PTX3 AS PROGNOSTIC MARKER IN COVID-19
20230251274 · 2023-08-10
Assignee
Inventors
- Barbara BOTTAZZI (Pieve Emanuele (MI), IT)
- Cecilia GARLANDA (Pieve Emanuele (MI), IT)
- Alberto MANTOVANI (PIEVE EMANUELE (MI), IT)
Cpc classification
G01N2800/56
PHYSICS
International classification
Abstract
The present invention refers to an in-vitro or ex vivo method for the prognosis of a Coronavirus disease and/or for the monitoring of the efficacy of a therapeutic treatment of a Coronavirus disease having the steps of: a) detecting and/or measuring the amount of the protein PTX3 or of fragments thereof or of the polynucleotide coding for the protein or of fragments thereof in an isolated biological sample obtained from the subject Preferably the method further includes—the step: b) comparing the same with a proper control.
Claims
1. An in-vitro or ex vivo method for the prognosis of a Coronavirus disease and/or for the monitoring of the efficacy of a therapeutic treatment of a Coronavirus disease comprising the steps of: a) detecting and/or measuring the amount of the protein PTX3 or of fragments thereof or of the polynucleotide coding for said protein or of fragments thereof in an isolated biological sample obtained from the subject, preferably the method further comprises the step: b) comparing the same with a proper control.
2. The method according to claim 1, wherein the Coronavirus is a beta Coronavirus, preferably SARS-CoV-2, and/or the Coronavirus disease is selected from the group consisting of: Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), COVID-19, coronavirus-associated acute respiratory distress syndrome (ARDS)
3. The method according to claim 1, wherein the biological sample is selected from the group consisting of: plasma, serum, blood, CSF, saliva, or Bronchoalveolar lavage fluid (BALF) and pulmonary tissue.
4. The method according to claim 1, wherein the amount of PTX3 is detected or measured by means of specific antibody or coulometric or electrochemical detector.
5. The method according to claim 1, wherein the subject is a patient who has been diagnosed with a Coronavirus disease.
6. The method according to claim 1, wherein when PTX3 is higher than the proper control, the subject is at risk of short-term mortality or of being affected by a more severe disease and/or of a poor prognosis.
7. (canceled)
8. (canceled)
9. (canceled)
10. (canceled)
11. Kit for the prognosis of a Coronavirus disease and/or for the monitoring of the efficacy of a therapeutic treatment of a Coronavirus disease comprising: means to detect and/or measure the amount of at least the biomarker PTX3 and optionally control means.
12. (canceled)
Description
[0091] The present invention is therefore illustrated by means of non-limiting examples in reference to the following figures.
[0092]
[0093]
[0094] Left panel (A): PTX3 plasma levels in patients based on the primary outcome (mortality). Right panel (B): PTX3 plasma levels in patients admitted to medical wards (50) or ICU (46).
[0095]
[0096]
EXAMPLES
Methods
Bioinformatic Analysis
[0097] Data relative to the transcriptional response to SARS-CoV-2 infection were derived from datasets deposited within the Gene Expression Omnibus (GEO). Data relative to bulk transcription in human normal bronchial (NHBE) and malignant cell lines (Calu-3 and A549) upon SARS-CoV-2 infection, were derived from the experiments within the series GSE147507.sup.28. Raw bulk RNA-Seq reads were quality inspected with the software “FastQC” (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and aligned with STAR (version 2.6.1).sup.29 on the GRCh38 genome guided by GENCODE annotation (version 33).
[0098] Gene summarized counts were processed in R, genes whose expression was major than 2 reads were filtered and vst normalized with the R package DESeq2.sup.30. Significantly changing genes upon SARS-CoV-2 infection were obtained with DESeq2 by comparing each infected cell line with the respective mock-treated counterpart. Gene identifiers conversions were performed with the “org.Hs.eg.db” library (https://www.bioconductor.org/packages//2.10/data/annotation/html/org.Hs.eg.db.html). Plots were rendered with the R library “ggplot2” (https://ggplot2.tidyverse.org).
[0099] Available single cell RNA-Seq experiments, related to BALF of SARS-CoV2 individuals, were obtained from the public repositories Gene Expression Omnibus (GEO) and FigShare platform under the identifiers GSE145926, GSE149443 and the FigShare platform (https://figshare.com/articles/COVID-19_severity_correlates_with_airway_epithelium-immune_cell_interactions_identified_by_single-cell_analysis/12436517), scRNA-Seq datasets of SARS-CoV-2 infected PBMC (deposited in GEO under the series GSE150728).sup.31 were explored with the portal cellxgene (https://chanzuckerberg.github.io/cellxgene/) and obtained from “The COVID19 Cell Atlas portal” (https://www.covid19cellatlas.org/#wilk20). Sparsecount matrices or Seurat objects were obtained as released and processed with the R package “Seurat”.sup.32 and confirmed with the published pipelines shared by respective authors.
[0100] Classification of clusters was performed according to the authors' parameters; the distribution of PTX3 expression was obtained after imputation with the “Rmagic” package.sup.33.
Study Design and Participants
[0101] This cohort study analyzed a cohort of 96 patients. Inventors included all males and non-pregnant females, 18 years of age or older, admitted to Humanitas Clinical and Research Center (Rozzano, Milan, Italy) between Mar. 4 and May 16, 2020 (data cutoff on May 13rd) with a laboratory-confirmed diagnosis of COVID-19. Hospital admission criteria were based on a positive assay for SARS-CoV-2 associated with respiratory failure requiring oxygen therapy, or radiological evidence of significant pulmonary infiltrates on chest computed tomography (CT) scan, or reduction in respiratory/cardiopulmonary reserve as assessed by 6 minutes walking test, or due to frailty related with patient comorbidity. Inventors assessed an outcome of death. 52 patients of 96 (54%) were transferred to ICU because requiring invasive ventilation or non-invasive mechanical ventilation with oxygen fraction over 60%. Patients with continuous positive airway pressure therapy (CPAP) were followed up by ICU outreach team and ward physicians in COVID-19 wards. Acute respiratory syndrome (ARDS) was defined according to the Berlin definition.sup.34.
Laboratory Test, Demographic, and Medical History
[0102] Laboratory testing at hospital admission included: complete blood count, renal and liver function (transaminase, total/direct/indirect bilirubin, gamma-glutamyl transferase, alkaline phosphatase), creatinine kinase, lactate dehydrogenase, myocardial enzymes, electrolytes and triglycerides. A panel of acute phase reactants including interleukin-6 (IL-6), serum ferritin, D-dimer, C-reactive protein (CRP), fibrinogen, and procalcitonin (PCT) was performed. Body temperature, blood pressure, heart rate, peripheral saturation, and respiratory rate were measured in all patients. Chest CT scan and arterial blood gas analysis were performed in the emergency department. In all patients PTX3 was measured within the first few days after the admission date (mean 2.1+1.6 days). Pneumococcal and Legionella urinary antigen tests were routinely performed. Nasopharyngeal swab for influenza A, B and H1N1 was also routinely performed to exclude co-infections. Additional microbiological tests were performed to exclude other pathogens as possible etiological agents when suggested by clinical conditions (bacterial cultures of sputum, blood and urine). Inventors obtained a comprehensive present and past medical history from patients. Positivity was assessed on the basis of reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assay for SARS-CoV-2 on a respiratory tract sample tested by our laboratory, in accordance with the protocol established by the WHO (https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/laboratory-guidance). Due to the high false negative rate of RT-PCR from pharyngeal swab, two different swabs were performed in every patient to increase the detection rate.sup.35. In cases of negative assay in throat-swab specimens, but with suggestive clinical manifestations, presence of contact history or suggestive radiological evidence for COVID-19, the detection was performed on bronchoalveolar lavage fluid (BAL) or endotracheal aspirate, which has higher diagnostic accuracy. All demographics, medical history and laboratory tests were extracted from electronic medical records and were checked by a team of three expert physicians. The study was approved by the local Ethical Committee (authorization 233/20), and the requirement for informed consent was waived.
Sample Collection and PTX3 Measurement
[0103] Venous blood samples were collected during the first 5 days after hospital admission (mean 2.1±standard deviation 1.6 days), centrifuged, and EDTA plasma was stored at −80° C. until use. PTX3 plasma levels were measured, as previously described.sup.18, by a sandwich ELISA (detection limit 0.1 ng/mL, inter-assay variability from 8 to 10%) developed in-house, by personnel blind to patients' characteristics. In each analytical session a sample obtained from a pool of plasma from healthy donors was used as internal control. The mean PTX3 concentration measured in this sample was 1.88±0.6 ng/mL.
Statistical Methods
[0104] Demographic, clinical, laboratory, and outcome data were obtained from electronic medical records and patient chart notes using a standardized data collection form. Descriptive statistics included means with standard deviations (SD) and medians with interquartile ranges (IQR) for continuous variables, and frequency analyses (percentages) for categorical variables. Linearity of continuous variables was checked by comparing models with the linear term to the model with restricted cubic splines. The optimal cut-off levels of PTX3 for predicting 28-day outcome of death have been investigated using receiver operating characteristic (ROC) curves. The correlation between variables and sequential organ failure assessment (SOFA) score was evaluated by Spearman's rank correlation coefficient (rho). To identify the association between PTX3 levels and the outcome in hospitalized COVID-19 patients, inventors used time-to-event (survival) methods for censored observations. The composite study endpoint was “death” within 28 days from hospital admission. Time to event was defined as the time from hospital admission until the date of event or censoring. Patients discharged early and alive from the hospital, and without having experienced ICU transfer, were considered event-free through day 28.sup.36. May 13, 2020 was considered as data cutoff. Kaplan-Meier estimates were used to draw the cumulative incidence curves, compared by log-rank tests. Furthermore, multivariable Cox proportional hazards (PH) models of prognostic factors were used. The analyses were based on non-missing data (missing data not imputed). Confounders were selected according to a review of the literature, statistical relevance, and consensus opinion by an expert group of physicians and methodologists. After fitting the model, the PH assumption was examined on the basis of Schoenfeld residuals. The hazard ratios (HR) were presented with their 95% confidence intervals (CI) and the respective p-values. A ratio higher than 1.0 implies a higher probability of event compared to the reference group.
Results
PTX3 Expression is Induced in COVID-19
[0105] Inventors conducted an in silico bioinformatic analysis of the expression of PTX3 using public databases. As shown in
Prognostic Significance of PTX3
[0106] Demographic, clinical, and laboratory features of patients were shown in Table 1. All variables were also stratified by clinical outcome death versus alive (Table 1).
[0107] In the overall population, patients had elevated PTX3 (mean 28.7±29.8 ng/mL; median 17.3 ng/mL, IQT 10.0-39.8 ng/mL), CRP (mean 14.2±10.3 mg/dL; median 12.29 mg/dL, IQT 6.22-22.0 mg/dL), and IL-6 levels (median 48 pg/ml; IQT 21-115 pg/ml). Similarly to our previous study.sup.38, inventors also observed elevated levels of ferritin (mean 634±608 μg/mL; median 536 μg/mL, IQT 151-822 μg/mL), D-dimer (mean 2404±7448 μg/mL; median 741 μg/mL, IQT 476-1456 μg/mL), and LDH (mean 371±168 U/L; median 345 U/L, IQT 268-424 U/L). A reduction in lymphocytes (mean 0.86±0.43×10.sup.3/mL; median 0.8×10.sup.3/mL, IQT 0.55-1.1×10.sup.3/mL) and eosinophils (mean 0.06±0.111×10.sup.3/mL; median 0×10.sup.3/mL, IQT 0-0.1×10.sup.3/mL) were also found, as previously reported.sup.38.
[0108] In our cohort, 52 patients (54.2%) were transferred to the ICU within 7 days from admission because of clinical worsening. The primary endpoint of death occurred in 22 patients (23%), comprised of 14 patients who died in ICU and 8 in medical wards, while a total of 58 patients (60.4%) had been discharged and 16 (16.6%) were still hospitalized. PTX3 values were higher in dead patients compared to survivors (mean 52.3±41.8 ng/mL; median 39.8 ng/mL, IQT 20.2-75.7 ng/mL versus mean 21.0±20.7 ng/mL; median 15.3 ng/mL, IQT 8.2-21.3 ng/mL, respectively; Table 1 and
[0109] The best AUC value for the prediction of death at 28 days was calculated for PTX3 (0.75, 95% CI 0.64-0.86). The most valid cut-off level to predict 28-day mortality was PTX3=22.25 ng/mL (sensitivity 0.73; specificity 0.78;
[0110] Correlations between PTX3 and other inflammatory markers were initially assessed with Spearman test and were reported in Table 6. PTX3 was significantly correlated to CRP, procalcitonin, IL-6, ferritin, and D-dimer, but also with other COVID-19 poor prognostic factors, such as LDH, troponin-I, lymphocyte count (Table 1s). PTX3, CRP, ferritin, IL-6, and D-dimer were also analyzed as continuous values for predicting mortality (univariate Cox regression; Table 3). PTX3 was confirmed significantly associated to mortality; moreover, IL-6 and D-dimer were also significantly, but weakly, associated with mortality at the unadjusted analysis. Performing a multivariate logistic regression including all these markers (PTX3, ferritin, D-Dimer, IL-6, and CRP), PTX3 was the only inflammatory marker significantly associated with death (adjusted HR for 1 ng/mL increase, 1.13; 95% CI, 1.02 to 1.24; p=0.021; Table 4).
[0111] To investigate if PTX3 could be also considered a biomarker of COVID-19 severity at the time of hospital admission, identified by basal SOFA score ≥3, inventors performed a multivariate logistic regression including PTX3, CRP, IL-6, and D-dimer. CRP was the only factor significantly associated to SOFA score ≥3 (Table 5).
DISCUSSION
[0112] As of May 25 2020 the case fatality rate of COVID-19 in Italy is reported to be 14.3% with an average ICU admission rate of more than 20.4%.sup.39. Elevated levels of CRP, cytokines, and chemokines.sup.8.38,40 together with low lymphocyte and eosinophil counts characterize patients with severe disease.sup.41. However, a reliable biomarker of poor outcome in COVID-19 is still lacking. A delayed identification of severe cases might lead to delayed intensive care treatment and increased mortality. On the contrary, the early and accurate triaging of the patients may contribute to a timely and rationale planning of ICU admissions. The present study was designed to investigate expression and clinical significance of the fluid phase pattern recognition molecule PTX3 in COVID-19. Inventors found that PTX3 was induced by SARS-CoV-2 in respiratory tract epithelial cells. In COVID-19 patients, PTX3 analyzed at single cell level was selectively expressed by monocytes among circulating cells and by lung macrophages. High PTX3 plasma levels (≥22.25 ng/mL) were a strong independent indicator of short term 28-day mortality with an adjusted Hazard Ratio of 7.8 (95% CI 2.5-24). In this patient cohort, PTX3 fared definitely better than other known prognostic markers including CRP, IL-6, ferritin and D-dimer.
[0113] PTX3 serum levels above the normal value (<2 ng/mL.sup.42) can be found in the subclinical inflammatory status of cardiovascular disease 43 as well as in infections and sepsis, with increasing median values when moving to more severe conditions. In a large study conducted in 1326 unselected hospitalized subjects (14% with infectious diseases), PTX3 above 95th percentile of healthy non-hospitalized subjects (>6.4 ng/mL) was significantly associated to higher mortality in the short term, independently of hospitalization causes (adjusted HR 5, 95%CI 2.9-8.8).sup.44. Elevated PTX3 serum levels are indeed not related to a specific diagnosis rather predict severe cases or poor prognosis in different contexts characterized by a systemic inflammatory response.sup.44. In a recent, prospective, observational study including 547 ICU patients (42.4% with infections), a PTX3 cut off similar to that identified in our study was reported to predict mortality: PTX3 serum level above the median cohort value of 20.9 ng/mL was independently associated to 28-day mortality when adjusted for age, sex, chronic diseases, and immunosuppression (HR 1.87, 95% CI 1.41-2.48).sup.45. In another recent paper conducted on 281 sepsis patients, serum PTX3>26 ng/mL was associated to mortality.sup.46. Taken together, these findings and our results suggest that circulating PTX3 levels ten-fold above the normal value reflect a severe systemic inflammatory involvement with ominous outcome.
[0114] PTX3 has been shown to be produced by diverse cell types including myelomonocytic cells, lung epithelial cells and endothelial cells. In the present study, inventors found that SARS-CoV-2 induced gene expression of PTX3 in respiratory tract epithelial cells. Peripheral blood mononuclear cells represent the easily accessible cellular source in patients. By bioinformatic analysis at single cell level, inventors found that PTX3 was selectively expressed by monocytes among circulating leukocytes. Moreover, in lung bronchoalveolar lavage fluid, single cell analysis revealed selective expression of PTX3 in neutrophils and macrophages, which play a major role in the pathogenesis of the disease.sup.3,4.
[0115] The PTX3 gene was originally cloned in endothelial cells.sup.11 and vascular cells are a major source of this component of humoral innate immunity, though their role could not be directly ascertained in the present study. Endothelial cells and the lung vascular bed have emerged as major determinant of COVID-19-associated microvascular thrombosis and disease pathogenesis.sup.9. PTX3 plasma levels have been shown to correlate with severity of disease in various forms of vascular pathology including small vessel vasculitis, coronary heart disease, and Kawasaki disease.sup.27,43,47. The latter observation raises the issue of its significance in the Kawasaki-like disease observed in children after COVID-19 (e.g..sup.48,49). Of interest, our data show a significant correlation between PTX3 and D-dimer, surrogate of coagulation cascade activation and marker of venous thrombosis, and between PTX3 and troponin-I, marker of myocardial disease: both myocardial inflammation and acute ischemic heart disease have been described in COVID-19.sup.50,51. These observations raise the possibility that the strong prognostic significance of PTX3 in COVID-19 may reflect its positioning at the very intersection between macrophage-driven inflammation and vascular involvement.
[0116] In conclusion, the results presented here suggest that high PTX3 plasma levels (≥22 ng/mL) are strongly associated with unfavorable COVID-19 disease progression, defined as 28-day mortality, and may serve as a useful prognostic biomarker to decide intensity of care based on the predicted individual risk of death. PTX3 fared better than other classic biomarkers including CRP and IL-6. Given the relatively small sample size (96 patients) this finding should be interpreted with caution. With this caveat, it is tempting to speculate that PTX3 plasma levels may better reflect local tissue disruptive inflammation including the involvement of myelomonocytic cells and the vascular bed. The significance of PTX3 as a biomarker in COVID-19 patient management and stratification and its role in the virus-host interaction deserve further studies.
TABLES
[0117]
TABLE-US-00001 TABLE 1 Demographics, laboratory and clinical characteristics of COVID-19 hospitalized patients grouped by different outcome. Patients Alive patients Death patients Variables (n = 96) (n = 74) (n = 22) Demographic Characteristics Mean ± SD; Median (IQR) or n (%) Age (years)* 65.2 ± 15.2; 62.5 ± 15.1; 74.6 ± 11.3; 65.1 (56-74.5) 61 (51-73) 73 (69-83) Gender Female 31 (33) 25 (34) 6 (27) Male 65 (67) 49 (66) 16 (73) Laboratory Characteristics Blood Biochemistry White blood cells (×10.sup.9/L; 9.2 ± 5.6; 9.3 ± 6; 8.9 ± 4.1; normal range 4.0-10.0) 7.7 (5.9-10.4) 7.5 (5.8-10.9) 8 (6.6-9.7) Neutrophils (×10.sup.9/L; 7.7 ± 5.4; 7.7 ± 5.8; 7.6 ± 4; normal range 2.0-7.0) 6.2 (4.4-8.9) 6 (4.3-9) 7 (5-8.4) Lymphocytes (×10.sup.9/L; 0.9 ± 0.4; 0.9 ± 0.4; 0.7 ± 0.4; normal range 1.0-4.0)* 0.8 (0.6-1.1) 0.9 (0.6-1.2) 0.6 (0.4-0.8) Eosinophils (×10.sup.9/L; 0.1 ± 0.1; 0.1 ± 0.1; 0.1 ± 0.1; normal range 1.0-5.0) 0.0 (0.0-0.1) 0.0 (0.0-0.1) 0.1 (0.0-0.2) Hemoglobin (g/dL; 12.5 ± 1.9; 12.5 ± 1.9; 12.5 ± 1.8; normal range 13.0-16.0) 12.6 (11.3-13.7) 12.7 (11.3-13.6) 12.4 (11.6-13.9) Platelets (×10.sup.9/L; 253 ± 105; 271 ± 100; 195 ± 100; normal range 150-400)** 238 (172-313) 247 (198-332) 156 (100-237) Alanine aminotransferase 38 ± 37; 39 ± 38; 33 ± 21; (U/L; normal range <51) 26 (20-42) 26 (20-43) 28 (19.5-39) Aspartate aminotransferase 50 ± 56.5; 49 ± 60; 56 ± 38; (U/L; normal range <51) 32 (22-49) 32 (21-42) 51 (32.5-60.5) Gamma-glutamyl 52 ± 57; 48 ± 38; 70 ± 122; tanspeptidase (U/L; 28 (18-74) 32 (18-76) 27 (18-41) normal range <55) Alkaline phosphatase (U/L; 108 ± 54; 105 ± 55; 122 ± 50; normal range 40-150) 90 (75-124) 89 (74-111) 121 (78-184) Total bilirubin (mg/dL; 0.9 ± 0.6; 0.9 ± 0.6; 1.1 ± 0.6; normal range 0.3-1.2) 0.8 (0.6-1.2) 0.7 (0.5-1.2) 0.9 (0.7-1.3) Direct bilirubin (mg/dL; 0.2 ± 0.2; 0.2 ± 0.1; 0.4 ± 0.3; normal range <0.3)** 0.2 (0.1-0.3) 0.2 (0.1-0.2) 0.4 (0.2-0.5) Indirect bilirubin (mg/dL; 0.6 ± 0.2; 0.5 ± 0.2; 0.9 ± 0.3; normal range 0.05-1.10)** 0.6 (0.4-0.7) 0.5 (0.4-0.6) 0.8 (0.7-0.9) Creatine kinase (U/L; 183 ± 222; 151 ± 177; 348 ± 347; normal range <172) 105 (58-184) 98 (53-180) 150 (92-651) Lactate dehydrogenase (U/L; 372 ± 169; 338 ± 146; 483 + 196; normal range <248)* 345 (268-424) [n = 61] 326 (240-382) [n = 47] 412 (380-505) [n = 14] Serum creatinine (mg/dL; 1.2 ± 1.2; 1.2 ± 1.3; 1.2 ± 0.7; normal range 0.67-1.17) 0.9 (0.7-1.2) 1.0 (0.7-1.7) 0.9 (0.8-1.5) Troponin-I (ng/L; 219.5 ± 112.6; 25.2 ± 34.2; 1215.4 ± 2669.6; normal range 1-35)** 12.3 (6.3-46.6) [n = 49] 11.1 (5.7-26.9) [n = 41] 93.9 (35.6-865.5) [n = 8] D-dimer (μg/mL; 2136 ± 6583; 1521 ± 2668; 5083 ± 14795; normal range 0.2-0.35) 637 (409-1394) [n = 87] 625 (366-1376) [n = 72] 720 (509-2735) [n = 15] Fibrinogen (mg/dL; 507 ± 169; 516 ± 158; 462 ± 225.6; normal range 160-400) 488 (410-586) 481 (410-586) 510 (344-558) Triglycerides (mg/dL; 150 ± 78; 141 ± 60; 198 ± 136; normal range 10-150) 134 (105-171) 133 (107-161) 164 (89-279) Infection-Related Biomarkers Pentraxin 3 (ng/mL)** 28.7 ± 29.8; 21 ± 20.7; 52.3 ± 41.8; 17.3 (10-39.8) 15.3 (8.2-21.3) 39.8 (20.2-75.7) Interleukin-6 (pg/mL; 115 ± 247; 85 ± 141; 303 ± 564; normal range <6.4) 56 (21-115) [n = 51] 41 (19-103) [n = 44] 82 (42-228) [n = 7] C-reactive protein (mg/dL; 14.2 ± 10.4; 13.3 ± 10; 17.7 ± 11.2; normal range <1.0) 12.3 (6.2-22) 12.2 (4.8-20.2) 15 (6.6-26.4) Procalcitonin (ng/mL; 1.7 ± 7; 1.9 ± 7.9; 1.1 ± 1.6; normal range 0.05-0.5) 0.4 (0.1-1.4) [n = 91] 0.3 (0.1-1.4) [n = 70] 0.6 (0.4-0.8) [n = 21] Ferritin (ng/mL; 760 ± 721; 693 ± 675; 1133 ± 883; normal range 23.9-336.2) 622 (185-976) [n = 66] 567 (173-881) [n = 56] 867 (522-1624) [n = 10] Severity-Related Biomarkers Respiratory rate (breaths 19 ± 3; 18 ± 2; 21 ± 5; per minute) 18 (18-19.5) 18 (17-19) 19 (18-20) Ratio of PaO2 to FiO2* 259 ± 140; 277 ± 144; 201 ± 110; 200 (149-384) [n = 88] 230 (160-393) [n = 67] 142 (130-233) [n = 21] Pulse (beats per minute) 86 ± 15; 84 ± 15; 96 ± 13; 85 (76-95) 84 (72-90) 95 (90-101)* Mean pressure (mmHg) 86 ± 14; 87 ± 15; 83 ± 10; 86 (74-94) [n = 92] 87 (72-111) [n = 70] 84 (74-89) Temperature (° C.) 36.9 ± 0.8; 36.9 ± 0.8; 36.8 ± 0.9; 36.5 (36.2-37.5) 36.7 (36.2-37.5) 36.4 (36.2-38) SOFA* 4 ± 3; 3.5 ± 2.8; 5.6 ± 2.7; 4 (1-6) 3 (1-6) 6 (4-7) Comorbidities Hypertension 50 (52) 39 (53) 11 (50) Chronic heart diseases 14 (15) 11 (15) 3 (14) Atrial fibrillation 12 (13) 10 (14) 2 (9) Diabetes type 2 29 (30) 22 (30) 7 (32) Obesity (BMI) 28.3 ± 7.7; 28.3 ± 8.3; 27.8 ± 4.0; 26 (23-31) [n = 80] 26 (23-33) [n = 66] 27.7 (24-29) [n = 14] Chronic obstructive 9 (9) 7 (9) 2 (9) pulmonary disease Chronic Kidney disease 8 (8) 6 (8) 2 (9) History of neoplasia 14 (15) 14 (19) 0 (0) Stroke 9 (9) 5 (7) 4 (18) Mann-Whitney test between death and alive patients: *p < 0.05; **p < 0.001
TABLE-US-00002 TABLE 2 PTX3 as predictor of death in Hospitalized COVID-19 patients (multivariate Cox model, HR adjusted for confounders: age, stay in ICU, and SOFA score at admission). Variables HR Std. Error 95% CI p-Value PTX3 (≥22.25 ng/mL) 7.8 4.47 (2.54-24.03) <0.0001 Age 1.09 0.036 (1.03-1.17) 0.005 ICU 1.76 1.43 (0.36-8.61) 0.485 SOFA 1.22 0.12 (1.01-1.47) 0.036
TABLE-US-00003 TABLE 3 Inflammatory and other biomarkers as predictors of death in Hospitalized COVID-19 patients (univariate Cox models). Variables HR Std. Error 95% CI p-Value PTX3 1.031 0.006 (1.019-1.042) <0.001 C-reactive protein 1.036 0.021 (0.995-1.078) 0.081 Interleukin-6 1.001 0.001 (1.001-1.003) 0.01 Procalcitonin 0.979 0.056 (0.875-1.096) 0.72 Ferritin 1.0005 0.0003 (0.999-1.001) 0.09 D-dimer 1.0001 0.00003 (1.00003-1.0002) 0.005
TABLE-US-00004 TABLE 4 Correlations between inflammatory and other biomarkers levels (at admission) and outcome (death) in Hospitalized COVID-19 patients (multivariate logistic regression). Variables OR Std. Error 95% CI p-Value PTX3 1.13 0.06 (1.02-1.24) 0.021 C-reactive protein 0.90 0.11 (0.70-1.14) 0.38 Interleukin 6 1.0004 0.0032 (0.9941-1.0066) 0.908 Ferritin 1.0004 0.0010 (0.9983-1.0024) 0.709 D-Dimer 0.999 0.001 (0.998-1.001) 0.912
TABLE-US-00005 TABLE 5 Correlations between inflammatory and other biomarkers levels and systemic organ failure (SOFA score ≥3) at admission in Hospitalized COVID-19 patients (multivariate logistic regression). Variables OR Std. Error 95% CI p-Value PTX3 1.01 0.0128 (0.98-1.03) 0.508 C-reactive protein 1.14 0.0481 (1.05-1.23) 0.003 Interleukin 6 1.00 0.0009 (0.998-1.002) 0.927 D-Dimer 1.00 0.0003 (1.0-1.001) 0.218
TABLE-US-00006 TABLE 6 Spearman correlation between PTX3 and other inflammatory or biochemical markers. Variables ρ p-Value Neutrophils 0.0929 0.368 Lymphocytes −0.3048 0.002 C-reactive protein 0.3156 0.002 Procalcitonin 0.2841 0.006 Interleukin-6 0.5353 <0.001 Ferritin 0.4604 <0.001 D-dimer 0.2945 0.017 Lactate dehydrogenase 0.5443 <0.001 Platelets 0.0297 0.808 Troponin-I 0.5552 <0.001
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