however a very particular pathophysiology involving different organ systems as demonstrated in recent studies. Interestingly, we also found that previously proposed cut-off level of 0.87 nmol/L had a very sensitivity and negative likelihood ratio to rule out mortality in COVID-19 patients, similar to a previous study (35). Also, similar to our analysis, Christ-Crain et al. showed that MR-proADM has a high prognostic accuracy with an AUC of 0.81 for the prediction of ICU mortality in patients with sepsis (40) in contrast, Suberviola et al. found only a moderate value for the prediction of hospital mortality in sepsis patients with an AUC of 0.62 (50). This conflicting results on the prognostic role of MR-proADM may be explained by differences in study population like patient characteristics, disease severity underlying diseases and infection as well as sample size of the analyzed patient population. Zhoue et al (51) showed that already existing risk scores like CRB-65 and qSOFA may be helpful to identity COVID-19 affected patients with a poor prognosis, but they include too many false-positive patients. Consequences are a higher than needed demand for often limited resources. In this respect, other studies have confirmed that MR-proADM is more accurate compared to risk scores used alone and that it can improve the accuracy of these scores when used in combination (30, 52). Further, MR-proADM can be easy performed by a biomarker assay compared to scores that are often complex to calculate.
[0639] We found an optimal cut-off at 0.93 nmol/L in our analyzed cohort that can be recommended for the assessment of disease severity, disease progression, risk for in-hospital mortality and also for decisions regarding patient disposition. This cut-off is very close to already defined and validated MR-proADM cut-offs at 0.75 nmol/L (27, 42) and at 0.87 nmol/L (35). Higher cut-offs between 1.5 nmol/L and 2.5 nmol/L had low sensitivity but fairly high positive prognostic values. An initial MR-proADM value below the defined cut-off within the first 24 hours after presentation to the ED can be interpreted as low risk for mortality and can predict a mild course of disease while MR-proADM value above the cut-off indicates a high risk for mortality and thus predict a severe course of disease. Therefore, physicians may choose to monitor patients more closely. With this classification hospital resources could potentially be used more efficiently by improve site-of-care decisions and early discharge of patients. This is essential, especially for regions where healthcare systems reach their maximum capacity during peaks of the COVID-19 pandemic.
[0640] Conclusion
[0641] In conclusion, this first study evaluating MR-proADM in patients with COVID-19 infection, confirms its high prognostic value regarding prediction of in-hospital mortality. When used in conjunction With clinical findings and results of other laboratory parameters during an initial risk assessment, MR-proADM may improve early risk stratification in this patient population. The strong prognostic value of MR-proADM in COVID-19 confirmed patients is of interest and warrants further investigation.
[0642] Tables of Example 2
TABLE-US-00007 TABLE 3 Demographic data, comorbidities, in-hospital treatment and in-hospital endpoints in the study population Overall Survivors Non-Survivors p-value Sociodemographics Age [years], median (IQR) 67.0 (56.0, 74.0) 63.0 (55.5, 74.0) 74.0 (69.0, 80.0) <0.01 Female gender, n (%) 31 (35%) 30 (42%) 1 (6%) <0.01 Nationality, n (%) France 3 (4%) 3 (4%) 0 (0%) 0.80 Italy 6 (7%) 5 (7%) 1 (6%) Switzerland 56 (63%) 46 (64%) 10 (59%) Turkey 4 (4%) 4 (6%) 0 (0%) Others 20 (22%) 14 (19%) 6 (35%) Pre-esisting risk-factors and medication Active smoker, n (%) 6 (9%) 5 (9%) 1 (8%) 0.95 Corticosteriod use, n (%) 2 (2%) 1 (1%) 1 (6%) 0.26 Imuunosuppressant, n (%) 4 (4%) 2 (3%) 2 (12%) 0.11 Angiotensin converting 20 (22%) 14 (19%) 6 (35%) 0.16 enzyme-inhibitor, n (%) Angiotensin II receptor 17 (19%) 13 (18%) 4 (24%) 0.61 blockers, n (%) Pre-admission history Symptom onset before 8.0 (5.0, 10.0) 8.0 (4.0, 11.0) 7.0 (5.0, 8.0) 0.47 admission [days], median (IQR) Transfer from another 27 (30%) 21 (29%) 6 (35%) 0.62 Hospital, n (%) Comorbidities Age adjusted Charlson 3.0 (2.0, 6.0) 3.0 (2.0, 6.0) 5.0 (3.0, 9.0) <0.01 comorbidity Index, median (IQR) Clinical frailty scale, 3.0 (2.0, 4.0) 3.0 (2.0, 4.0) 3.0 (3.0, 4.0) 0.27 median (IQR) Cancer, n (%) 9 (10%) 5 (7%) 4 (24%) 0.04 Hypertension, n (%) 52 (58%) 39 (54%) 13 (76%) 0.09 Coronary artery disease, 23 (26%) 16 (22%) 7 (41%) 0.11 n (%) Chronic heart failure, n (%) 3 (3%) 3 (4%) 0 (0%) 0.39 Asthma, n (%) 14 (16%) 11 (15%) 3 (18%) 0.81 COPD, n (%) 7 (8%) 4 (6%) 3 (18%) 0.81 Obsturctive sleep apnea, 12 (13%) 9 (13%) 3 (18%) 0.58 n (%) Solid organ transplant 12 (13%) 9 (13%) 3 (18%) 0.10 recipient, n (%) Rheumatic disease, n (%) 2 (2%) 1 (1%) 0 (0%) 0.63 Chronic kidney disease, 24 (27%) 16 (22%) 8 (47%) 0.04 n (%) Obesity (BMI > 30 kg/m.sup.2), 26 (29%) 22 (31%) 4 (24%) 0.57 n (%) Diabetes, n (%) 21 (24%) 17 (24%) 4 (24%) 0.99 In-hospital treatment Treatment specification, n (%) Hydroxychloroquine 36 (40%) 27 (38%) 9 (53%) 0.04 Hydroxychloroquine + 3 (3%) 3 (4%) 0 (0%) azithromycin Hydroxychloroquine + 1 (1%) 1 (1%) 0 (0%) Tocilizumab Lobinavir/ritonavir 2 (2%) 2 (3%) 0 (0%) Tocilizumab 2 (2%) 0 (0%) 2 (12%) Symptomatic treatment only 45 (51%) 39 (54%) 6 (35%) Antibiotic treatment, n (%) 38 (43%) 25 (35%) 13 (76%) <0.01 In-hospital endpoints ICU care, n (%) 23 (26%) 16 (22%) 7 (41%) 0.11 Need for mechanical 18 (78%) 12 (75%) 6 (86%) 0.09 ventilation, n (%) Length of stay, median (IQR) 9.0 (5.0, 18.0) 9.0 (5.0, 12.5) 15.0 (5.0, 24.0) 0.08 Abbreviations: BMI, Body-Mass-Index; COPD, Chronic Obstructive Pulmonary Disease; ICU, intensive care unit; IQR, interquartile range
TABLE-US-00008 TABLE 4 Univariate and multivariate logistic regression analysis for different MR-proADM cut-offs at different time points. Univariate Multivariate* Non- OR OR Survivors Survivors (95% CI), (95% CI), n = 72 n = 17 p-value AUC p-value p-value MR-proADM Time point 0 (within 24 h from admission) MR-proADM 0.8 1.3 <0.01 0.78 3.22 5.47 overall, (0.7, 1.1) (1.1, 2.3) (1.29, 8.06), (1.40, 21.36), median (IQR) p = 0.012 p = 0.015 MR-proADM cut-off, n (%) <0.75 20 (33%) 1 (7%) 0.02 Reference Reference >0.75 32 (53%) 7 (50%) 4.38 3.59 (0.50, 38.26), (0.38, 33.95), p = 0.182 p = 0.265 >1.5 8 (13%) 6 (43%) 15.00 14.39 (1.55, 145.22), (1.02, 202.25), p = 0.019 p = 0.048 MR-proADM 0.87-cut-offs, n (%) <0.87 33 (55%) 1 (7%) <0.01 Reference Reference >0.87 27 (45%) 13 (93%) 15.89 11.78 (1.95, 129.31), (1.23, 112.38), p = 0.010 p = 0.032 MR-proADM median- cutoff, n (%) <0.03 36 (60%) 1 (7%) <0.01 Reference Reference >0.03 24 (40%) 13 (93%) 19.50 14.40 (2.39, 159.00), (1.48, 139.83), p = 0.006 p = 0.021 MR-proADM Time point 1 (day 3/day 4 of hospitalization) MR-proADM 1.0 2.5 <0.01 0.84 2.84 2.84 overall, (0.8, 1.5) (1.4, 4.0) (1.44, 5.60), (1.34, 6.00), median (IQR) p = 0.003 p = 0.006 MR-proADM cut-offs, n (%) <0.75 11 (21%) 0 (0%) <0.01 NA NA >0.75 28 (54%) 4 (29%) NA NA >1.5 13 (25%) 10 (71%) NA NA MR-proADM 0.87-cut-offs, n (%) <0.87 21 (40%) 0 (0%) <0.01 NA NA >0.87 31 (60%) 14 (100%) NA NA MR-proADM median- cut-off, n (%) <1.1 32 (62%) 2 (14%) <0.01 Reference Reference >1.1 20 (33%) 12 (86%) 9.60 7.46 (1.94, 47.44), (1.35, 41.26), p = 0.006 p = 0.021 MR-proADM Time point 2 (day 5/day 6 of hospitalization) MR-proADM 0.9 3.8 <0.01 0.92 2.02 1.94 overall, (0.6, 1.4) (2.6, 8.3) (1.22, 3.35), (1.15, 3.26), median (IQR) p = 0.006 p = 0.012 MR-proADM cut-offs, n (%) <0.75 16 (36%) 0 (0%) <0.01 NA NA >0.75 19 (43%) 1 (10%) NA NA >1.5 9 (20%) 9 (90%) NA NA MR-proADM 0.87-cut-offs, n (%) <0.87 21 (48%) 0 (0%) NA NA >0.87 23 (52%) 10 (100%) <0.01 NA NA MR-proADM median- cut-off, n (%) <1.1 28 (64%) 0 (0%) <0.01 NA NA >1.1 16 (36%) 10 (100%) NA NA MR-proADM Time point 3 (day 7/day 8 of hospitalization) MR-proADM 1.3 2.5 0.01 0.82 1.29 1.28 overall, (0.8, 1.8) (1.6, 9.2) (0.99, 1.67), (0.97, 1.69), median (IQR) p = 0.055 p = 0.087 MR-proADM cut-offs, n (%) <0.75 8 (24%) 0 (0%) 0.09 NA NA >0.75 13 (39%) 1 (17%) NA NA >1.5 12 (36%) 5 (83%) NA NA MR-proADM 0.87-cut-offs, n (%) <0.87 10 (30%) 0 (0%) NA NA >0.87 23 (70%) 6 (100%) 0.1 NA NA MR-proADM median- cut-off, n (%) <1.3 19 (58%) 1 (17%) 0.07 Reference Reference >1.3 14 (42%) 5 (83%) 6.79 4.71 (0.71, 64.72), (0.45, 48.71), p = 0.096 p = 0.194 *adjusted for, gender and age adjusted Charlson comorbidity Index. Abbreviations: AUC, area under the curve; CI, confidence interval; OR, odds ratio; MR-proADM, pro-adrenomedullin.
TABLE-US-00009 TABLE 5 Diagnostic accuracy of different MR-proADM cut-offs at baseline. Sensitivity Specificity Positive predictive Negative predictive (95% Cl) (95% Cl) value (95% Cl) value (95% Cl) MR-proADM cut-off values 0.75 nmol/L 92.9 33.3 24.5 95.2 (95% Cl 66.1 to (95% Cl 22.7 to (95% Cl 13.8 (95% Cl 76.2 to 99.9) 99.8) 46.7) to 38.3) 0.87 nmol/L 92.9 55.0 32.5 97.1 (95% Cl 86.1 to (95% Cl 41 6 to (95% Cl 18.6 (0 5% Cl 84.7 to 99.9) 99.8) 67.9) to 49.1) 1.5 nmol/L 42.9 86.7 42.9 86.7 (95% Cl 17.7 to (95% Cl 75.4 to (95% Cl 17.7 (95% Cl 75.4 to 94.1) 71.1) 94.1) to 71.1) 2.5 nmol/L 21.4 98 3 75.0 84.3 (95% Cl 4.7 to (95% Cl 91.1 to (95% Cl 19.4 (95% Cl 73.6 to 91.9) 50.8) 100.0) to 99.4) MR-proADM- Median 0.93 nmol/L 92.9 60.0 35.1 97.3 (95% Cl 66.1 to (95% Cl 46.5 to (95% Cl 20.2 (95% Cl 85.8 to 99.9) 99.8) 72.4) to 52.5)
REFERENCES
[0643] 1. https://www.mayoclinic.org/diseases-conditions/sars/symptoms-causes/syc-20351765 [0644] 2. Vjaykrishna, D.; Smith, G. J.; Zhang, J. X.; Peiris, J. S.; Chen, H.; Guan, Y. Evolutionary insights into the ecology of coronaviruses. J. Virol. 2007, 81, 4012-4020. [0645] 3. Yuefei, J.; Haiyan, Y.; Wangquan, J.; Weidong, W; Shuaiyin, C.; Weiguo, Z; Guangcai, D.
[0646] Virology, Epidemiology, Pathogenesis and Control of Covid-19. Viruses 2020, 12, 372. [0647] 4. Al-Tawfiq Asymptomatic Coronavirus Infection: MERS-CoV and SARS-CoV-2 (COVID-19). Travel Med Infect Dis. 2020 February (Epub) [0648] 5. Gu, J; Gong, E; Zhang, B: et al. Multiple organ infection and the pathogenesis of SARS. J Exp Med 2005 205 (3): 415-424 [0649] 6. Fei Z.; Ting, Y; Ronghui, D; et al. Clinical cours and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet 2020, 395:1054-1062 [0650] 7. 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. [0651] 8. 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. [0652] 9. Vallet B. Endothelial cell dysfunction and abnormal tissue perfusion. Crit Care Med. May 2002; 30(5 Suppl):S229-234. [0653] 10. Gonzalez-Rey E, Chomy A, Varela N, Robledo G, Delgado M. Urocortin and adrenomedullin prevent lethal endotoxemia by down-regulating the inflammatory response. Am J Pathol. Jun. 2006; 168(6):1921-1930. [0654] 11. 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. [0655] 12. 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. [0656] 13. 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. [0657] 14. 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. [0658] 15. 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. [0659] 16. Hartmann O, Schuetz P, Albrich V 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. [0660] 17. Albrich V 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. [0661] 18. Albrich V 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. [0662] 19. Tyagi A, Sethi A K, Girotra G, Mohta M. The microcirculation in sepsis. Indian J Anaesth. June 2009; 53(3):281-293. [0663] 20. Hemandez G, Bruhn A, Ince C. Microcirculation in sepsis: new perspectives. Curr Vasc Pharmacol. Mar. 1, 2013; 11(2):161-169. [0664] 21. Vijayanand, P.; Wilkins, E.; Woodhead, M.; Severe Acute Respiratory Syndrome (SARS): a review. Clin Med 2004; 4:152-60 [0665] 22. Li R, Pei S, Chen B, et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science 2020; 368(6490): 489-93. [0666] 23. Gupta A, Madhavan M V, Sehgal K, et al. Extrapulmonary manifestations of COVID-19.
[0667] Nature medicine 2020; 26(7): 1017-32. [0668] 24. Wu Z, McGoogan J M. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72314 Cases From the Chinese Center for Disease Control and Prevention. JAMA: the journal of the American Medical Association 2020. [0669] 25. Wilson D C, Schefold J C, Baldira J, Spinetti T, Saeed K, Elke G. Adrenomedullin in COVID-19 induced endotheliitis. Crit Care 2020; 24(1): 411. [0670] 26. Valenzuela-Sanchez F, Valenzuela-Mendez B, Rodriguez-Gutierrez J F, Estella-Garcia A, Gonzalez-Garcia M A. New role of biomarkers: mid-regional pro-adrenomedullin, the biomarker of organ failure. Ann Transl Med 2016; 4(17): 329. [0671] 27. Kutz A, Hausfater P, Amin D, et al. The TRIAGE-ProADM Score for an Early Risk Stratification of Medical Patients in the Emergency Department—Development Based on a Multi-National, Prospective, Observational Study. PLoS One 2016; 11(12): e0168076. [0672] 28. Renaud B, Schuetz P, Claessens Y E, Labarere J, Albrich W, Mueller B. Proadrenomedullin improves Risk of Early Admission to ICU score for predicting early severe community-acquired pneumonia. Chest 2012; 142(6): 1447-54. [0673] 29. Labarere J, Schuetz P, Renaud B, Claessens Y E, Albrich W, Mueller B. Validation of a clinical prediction model for early admission to the intensive care unit of patients with pneumonia Academic emergency medicine: official journal of the Society for Academic Emergency Medicine 2012; 19(9): 993-1003. [0674] 30. Schuetz P, Wolbers M, Christ-Crain M, et al. Prohormones for prediction of adverse medical outcome in community-acquired pneumonia and lower respiratory tract infections. Crit Care 2010; 14(3): R106. [0675] 31. Elke G. Bloos F, Wilson D C, et al. The use of mid-regional proadrenomedullin to identify disease severity and treatment response to sepsis—a secondary analysis of a large randomised controlled trial. Critical care 2018; 22(1): 79. [0676] 32. Huang D T, Angus D C, Kellum J A, et al. Midregional proadrenomedullin as a prognostic tool in community-acquired pneumonia. Chest 2009; 136(3): 823-31. [0677] 33. Christ-Crain M, Morgenthaler N G, Stolz D. et al. Pro-adrenomedullin to predict severity and outcome in community-acquired pneumonia [ISRCTN04176397]. Crit Care 2006; 10(3): R96. [0678] 34. Kruger S, Ewig S, Giersdorf S, et al. Cardiovascular and inflammatory biomarkers to predict short- and long-term survival in community-acquired pneumonia: Results from the German Competence Network, CAPNETZ. Am J Respir Crit Care Med 2010; 182(11): 1426-34. [0679] 35. Saeed K, Wilson D C, Bloos F, et al. The early identification of disease progression in patients with suspected infection presenting to the emergency department: a multi-centre derivation and validation study. Critical care 2019; 23(1): 40. [0680] 36. Gregoriano C, Koch D, Haubitz S, et al. Characteristics, predictors and outcomes among 99 patients hospitalised with COVID-19 in a tertiary care centre in Switzerland: an observational analysis. Swiss medical weekly 2020; 150: w20316. [0681] 37. WHO. Clinical management of severe acute respiratory infection when novel coronavirus (nCov) infection is suspected; interim guidance, 25 Jan. 2020. Published Jan. 25, 2020. [0682] 38. Charlson M, Szatrowski T P, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol 1994; 47(11): 1245-51. [0683] 39. Juma S, Taabazuing M M, Montero-Odasso M. Clinical Frailty Scale in an Acute Medicine Unit: a Simple Tool That Predicts Length of Stay. Can Geriatr J 2016; 19(2): 34-9. [0684] 40. 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. Critical care 2005; 9(6): R816-24. [0685] 41. Morgenthaler N G, Struck J, Alonso C, Bergmann A. Measurement of midregional proadrenomedullin in plasma with an immunoluminometric assay. Clin Chem 2005; 51(10): 1823-9. [0686] 42. 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 infectious diseases 2011; 11: 112. [0687] 43. Cavallazzi R, El-Kersh K, Abu-Atherah E, et al. Midregional proadrenomedullin for prognosis in community-acquired pneumonia: a systematic review. Respir Med 2014; 108(11): 1569-80 [0688] 44. Gordo-Remartinez S, Calderon-Moreno M, Femandez-Herranz J, et al. Usefulness of midregional proadrenomedullin to predict poor outcome in patients with community acquired pneumonia. PLoS One 2015; 10(6): e0125212. [0689] 45. Espana P P, Capelastegui A, Mar C, et al. Performance of pro-adrenomedullin for identifying adverse outcomes in community-acquired pneumonia. J Infect 2015; 70(5): 457-66. [0690] 46. Grolimund E, Kutz A, Marlowe R J, et al. Long-term Prognosis in COPD Exacerbation: Role of Biomarkers, Clinical Variables and Exacerbation Type. COPD 2015; 12(3): 295-305. [0691] 47. Schuetz P. Marlowe R J, Mueller B. The prognostic blood biomarker proadrenomedullin for outcome prediction in patients with chronic obstructive pulmonary disease (COPD): a qualitative clinical review. Clin Chem Lab Med 2015; 53(4): 521-39. [0692] 48. Shah K S, Marston N A, Mueller C, et al. Midregional proadrenomedullin predicts mortality and major adverse cardiac events in patients presenting with chest pain: results from the CHOPIN trial. Acad Emerg Med 2015; 22(5): 554-83. [0693] 49. Yuyun M F, Narayan H K, Ng L L. Prognostic significance of adrenomedullin in patients with heart failure and with myocardial infarction. Am J Cardiol 2015; 115(7): 986-91. [0694] 50. 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. Intensive Care Med 2013; 39(11): 1945-52. [0695] 51. Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 2020; 395(10229): 1054-62. [0696] 52. Legramante J M, Mastropasqua M, Susi B, et al. Prognostic performance of MR-pro-adrenomedullin in patients with community acquired pneumonia in the Emergency Department compared to clinical severity scores PSI and CURB. PloS one 2017; 12(11): e0187702.