Immunological sub-phenotypes and response to Convalescent Plasma in COVID-19 induced ARDS: a secondary analysis of the CONFIDENT trial.

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Benoit Misset, Anh Nguyet Diep, Axelle Bertrand, Michael Piagnerelli, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3793271/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Convalescent Plasma (CP) reduced the mortality in COVID-19 induced ARDS (C-ARDS) patients treated in the CONFIDENT trial. As patients are immunologically heterogeneous, we hypothesized that clusters may differ in their treatment responses to CP. Methods We measured 20 cytokines, chemokines and cell adhesion markers using a multiplex technique at the time of inclusion in the CONFIDENT trial in patients of centers having accepted to participate in this secondary study. We performed descriptive statistics, unsupervised hierarchical cluster analysis, and examined the association between the clusters and CP effect on day-28 mortality. Results Of the 475 patients included in CONFIDENT, 391 (82%) were sampled, and 196/391 (50.1%) had been assigned to CP. We identified four sub-phenotypes representing 89 (22.8%), 178 (45.5%), 38 (9.7%), and 86 (22.0%) patients. The most contributing biomarkers in the principal component analysis were IL-1β, IL-12p70, IL-6, IFN-α, IL-17A, IFN-γ, IL-13, TFN-α, total IgG, and CXCL10. Sub-phenotype-1 displayed a lower immune response, sub-phenotype-2 a higher adaptive response, subphenotype-3 the highest innate antiviral, pro and anti-inflammatory response, and adhesion molecule activation, and sub-phenotype-4 a higher pro and anti-inflammatory response, migration protein and adhesion molecule activation. Sub-phenotype-2 and sub-phenotype-4 had higher severity at the time of inclusion. The effect of CP treatment on mortality appeared higher than standard care in each sub-phenotype, without heterogeneity between sub-phenotypes (p = 0.97). Conclusion In patients with C-ARDS, we identified 4 sub-phenotypes based on their immune response. These sub-phenotypes were associated with different clinical profiles. The response to CP was similar across the 4 sub-phenotypes. COVID-19 convalescent plasma ARDS immune response phenotypes Figures Figure 1 Figure 2 Figure 3 Take-home message In the cohort of participants in the CONFIDENT COVID-19 Convalescent Plasma trial in patients with COVID-19-induced ARDS, patient groups determined by their immune profile did not respond differently to the study treatment. Immune profile should not be a selection criterion for the use of convalescent plasma in ARDS due to COVID-19. Introduction Acute respiratory distress syndrome (ARDS) was a prominent feature during the first three years of the COVID-19 pandemic, leading to hospital saturation all over the world [ 1 ]. Among therapies targeting the response against SARS-CoV-2 in these patients, low-dose steroids for 10 days was the first accepted therapy [ 2 ]. Passive immunization with plasma collected in COVID-19 convalescents was inconclusive at various stages of the disease [ 3 , 4 ]. In patients admitted to the Intensive Care Unit (ICU) for SARS-CoV-2 induced pneumonia, the REMAP-CAP trialists observed a lower likelihood of providing improvement in organ support-free days [ 5 ]. In a secondary analysis, unsupervised analysis based on cytokines, chemokines and endothelial biomarkers, allowed to individualize sub-phenotypes with different response to convalescent plasma (CP) [ 6 ]. As severe COVID-19 has been considered as a particular form of sepsis [ 7 ] and/or ARDS [ 8 ], future trials should implement some form of predictive enrichment to increase the likelihood for beneficial effects of an intervention to emerge [ 9 ]. In the CONFIDENT trial of CP, we observed that patients with COVID-19 induced ARDS in the first days of invasive mechanical ventilation (IMV) experienced a significant reduction of mortality at 28 days [ 10 ]. By comparison to prior trials, we attributed this positive result to a greater homogeneity of the study population and to the high neutralizing activity of the CP we administered. In the present study, as the individual immune response is likely heterogeneous, we hypothesized that the response to CP could differ in sub-groups determined by their immune profile. We tested this hypothesis as a secondary analysis in the patients of the CONFIDENT trial whose blood samples had been centralized for this purpose. The biomarkers we assessed were based on a multiplex test targeting 20 proteins involved in the inflammatory response. These proteins had all been described to be part of the immune response commonly observed in severe COVID-19 [ 11 – 18 ]. Methods Study design The present study is a secondary analysis of CONFIDENT and is labelled CONFIDENT-II. CONFIDENT was a publicly funded Belgian multicenter randomized open-label trial. The trial was designed to determine the effect on mortality at day-28 of CP with a neutralizing titer against SARS-CoV-2 at least 1/160 versus standard care (SC) in patients with C-ARDS requiring invasive mechanical ventilation (IMV) for less than five days during the pandemic. The randomization process included a stratification based on the prior duration of IMV ( 48 hours). The positive effect on mortality was mainly observed in the patients who underwent randomization 48 hours or less after IMV initiation. Study population The CONFIDENT trial involved adult patients with a Clinical Frailty Scale < 6 [ 19 ], admitted to a participating ICU with a diagnosis of C-ARDS and submitted to IMV for a maximum of five days (WHO 10-point progression scale 7 to 9 [ 9 ]). ARDS was classified according to the Berlin definition [ 10 ]. C-ARDS was defined by an extended pneumonia on a CT scan or a chest X-ray within 10 days, and a positive result of SARS-CoV-2 nasopharyngeal PCR (NP-PCR) test within 15 days prior to inclusion. Exclusion criteria were pregnancy, prior episode of transfusion-related side effect, medical decision to limit therapy, and participation in another COVID-19 trial. The trial involved 475 patients between September 10, 2020, and March 9, 2022, and showed a 9.6% crude reduction in mortality at day 28 (p = 0.03). The CONFIDENT-II population involved the patients who were included in those centers and having accepted to centralize blood samples for secondary analyses. Out of the trial population, plasma total IgG antibodies against SARS-CoV-2 and biomarkers were collected after patients’ informed consent and before randomization to CP or standard care (SC). The trial was registered at clinicaltrials.gov as NCT04558476 and approved by the institutional review boards of all centers. Biomarkers The measurements were performed at the Laboratory Medicine Department of the CHU de Liège, Belgium. Total IgG antibodies against SARS-CoV-2 were assessed with the LIAISON® SARS-CoV-2 TrimericS IgG chemiluminescent kit (Diasorin, Saluggia, Italy). Results are expressed as Binding Antibody Units per mL (BAU/mL). A value > 33.7 BAU/mL is considered positive (manufacturer). Values of 535, 606, 860 and 1335 BAU/mL are considered predictive of 20, 40, 160 and 320 neutralizing antibody titers with 50% plaque reduction (PRNT50), respectively [ 22 ] The balance between pro- and anti-inflammatory mediators was assessed using Luminex xMAP technology with the Human Inflammation ProcartaPlex™ Panel (20-Plex, Thermo Fisher Scientific, MA). The biomarkers assessed were thirteen cytokines (GM-CSF, IFN-α, IFN-γ, IL-1α, IL-1β, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, IL-17A (CTLA-8), and TNF-α), four chemokines (IP-10 (CXCL10), MCP-1 (CCL2), MIP-1α (CCL3), and MIP-1β (CCL4)), and three cell adhesion molecules (ICAM-1, CD62E (E-selectin), and CD62P (P-Selectin)). All the samples were measured in duplicate and the coefficients of variation were all < 15%. The results were provided as quantitative values in pg/mL [ 13 ]. For biomarkers whose value was below the lower limit of quantification (LLoQ), we attributed a value of LLoQ. For biomarkers whose value was over the upper limit of quantification (ULoQ), we attributed the ULoQ value. Blood CRP and platelets were included among the biomarkers as indicators of both inflammation and coagulation activation [ 24 ]. Statistics Variables are provided as mean (SD) or median (IQR) or counts (percentages). The homogeneity between the CONFIDENT and CONFIDENT-II populations was estimated on baseline characteristics and the primary endpoint (day-28 mortality). Comparisons were made with the Chi-square Fisher’s exact, and t-test or and Mann-Whitney tests if assumptions regarding the two tests were not satisfied. The effect on the principal endpoint was measured with the Odds ratio (OR). Cluster analysis Agglomerative hierarchical cluster analysis was applied to examine the number of clusters of participants in the included cohort explained by the biomarkers. Wald.D2 linkage function was employed to investigate the variance of the clusters on the log10-transformed biomarker data and the number of clusters was based on the CH index (Calinski and Harabasz Index) [ 25 ] as the cluster stopping rule. Principal component analysis (PCA) was performed to determine the most contributing biomarkers. Based on descriptive statistics, we normalized the protein biomarkers to the median of the subphenotype with the lowest median values for most biomarkers, namely subphenotype-1, and presented with boxplots of log2 fold-change. Association between clusters and treatment effect We used Kruskal Wallis, chi-square and/or Fisher’s exact tests to examine the differences among the identified clusters with regards to clinical characteristics. We examined differences in the CP treatment effect as to D-28 mortality within each cluster and as a whole cohort. A test of heterogeneity of the ORs was performed and presented with the Q-statistic. The results were visualized by means of a forest plot, displaying the ORs and the respective 95% confidence interval (CI). The analyzes and figures were carried out with the R statistical software, version 4.2.2 (R Core Team, 2021). Results Study cohort Among the 475 participants in the CONFIDENT trial [ 10 ], 237 patients were randomly assigned to CP and 238 to SC. The CONFIDENT-II cohort involved 391/475 (82%) patients. Of these, 196 (50.1%) had been assigned to CP and 195 (49.9%) to SC with a median age was 64 [IQR: 56–72] years. 66.8% (n = 261) were males. Most participants had blood group A (n = 180, 46.0%) and O (n = 158, 40.4%), their median APACHE II score was 13 [IQR: 9–17], with a P/F ratio 116 [89–174]. In this secondary analysis, 375 (95.9%) patients were receiving steroids; and 299 (76.5%) were included within 48 hours of IMV. 149 (38.1%) patients died before day-28. The characteristics of the CONFIDENT-II patients were similar to those in the CONFIDENT cohort except for a lower PaO2/FiO2 ratio and a shorter delay from ICU admission to inclusion in the study cohort. The characteristics of the CP and SC groups within the CONFIDENT-II population are similar (Table 1 ). Table 1 Characteristics of the study population. CONFIDENT-II population Population not included CONFIDENT population Convalescent Standard Overall Convalescent Standard Overall Total p-value (n = 196) (n = 195) (n = 391) (n = 41) (n = 43) (n = 84) (n = 475) Age, years 64 [56–72] 65 [56–71] 64 [56–72] 64 [52–70] 63 [56–72] 63.5 [55–70] 64 [56–72] 0.38 Male sex, n 130 (66.3) 131 (67.2) 261 (66.8) 28 (68.3) 34 (79.1) 62 (73.8) 323 (68.0) 0.26 IMV < 48h at inclusion, n 150 (76.5) 149 (76.4) 299 (76.5) 21 (51.2) 22 (51.2) 43 (51.2) 342 (72.0) < 0.001 BMI, kg/m² 30.5 [26.4–34.8] 29.9 [26.5–34.2] 30.4 [26.4–34.5] 30.4 [27.2–34.5] 29.4 [26.4–37.1] 29.7 [27.0–35.9] 30.2 [26.5–34.6] 0.73 Blood group, n 0.95 A 87 (44.6) 93 (47.4) 180 (46.0) 23 (56.1) 16 (37.2) 39 (46.4) 219 (46.1) AB 8 (4.1) 6 (3.1) 14 (3.6) 1 (2.4) 1 (2.3) 2 (2.4) 16 (3.4) B 19 (9.7) 20 (10.2) 39 (10.0) 4 (9.8) 5 (11.6) 9 (10.7) 48 (10.1) O 81 (41.5) 77 (39.3) 158 (40.4) 13 (31.7) 21 (48.8) 34 (40.5) 192 (40.4) NP PCR test for SARS-Cov-2, Ct 21 [ 18 – 26 ] 20 [ 17 – 25 ] 21 [ 18 – 26 ] 23 [ 19 – 27 ] 23 [ 16 – 27 ] 23 [ 19 – 27 ] 21 [ 17 – 26 ] 0.41 Time from ICU admission, days 2.6 [1.6–4.6] 3.4 [1.7–5.1] 2.7 [1.6–4.6] 4.4 [2.7–5.8] 4.5 [3.5–5.6] 4.4 [3.4–5.7] 3.4 [1.7–4.7] < 0.001 Severity at ICU admission APACHE II score, points 13 [ 9 – 18 ] 13 [9.25–17] 13 [ 9 – 17 ] 14 [ 9 – 17 ] 12 [ 8 – 15 ] 12 [8.25–16] 13 [ 9 – 17 ] 0.38 SOFA, points 6 [ 4 – 8 ] 6 [ 4 – 8 ] 6 [ 4 – 8 ] 6 [ 5 – 8 ] 6 [ 4 – 8 ] 6 [ 4 – 8 ] 6 [ 4 – 8 ] 0.96 PEEP level, mmHg 10 [ 10 – 12 ] 10 [ 10 – 12 ] 10 [ 10 – 12 ] 10 [10–13.25] 12 [ 8 – 13 ] 10 [9.9–13] 10 [ 10 – 12 ] 0.59 PaO2/FiO2, mmHg 116 [89–154] 124 [91–155] 116 [89–154] 129 [99–174] 145 [106–172] 137 [100–174] 123 [91–160] 0.03 CRP, mg/L 125 [67–189] 115 [65–198] 123 [65–191] 130 [65–214] 101 [43–150] 123 [55–189] 123 [91–160] 0.18 WHO progression scale 8 [ 8 – 8 ] 8 [ 8 – 8 ] 8 [ 8 – 8 ] 8 [ 7 – 8 ] 8 [ 7 – 8 ] 8 [ 7 – 8 ] 8 [ 8 – 8 ] 0.02 Comorbidities, n Hypertension 121 (61.7) 104 (53.3) 225 (57.5) 24 (58.5) 25 (58.1) 49 (58.3) 274 (57.7) 0.90 Congestive heart failure 15 (7.7) 9 (4.6) 24 (6.1) 4 (9.8) 2 (4.7) 6 (7.1) 30 (6.3) 0.88 COPD 23 (11.7) 22 (11.3) 45 (11.5) 4 (9.8) 2 (4.7) 6 (7.1) 51 (10.7) 0.34 Asthma 20 (10.2) 17 (8.7) 37 (9.5) 2 (4.9) 0 (0) 2 (2.4) 39 (8.2) 0.06 Diabetes 68 (34.7) 73 (37.4) 141 (36.1) 13 (31.7) 19 (44.2) 32 (38.1) 173 (36.4) 0.77 Chronic renal failure 26 (13.3) 24 (12.3) 50 (12.8) 6 (14.6) 6 (14.0) 12 (14.3) 62 (13.1) 0.82 Haematological_cancer 4 (2.0) 10 (5.1) 14 (3.6) 2 (4.9) 1 (2.3) 3 (3.6) 17 (3.6) 1.00 Solid tumor 6 (3.1) 12 (6.2) 18 (4.6) 0 (0) 1 (2.3) 1 (1.2) 18 (3.8%) 0.09 Therapy against SARS-Cov-2, n Hydroxychloroquine 1 (0.5) 0 (0) 1 (0.3) 0 (0) 0 (0) 0 (0) 1 (0.2) 1.00 Azythromycin 6 (3.1) 3 (1.5) 9 (2.3) 4 (9.8) 1 (2.3) 5 (6.0) 14 (2.9) 0.15 Remdesivir 8 (4.1) 12 (6.2) 20 (5.1) 5 (12.2) 2 (4.7) 7 (8.3) 27 (5.7) 0.37 Anti-IL-6 or IL-6R 10 (5.1) 5 (2.6) 15 (3.8) 2 (4.9) 2 (4.7) 4 (4.8) 19 (4.0) 0.94 Any steroid 192 (98.0) 191 (97.9) 383 (98.0) 41 (100.0) 42 (97.7) 83 (98.8) 466 (98.1) 1.00 Death at day-28, n 70 (35.7) 79 (40.5) 149 (38.1) 14 (34.1) 28 (65.1) 42 (50.0) 191 [40.2) 0.06 Legend : IMV : invasive mechanical ventilation, BMI : body mass index, NP-PCR: naso-pharyngeal polymerase chain reaction, SARS-CoV-2: severe acute respiratory syndrome coronavirus 2, ICU : intensive care unit, APACHE II : Acute Physiology And Chronic Health Evaluation II, SOFA : sequential organ failure assessment, PEEP : positive end-expiratory pressure, PaO2 : arterial partial pressure of oxygen, FiO2 : fraction of inspired oxygen, CRP : C-reactive protein, WHO : world health organization, COPD : chronic obstructive pulmonary disease, IL-6 : interleukin-6, IL-6R : IL-6 receptor. The frequency of missing samples was less than 1% for all the data presented, except for BMI (6.9%) and for the quantitative value of the SARS-CoV-2 nasopharyngeal PCR (NP-PCR) patients (32.2%) because the routine laboratory of several centers responded a qualitative (yes/no) or semi-quantitative result. We considered that these missing values were completely at random. The frequency of missing samples was less than 1% for all the data presented, except for BMI (6.9%) and for the quantitative value of the SARS-CoV-2 NP-PCR patients (32.2%) because the routine laboratory of several centers responded a qualitative (yes/no) or semi-quantitative result. Therefore, we considered that these missing values were completely at random. Clusters Hierarchical clustering determined 4 biomarker signatures and 4 sub-phenotypes of patients that could be distinguished by their biomarker and clinical profiles (Figs. 1 and 2). The comparative blood levels of each biomarker in the sub-phenotypes are provided in Online Resource 1. Biomarker signatures were labelled A, B, C and D. Signature A mainly gathered cell adhesion markers (E-selectin, P-selectin, ICAM-1); signature B adaptive response (total IgG against SARS-CoV-2) and migration markers (CCL2, CXCL10, CCL4), signature C pro (IL-6, IL-12p70, GM-CSF, TNF-α, IFN-γ) and anti-inflammatory (IL-4) cytokines; and signature D innate cytokines (IFN-α), pro-antiviral response (IL-1α, IL-1β, IFN-α, IL17A), and anti-inflammatory cytokines (IL-10, IL-13). Sub-phenotype-2 consisted of the most patients (n = 178, 45.5%), followed by sub-phenotype-1 (n = 89, 22.8%) and sub-phenotype-4 (n = 86, 22.0%), and sub-phenotype-3 (n = 38, 9.7%) (Table 2 and Fig. 1). The most contributing biomarkers were IL-1β, IL-12p70, IL-6, IFN-α, IL-17A, IFN-γ, IL-13, TFN-α, total IgG, and CXCL10 (Online Resource 2, 3 and 4). Table 2 Clinical characteristics of the sub-phenotypes Subphenotype 1 Subphenotype 2 Subphenotype 3 Subphenotype 4 Total (n = 89) (n = 178) (n = 38) (n = 86) (n = 391) p-value Age, years 65 [56–72] 66 [56–73] 65 [59–72] 63 [57–70] 64 [56–72] 0.60 Male gender, n 64 (72.9) 114 (64.0) 27 (71.1) 56 (65.1) 261 (66.8) 0.56 BMI, kg/m² 30.8 [26.9–34.6] 30.7 [27.4–34.4] 29.2 [26.0–34.6] 28.2 [24.7–34.4] 30.4 [26.4–34.5] 0.08 ABO blood group, n 0.27 A 48 (53.9) 78 (43.8) 17 (44.7) 37 (43.0) 180 (46.0) AB 1 (1.1) 9 (5.1) 0 (0) 4 (4.7) 14 (3.6) B 13 (14.6) 15 (8.4) 3 (7.9) 8 (9.3) 39 (10.0) O 27 (30.3) 76 (42.7) 18 (47.4) 37 (43.0) 158 (40.4) NP PCR test for SARS-Cov-2, Ct 20 [ 18 – 24 ] 22 [ 19 – 28 ] 21 [ 18 – 25 ] 19 [ 16 – 22 ] 20 [18–38] 0.006 Time from ICU admission, days 2.5 [1.6–3.4] 3.6 [2.4–5.4] 3.5 [1.6–5.5] 2.5 [1.5–4.5] 2.7 [1.6–4.6] < 0.001 Severity at inclusion IMV < 48 hours, n 72 (80.9) 118 (66.3) 32 (84.2) 77 (89.5) 299 (76.5) < 0.001 APACHE II score, points 13 [ 9 – 18 ] 12 [ 9 – 16 ] 14 [ 11 – 18 ] 15 [ 12 – 19 ] 13 [ 9 – 17 ] < 0.001 SOFA total, points Total 6 [ 4 – 7 ] 6 [ 4 – 7 ] 7 [ 4 – 8 ] 7 [ 5 – 9 ] 6 [ 4 – 8 ] < 0.001 Respiratory 3 [ 3 – 4 ] 3 [ 3 – 4 ] 4 [ 4 – 4 ] 4 [ 3 – 4 ] 4 [ 3 – 4 ] < 0.001 Coagulation 0 [0–0] 0 [0–0] 0 [0–0] 0 [0–0] 0 [0–0] 0.17 Liver 0 [0–0] 0 [0–0] 0 [0–0] 0 [0–0] 0 [0–0] 0.28 Cardiovascular 1 [0–3] 1 [0–3] 3 [0–3] 3 [ 1 – 4 ] 3 [0–3] 0.001 Central nervous system 0 [0–0] 0 [0–0] 0 [0–0] 0 [0–0] 0 [0–0] 0.35 Renal 0 [0–0] 0 [0–0] 0 [0–0] 0 [0–1] 0 [0–0] 0.12 PaO2/FiO2, mmHg 138 [105–164] 122 [96–159] 96 [83–137] 98 [84–133] 116 [89–154] < 0.001 PEEP level, mmHg 10 [ 9 – 12 ] 10 [ 9 – 12 ] 10 [ 10 – 12 ] 10 [ 10 – 12 ] 10 [ 10 – 12 ] 0.54 WHO progression scale, points 8 [ 8 – 8 ] 8 [ 8 – 8 ] 8 [ 8 – 8 ] 8 [ 8 – 8 ] 8 [ 8 – 8 ] 0.47 Comorbidities, n Hypertension 51 (57.3) 102 (57.3) 26 (68.4) 46 (53.5) 225 (57.5) 0.49 Congestive heart failure 6 (6.7) 7 (3.9) 5 (13.2) 6 (7.0) 24 (6.1) 0.18 Diabetes 33 (37.1) 66 (37.1) 14 (36.8) 28 (32.6) 141 (36.1) 0.89 COPD 14 (15.7) 24 (13.5) 3 (7.9) 4 (4.7) 45 (11.5) 0.08 Asthma 8 (9.0) 18 (10.1) 3 (7.9) 8 (9.3) 37 (9.5) 0.97 Chronic renal failure 12 (13.5) 19 (10.7) 6 (15.8) 13 (15.1) 50 (12.8) 0.69 Hematologic cancer 4 (4.5) 2 (1.1) 0 (0.0) 8 (9.3) 14 (3.6) 0.005 Solid tumor 3 (3.4) 8 (4.5) 1 (2.6) 6 (7.0) 18 (4.6) 0.72 Therapy against SARS-Cov-2, n Hydroxychloroquine 0 (0.0) 0 (0.0) 0 (0.0) 1 (1.2) 1 (0.3) 0.32 Azythromycin 3 (3.4) 6 (3.4) 0 (0.0) 0 (0.0) 9 (2.3) 0.23 Remdesivir 8 (9.0) 7 (3.9) 2 (5.3) 3 (3.5) 20 (5.1) 0.30 Anti-IL-6 or anti-IL-6R 0 (0.0) 6 (3.4) 2 (5.3) 7 (8.1) 15 (3.8) 0.02 Dexamethasone 85 (95.5) 171 (96.1) 36 (94.7) 83 (96.5) 375 (95.9) 0.21 Treatment group and outcome, n Allocated to CP 49 (55.1) 85 (47.8) 20 (52.6) 42 (48.8) 196 (50.1) 0.70 Deceased at day-28 34 (38.2) 60 (33.7) 17 (44.7) 38 (44.2) 149 (38.1) 0.32 Legend : IMV : invasive mechanical ventilation, BMI : body mass index, NP-PCR: naso-pharyngeal polymerase chain reaction, SARS-CoV-2: severe acute respiratory syndrome coronavirus 2, ICU : intensive care unit, APACHE II : Acute Physiology And Chronic Health Evaluation II, SOFA : sequential organ failure assessment, PEEP : positive end-expiratory pressure, PaO2 : arterial partial pressure of oxygen, FiO2 : fraction of inspired oxygen, WHO : world health organization, COPD : chronic obstructive pulmonary disease, IL-6 : interleukin-6, IL-6R : IL-6 receptor. The frequency of missing samples was less than 1% for all the data presented, except for BMI (6.9%) and for the quantitative value of the SARS-CoV-2 nasopharyngeal PCR (NP-PCR) patients (32.2%) because the routine laboratory of several centers responded a qualitative (yes/no) or semi-quantitative result. We considered that these missing values were completely at random. The p-value indicates the significance of the test comparing all the subphenotypes. Post-hoc test of significance between each subphenotypes is provided in Online Resource 5. Subphenotype 1 was characterized by low IgG level and P-Selectin; and various levels of migration markers (CCL2, CXCL10, CCL4), as well as rather low reaction in biomarkers signature C. In general, the median values of most biomarkers were lower in subphenotype 1 than in other sub-phenotypes. Subphenotype 2 demonstrated a high level of IgG, suggesting a higher adaptive response. Yet, just as subphenotype 1, patients in this group presented various levels of CRP, CCL3 and IL-1α. Compared to the subphenotypes 1 and 2, subphenotype 3 was more homogeneous, demonstrating elevated levels of E-selectin, and P-selectin, Signature C pro (IL-12p70, TNF-α, IFN-γ, IL-4) and pro-antiviral response (IL-1β, IFN-α, IL17A) and anti-inflammatory cytokines (IL-13). Altogether, subphenotype 3 represented a higher innate antiviral, pro and anti-inflammatory response, and adhesion molecule activation. Similarly, subphenotype 4 was distinguished from others by higher levels of cell adhesion markers (ICAM-1), migration markers (CCL2, CXCL10, CCL4), signature C pro (IL-6) and anti-inflammatory cytokines (IL-10), suggesting a higher pro and anti-inflammatory response, migration protein and adhesion molecule activation) (Figs. 1 and 2). These profiles are consistent with the immune profiles described in COVID-19 [ 12 , 18 ]. By comparison to the other sub-phenotypes (Table 2 and Fig. 2), sub-phenotype 1 had a lower respiratory severity as expressed by a higher PaO2/FiO2 ratio; sub-phenotype 2 patients were included later in the disease course (they were more frequently under IMV for 2–5 days) and had higher circulating platelet counts. These 2 sub-phenotypes had a lower severity in terms of APACHE II and SOFA scores, and PaO2/FiO2 ratio than sub-phenotypes 3 and 4. Sub-phenotype 4 had higher circulating CRP levels. The post-hoc test of significance between each subphenotypes is provided in Online Resource 5. The patients in the 4 sub-phenotypes did not differ regarding age, gender, ABO blood group, BMI and prior length of hospital stay. Their severity at inclusion as assessed by APACHE II and SOFA scores was higher in the sub-phenotypes 3 and 4. Comorbidities were similar across the phenotypes except that prior hematological malignancy was more prominent in sub-phenotype 4. The use of concomitant medications against SARS-CoV-2 was similar across the sub-phenotypes, except that anti-IL-6 or IL-6R drugs were more frequently administered to the patients of sub-phenotype 4. The patients of sub-phenotype 4 were more frequently included in the first 48 hours from the start of invasive ventilation, suggesting either a faster disease kinetics or a later presentation. The allocation of the trial intervention - CP or SC - was similar across the 4 sub-phenotypes (Table 2 ). Association between clusters and treatment effect As in the entire population of the trial [ 10 ], the results favor CP over SC in all 4 sub-phenotypes. The OR in each sub-phenotype did not reach statistical significance. The heterogeneity of the response to CP between the four sub-phenotypes was insignificant (Q = 0.24, df = 3, p = 0.97) (Fig. 3 and Online Resource 6). Discussion Exploring the immunological response of patients presenting with C-ARDS and requiring IMV, we identified 4 biomarker signatures and 4 sub-phenotypes of patients with different immune profiles. While these sub-phenotypes were associated with differences in terms of acute severity and kinetics of the disease, their response to CP was consistent with its positive effect in the CONFIDENT trial [ 10 ] and did not differ between the phenotypes. We have chosen biomarkers which make it possible to qualify the immune response during sepsis [ 26 ] as well as during COVID-19 [ 18 ]. We used a multiplex panel which is proposed to explore the “immune dysregulation” observed in sepsis [ 27 ] because it includes molecules describing the syndrome proposed under the term “cytokine storm” in COVID-19 [ 28 ]. These include mediators which amplify inflammation as well as the anti-inflammatory response. The regulation of this simultaneous response is supposed to make it possible to eliminate the pathogen while avoiding the dangers of an excessive inflammation. Additionally, a series of chemokines, have the particularity of facilitating the migration of effector cells in the body and certainly participate in the compartmentalization of the anti-infectious response [ 29 ]. Our panel of markers also includes markers of cell adhesion, an indicator of endothelial activation and coagulation [ 27 ]. The production of these biomarkers characterized the various responses observed in bacterial sepsis and in COVID-19 [ 18 , 27 ]. Finally, our panel included alpha interferon, which is known to act in the innate anti-viral response [ 14 ]. The fact that we were able to identify clusters based on these biomarkers with unsupervised statistical analysis techniques was expected because this has already been observed in sepsis [ 30 ] and in COVID-19 [ 6 ]. This requires large patient cohorts and good clinical and pathophysiological homogeneity. These characteristics were observed during the first years of the COVID-19 pandemic. The different profiles that we observed are compatible with what has already been published during covid [ 6 , 12 , 13 , 18 ]. When we initiated this secondary analysis, we assumed that we could individualize certain clusters with a better response to CP and confirm other results [ 6 ]. This was not the case, and we believe this may be due to at least two reasons. First, our cohort is more homogeneous than Fish’s population [ 6 ] in terms of the infection kinetics because our patients were all recruited within a narrow time range when ARDS appeared. This may have led to too small differences between the groups of patients identified by sub-phenotypes. Second, the panel of biomarkers that we used studies the innate immune and inflammatory response to viral infection, while CP is supposed to act via antibodies neutralizing SARS-CoV-2 [ 31 ] by reducing the quantity of viable virus in infected tissues, in this case the lung parenchyma. This action is located upstream of the inflammatory response. Therefore, it may have an impact whatever the inflammatory response secondary to the viral infection. This is consistent with the delay in effect on mortality of approximately 15 days that we observed with CP during ARDS in the CONFIDENT trial, the same delay as that between viral infection and appearance of ARDS [ 10 ]. Our results discourage us to propose selecting certain profiles of innate immune response to treat or to study the effects of treatments with CP. In the absence of studies on cohorts treated with passive immunization other than CP, this result probably cannot be generalized to these types of treatments. Conclusion In a cohort of patients with C-ARDS included in a CP trial, we identified 4 sub-phenotypes based on their immune response. These sub-phenotypes were associated with different clinical profiles. The response to CP, as assessed by the day-28 mortality, was similar across the 4 sub-phenotypes. Abbreviations TNF-α = Tumor necrosis factor alpha IL-1α = Interleukin 1 alpha IL-1β = Interleukin 1 alpha IL-6 = Interleukin 6 IL-12p70 = Heterodimeric 70-kDa Interleukin 12 IL-17A = Interleukin 17A (also Interleukin 17 or CTLA8 (cytotoxic T-lymphocyte-associated antigen 8) GM-CSF = Granulocyte-macrophage colony-stimulating factor IFN-γ = Interferon gamma IL-4 = Interleukin 4 IL-10 = Interleukin 10 IL-13 = Interleukin 13 MCP-1 = monocyte chemoattractant protein 1 (C-C motif chemokine ligand 2 (CCL2)) MIP-1α = Macrophage inflammatory protein 1 alpha (C-C motif chemokine ligand 3 (CCL3)) MIP-1β = Macrophage inflammatory protein 1 beta (C-C motif chemokine ligand 4 (CCL4)) IL-8 = Interleukin 8 (C-X-C motif chemokine ligand 8 (CXCL8)) IP-10 = Interferon gamma-induced protein 10 (C-X-C motif chemokine ligand 10 (CXCL10)) ICAM-1 = Intercellular Adhesion Molecule 1 (Cluster of Differentiation 54 (CD54)) CD62E = CD62 antigen-like family member E (E-selectin) CD62P = CD62 antigen-like family member P (P-selectin) IFN-α = Interferon alpha Declarations Conflict of interest No conflict of interest Funding KCE 2020, 1-0-1 AXA 2021, Liège University 2021 Author contributions Conception and design: BM, AND, SR, AFD, MM, PFL Ethics Ethics Committee of the University Hospital of Liège CE 2020/239. Clinicaltrials.gov NCT04558476. Presentation at scientific conferences Part of the present work was presented at the annual meeting of the European Society of Intensive Care (ESICM) in Milan on October 23, 2023. Author contributions Conception and design: BM, AND, SR, AFD, MM, PFL Acquisition: AB, MP, EH, IM, EDW, AD, PGJ, EVDH, FV, WS, NDS, NDM, NL, JBM, Analysis: BM, AND, AB, EC, AFD Interpretation: BM, AND, AFD Drafting the work and reviewing for important intellectual content: BM, AND, EC, AFD Final approval of the version to be published: BM, AND, AB, MP, EH, IM, EDW, AD, PGJ, EVDH, FV, WS, NDS, NDM, NL, JBM, SB, EC, AFD, MM, PFL Tweet The immune profile does not influence the beneficial effect of convalescent plasma in ARDS due to COVID-19 (secondary analysis of CONFIDENT) References Tan E, Song J, Deane AM, Plummer MP (2021) Global Impact of Coronavirus Disease 2019 Infection Requiring Admission to the ICU. 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BioRxiv Prepr Serv Biol 2023. 08.22.553458. https://doi.org/10.1101/2023.08.22.553458 Supplementary Files ESM1.pdf ESM2.pdf ESM3.pdf ESM4.pdf ESM5.pdf ESM6.pdf STROBEchecklistcohort.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3793271","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":263511352,"identity":"1fb190e7-f33a-4760-bbda-7f2fc3504761","order_by":0,"name":"Benoit 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clustering of biomarkers in patients with COVID-19 induced ARDS. Heatmap of the sub-phenotypes and biomarker signatures.\u003c/p\u003e\n\u003cp\u003eEach patient (n=391) is represented by a row. The rows are grouped into 4 patients’ sub-phenotypes. The first column indicates the subphenotype of the patients. The second column indicates the group of randomization of the patients. The third column indicates the day-28 mortality. The following 22 columns are grouped into 4 biomarker signatures (A to D). The biomarker signatures, sub-phenotype numbers, treatment groups, and mortality at D-28 are noted using a color code provided on the right legend of the figure. Each biomarker (n=23) level transformed into a log10 scale is indicated in the following columns and represented by a color gradient going from blue (low level) to red (high level).\u003c/p\u003e","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3793271/v1/1b24d04ef5a38d5976150cb6.png"},{"id":49088191,"identity":"b317f7d9-3f4c-4b24-be56-a5b302874861","added_by":"auto","created_at":"2024-01-03 01:19:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18448,"visible":true,"origin":"","legend":"\u003cp\u003eBox-and-whisker plots of biomarkers according to sub-phenotypes.\u003c/p\u003e\n\u003cp\u003eBiomarker values are log2 transformed and normalized to the median of sub-phenotype 1 (if the difference of the two log2 of a biomarker of 2 subphenotypes, such as sub- phenotype 2 and sub- phenotype 1, is 1, this means that sub-phenotype 2 has a level twice as high as sub-phenotype 1). They are grouped by biomarker signature (A–D). Boxes are colored by sub-phenotype (bottom legend of the figure). The bottom border of each box represents the 25th percentile; the line bisecting the box represents the median; the upper border of the box is the 75th percentile. The whiskers represent extremes, 1.5 times the 75th (highest) and 25th (lowest) values.\u003c/p\u003e","description":"","filename":"OnlineFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3793271/v1/baec130e0e1287db3b7eaaaf.png"},{"id":49088718,"identity":"ac5fc3d7-d6b5-4891-baeb-c8e6bcf3ec7b","added_by":"auto","created_at":"2024-01-03 01:27:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8689,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot describing the effect on mortality at day-28 of CP per sub-phenotype.\u003c/p\u003e\n\u003cp\u003eCI: confidence interval; the reference group to calculate Odds ratios is standard of care (SOC).\u003c/p\u003e","description":"","filename":"OnlineFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3793271/v1/64f3d346b75828d877707426.png"},{"id":50000206,"identity":"121abb97-3426-478a-ba44-07ee1e5254df","added_by":"auto","created_at":"2024-01-22 23:19:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":621853,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3793271/v1/ac946d39-8552-4344-934f-7d04afb824e6.pdf"},{"id":49088197,"identity":"194bf8bc-c2a5-45dd-ad42-1b1fc744a667","added_by":"auto","created_at":"2024-01-03 01:19:30","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":114623,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3793271/v1/630fb339924e333422fd60f8.pdf"},{"id":49088720,"identity":"10698d64-8b9a-4063-9588-04bf33f3ae48","added_by":"auto","created_at":"2024-01-03 01:27:30","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":153608,"visible":true,"origin":"","legend":"","description":"","filename":"ESM2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3793271/v1/69d7eeb3aab0b27695567031.pdf"},{"id":49088193,"identity":"965fef16-003a-40dd-aba8-8a51329414c4","added_by":"auto","created_at":"2024-01-03 01:19:30","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":106463,"visible":true,"origin":"","legend":"","description":"","filename":"ESM3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3793271/v1/e79cf2d082b7483088804bce.pdf"},{"id":49088719,"identity":"ab6a459f-0edf-4293-9c3d-445e1aad1778","added_by":"auto","created_at":"2024-01-03 01:27:30","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":66989,"visible":true,"origin":"","legend":"","description":"","filename":"ESM4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3793271/v1/dbf7828956767b8f6d16fe53.pdf"},{"id":49088192,"identity":"a73810c7-54e5-413d-97b2-63c2fdd94555","added_by":"auto","created_at":"2024-01-03 01:19:30","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":53580,"visible":true,"origin":"","legend":"","description":"","filename":"ESM5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3793271/v1/dc87893b29c9fa4726b5c10b.pdf"},{"id":49088195,"identity":"9b603c1d-4c65-4bcf-b1e1-b2ac75dc1d71","added_by":"auto","created_at":"2024-01-03 01:19:30","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":29610,"visible":true,"origin":"","legend":"","description":"","filename":"ESM6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3793271/v1/e0b9f3a8178858efb42610e1.pdf"},{"id":49088721,"identity":"c868f271-0998-475c-99e0-e319553d3648","added_by":"auto","created_at":"2024-01-03 01:27:30","extension":"docx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":33839,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistcohort.docx","url":"https://assets-eu.researchsquare.com/files/rs-3793271/v1/32cb1bb51c78c24d92d85839.docx"}],"financialInterests":"","formattedTitle":"Immunological sub-phenotypes and response to Convalescent Plasma in COVID-19 induced ARDS: a secondary analysis of the CONFIDENT trial.","fulltext":[{"header":"Take-home message","content":"\u003cp\u003eIn the cohort of participants in the CONFIDENT COVID-19 Convalescent Plasma trial in patients with COVID-19-induced ARDS, patient groups determined by their immune profile did not respond differently to the study treatment. Immune profile should not be a selection criterion for the use of convalescent plasma in ARDS due to COVID-19.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eAcute respiratory distress syndrome (ARDS) was a prominent feature during the first three years of the COVID-19 pandemic, leading to hospital saturation all over the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among therapies targeting the response against SARS-CoV-2 in these patients, low-dose steroids for 10 days was the first accepted therapy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Passive immunization with plasma collected in COVID-19 convalescents was inconclusive at various stages of the disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In patients admitted to the Intensive Care Unit (ICU) for SARS-CoV-2 induced pneumonia, the REMAP-CAP trialists observed a lower likelihood of providing improvement in organ support-free days [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In a secondary analysis, unsupervised analysis based on cytokines, chemokines and endothelial biomarkers, allowed to individualize sub-phenotypes with different response to convalescent plasma (CP) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As severe COVID-19 has been considered as a particular form of sepsis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and/or ARDS [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], future trials should implement some form of predictive enrichment to increase the likelihood for beneficial effects of an intervention to emerge [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the CONFIDENT trial of CP, we observed that patients with COVID-19 induced ARDS in the first days of invasive mechanical ventilation (IMV) experienced a significant reduction of mortality at 28 days [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. By comparison to prior trials, we attributed this positive result to a greater homogeneity of the study population and to the high neutralizing activity of the CP we administered.\u003c/p\u003e \u003cp\u003eIn the present study, as the individual immune response is likely heterogeneous, we hypothesized that the response to CP could differ in sub-groups determined by their immune profile. We tested this hypothesis as a secondary analysis in the patients of the CONFIDENT trial whose blood samples had been centralized for this purpose. The biomarkers we assessed were based on a multiplex test targeting 20 proteins involved in the inflammatory response. These proteins had all been described to be part of the immune response commonly observed in severe COVID-19 [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThe present study is a secondary analysis of CONFIDENT and is labelled CONFIDENT-II. CONFIDENT was a publicly funded Belgian multicenter randomized open-label trial. The trial was designed to determine the effect on mortality at day-28 of CP with a neutralizing titer against SARS-CoV-2 at least 1/160 versus standard care (SC) in patients with C-ARDS requiring invasive mechanical ventilation (IMV) for less than five days during the pandemic. The randomization process included a stratification based on the prior duration of IMV (\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;or \u0026gt;\u0026thinsp;48 hours). The positive effect on mortality was mainly observed in the patients who underwent randomization 48 hours or less after IMV initiation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe CONFIDENT trial involved adult patients with a Clinical Frailty Scale\u0026thinsp;\u0026lt;\u0026thinsp;6 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], admitted to a participating ICU with a diagnosis of C-ARDS and submitted to IMV for a maximum of five days (WHO 10-point progression scale 7 to 9 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]). ARDS was classified according to the Berlin definition [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. C-ARDS was defined by an extended pneumonia on a CT scan or a chest X-ray within 10 days, and a positive result of SARS-CoV-2 nasopharyngeal PCR (NP-PCR) test within 15 days prior to inclusion. Exclusion criteria were pregnancy, prior episode of transfusion-related side effect, medical decision to limit therapy, and participation in another COVID-19 trial. The trial involved 475 patients between September 10, 2020, and March 9, 2022, and showed a 9.6% crude reduction in mortality at day 28 (p\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e \u003cp\u003eThe CONFIDENT-II population involved the patients who were included in those centers and having accepted to centralize blood samples for secondary analyses. Out of the trial population, plasma total IgG antibodies against SARS-CoV-2 and biomarkers were collected after patients\u0026rsquo; informed consent and before randomization to CP or standard care (SC). The trial was registered at clinicaltrials.gov as NCT04558476 and approved by the institutional review boards of all centers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBiomarkers\u003c/h2\u003e \u003cp\u003eThe measurements were performed at the Laboratory Medicine Department of the CHU de Li\u0026egrave;ge, Belgium. Total IgG antibodies against SARS-CoV-2 were assessed with the LIAISON\u0026reg; SARS-CoV-2 TrimericS IgG chemiluminescent kit (Diasorin, Saluggia, Italy). Results are expressed as Binding Antibody Units per mL (BAU/mL). A value\u0026thinsp;\u0026gt;\u0026thinsp;33.7 BAU/mL is considered positive (manufacturer). Values of 535, 606, 860 and 1335 BAU/mL are considered predictive of 20, 40, 160 and 320 neutralizing antibody titers with 50% plaque reduction (PRNT50), respectively [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe balance between pro- and anti-inflammatory mediators was assessed using Luminex xMAP technology with the Human Inflammation ProcartaPlex\u0026trade; Panel (20-Plex, Thermo Fisher Scientific, MA). The biomarkers assessed were thirteen cytokines (GM-CSF, IFN-α, IFN-γ, IL-1α, IL-1β, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, IL-17A (CTLA-8), and TNF-α), four chemokines (IP-10 (CXCL10), MCP-1 (CCL2), MIP-1α (CCL3), and MIP-1β (CCL4)), and three cell adhesion molecules (ICAM-1, CD62E (E-selectin), and CD62P (P-Selectin)). All the samples were measured in duplicate and the coefficients of variation were all \u0026lt;\u0026thinsp;15%. The results were provided as quantitative values in pg/mL [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For biomarkers whose value was below the lower limit of quantification (LLoQ), we attributed a value of LLoQ. For biomarkers whose value was over the upper limit of quantification (ULoQ), we attributed the ULoQ value. Blood CRP and platelets were included among the biomarkers as indicators of both inflammation and coagulation activation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eVariables are provided as mean (SD) or median (IQR) or counts (percentages). The homogeneity between the CONFIDENT and CONFIDENT-II populations was estimated on baseline characteristics and the primary endpoint (day-28 mortality). Comparisons were made with the Chi-square Fisher\u0026rsquo;s exact, and t-test or and Mann-Whitney tests if assumptions regarding the two tests were not satisfied. The effect on the principal endpoint was measured with the Odds ratio (OR).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCluster analysis\u003c/h2\u003e \u003cp\u003eAgglomerative hierarchical cluster analysis was applied to examine the number of clusters of participants in the included cohort explained by the biomarkers. Wald.D2 linkage function was employed to investigate the variance of the clusters on the log10-transformed biomarker data and the number of clusters was based on the CH index (Calinski and Harabasz Index) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] as the cluster stopping rule. Principal component analysis (PCA) was performed to determine the most contributing biomarkers. Based on descriptive statistics, we normalized the protein biomarkers to the median of the subphenotype with the lowest median values for most biomarkers, namely subphenotype-1, and presented with boxplots of log2 fold-change.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between clusters and treatment effect\u003c/h2\u003e \u003cp\u003eWe used Kruskal Wallis, chi-square and/or Fisher\u0026rsquo;s exact tests to examine the differences among the identified clusters with regards to clinical characteristics. We examined differences in the CP treatment effect as to D-28 mortality within each cluster and as a whole cohort. A test of heterogeneity of the ORs was performed and presented with the Q-statistic. The results were visualized by means of a forest plot, displaying the ORs and the respective 95% confidence interval (CI).\u003c/p\u003e \u003cp\u003eThe analyzes and figures were carried out with the R statistical software, version 4.2.2 (R Core Team, 2021).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy cohort\u003c/h2\u003e \u003cp\u003eAmong the 475 participants in the CONFIDENT trial [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], 237 patients were randomly assigned to CP and 238 to SC. The CONFIDENT-II cohort involved 391/475 (82%) patients. Of these, 196 (50.1%) had been assigned to CP and 195 (49.9%) to SC with a median age was 64 [IQR: 56\u0026ndash;72] years. 66.8% (n\u0026thinsp;=\u0026thinsp;261) were males. Most participants had blood group A (n\u0026thinsp;=\u0026thinsp;180, 46.0%) and O (n\u0026thinsp;=\u0026thinsp;158, 40.4%), their median APACHE II score was 13 [IQR: 9\u0026ndash;17], with a P/F ratio 116 [89\u0026ndash;174]. In this secondary analysis, 375 (95.9%) patients were receiving steroids; and 299 (76.5%) were included within 48 hours of IMV. 149 (38.1%) patients died before day-28. The characteristics of the CONFIDENT-II patients were similar to those in the CONFIDENT cohort except for a lower PaO2/FiO2 ratio and a shorter delay from ICU admission to inclusion in the study cohort. The characteristics of the CP and SC groups within the CONFIDENT-II population are similar (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCONFIDENT-II population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePopulation not included\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCONFIDENT population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConvalescent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConvalescent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStandard\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;196)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;195)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;391)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;475)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 [56\u0026ndash;72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 [56\u0026ndash;71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 [56\u0026ndash;72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 [52\u0026ndash;70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63 [56\u0026ndash;72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.5 [55\u0026ndash;70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64 [56\u0026ndash;72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130 (66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e261 (66.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (68.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34 (79.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62 (73.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e323 (68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMV\u0026thinsp;\u0026lt;\u0026thinsp;48h at inclusion, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150 (76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149 (76.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e299 (76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e342 (72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.5 [26.4\u0026ndash;34.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.9 [26.5\u0026ndash;34.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.4 [26.4\u0026ndash;34.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.4 [27.2\u0026ndash;34.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.4 [26.4\u0026ndash;37.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.7 [27.0\u0026ndash;35.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.2 [26.5\u0026ndash;34.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood group, n\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180 (46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39 (46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e219 (46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158 (40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e192 (40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNP PCR test for SARS-Cov-2, Ct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 [\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23 CR24\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 [\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 [\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25 CR26\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23 [\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23 [\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25 CR26\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21 [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from ICU admission, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6 [1.6\u0026ndash;4.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4 [1.7\u0026ndash;5.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7 [1.6\u0026ndash;4.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.4 [2.7\u0026ndash;5.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5 [3.5\u0026ndash;5.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.4 [3.4\u0026ndash;5.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.4 [1.7\u0026ndash;4.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeverity at ICU admission\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II score, points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 [9.25\u0026ndash;17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 [\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12 CR13 CR14\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12 [8.25\u0026ndash;16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA, points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6 [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEEP level, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 [10\u0026ndash;13.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 [\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10 [9.9\u0026ndash;13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10 [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaO2/FiO2, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116 [89\u0026ndash;154]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 [91\u0026ndash;155]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 [89\u0026ndash;154]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129 [99\u0026ndash;174]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e145 [106\u0026ndash;172]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e137 [100\u0026ndash;174]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e123 [91\u0026ndash;160]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 [67\u0026ndash;189]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 [65\u0026ndash;198]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123 [65\u0026ndash;191]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130 [65\u0026ndash;214]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101 [43\u0026ndash;150]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e123 [55\u0026ndash;189]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e123 [91\u0026ndash;160]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO progression scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities, n\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225 (57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (58.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49 (58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e274 (57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e173 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic renal failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaematological_cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTherapy against SARS-Cov-2, n\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydroxychloroquine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzythromycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemdesivir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-IL-6 or IL-6R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny steroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192 (98.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191 (97.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e383 (98.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42 (97.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83 (98.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e466 (98.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeath at day-28, n\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e191 [40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eLegend : IMV : invasive mechanical ventilation, BMI : body mass index, NP-PCR: naso-pharyngeal polymerase chain reaction, SARS-CoV-2: severe acute respiratory syndrome coronavirus 2, ICU : intensive care unit, APACHE II : Acute Physiology And Chronic Health Evaluation II, SOFA : sequential organ failure assessment, PEEP : positive end-expiratory pressure, PaO2 : arterial partial pressure of oxygen, FiO2 : fraction of inspired oxygen, CRP : C-reactive protein, WHO : world health organization, COPD : chronic obstructive pulmonary disease, IL-6 : interleukin-6, IL-6R : IL-6 receptor.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eThe frequency of missing samples was less than 1% for all the data presented, except for BMI (6.9%) and for the quantitative value of the SARS-CoV-2 nasopharyngeal PCR (NP-PCR) patients (32.2%) because the routine laboratory of several centers responded a qualitative (yes/no) or semi-quantitative result. We considered that these missing values were completely at random.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe frequency of missing samples was less than 1% for all the data presented, except for BMI (6.9%) and for the quantitative value of the SARS-CoV-2 NP-PCR patients (32.2%) because the routine laboratory of several centers responded a qualitative (yes/no) or semi-quantitative result. Therefore, we considered that these missing values were completely at random.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClusters\u003c/h2\u003e \u003cp\u003eHierarchical clustering determined 4 biomarker signatures and 4 sub-phenotypes of patients that could be distinguished by their biomarker and clinical profiles (Figs.\u0026nbsp;1 and 2). The comparative blood levels of each biomarker in the sub-phenotypes are provided in Online Resource 1.\u003c/p\u003e \u003cp\u003eBiomarker signatures were labelled A, B, C and D. Signature A mainly gathered cell adhesion markers (E-selectin, P-selectin, ICAM-1); signature B adaptive response (total IgG against SARS-CoV-2) and migration markers (CCL2, CXCL10, CCL4), signature C pro (IL-6, IL-12p70, GM-CSF, TNF-α, IFN-γ) and anti-inflammatory (IL-4) cytokines; and signature D innate cytokines (IFN-α), pro-antiviral response (IL-1α, IL-1β, IFN-α, IL17A), and anti-inflammatory cytokines (IL-10, IL-13).\u003c/p\u003e \u003cp\u003eSub-phenotype-2 consisted of the most patients (n\u0026thinsp;=\u0026thinsp;178, 45.5%), followed by sub-phenotype-1 (n\u0026thinsp;=\u0026thinsp;89, 22.8%) and sub-phenotype-4 (n\u0026thinsp;=\u0026thinsp;86, 22.0%), and sub-phenotype-3 (n\u0026thinsp;=\u0026thinsp;38, 9.7%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;1). The most contributing biomarkers were IL-1β, IL-12p70, IL-6, IFN-α, IL-17A, IFN-γ, IL-13, TFN-α, total IgG, and CXCL10 (Online Resource 2, 3 and 4).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of the sub-phenotypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubphenotype 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubphenotype 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubphenotype 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubphenotype 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;89)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;178)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;391)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 [56\u0026ndash;72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 [56\u0026ndash;73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 [59\u0026ndash;72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 [57\u0026ndash;70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64 [56\u0026ndash;72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale gender, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56 (65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e261 (66.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.8 [26.9\u0026ndash;34.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.7 [27.4\u0026ndash;34.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.2 [26.0\u0026ndash;34.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.2 [24.7\u0026ndash;34.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.4 [26.4\u0026ndash;34.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABO blood group, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e180 (46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eO\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e158 (40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNP PCR test for SARS-Cov-2, Ct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 [\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 [\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 [\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23 CR24\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 [\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 [18\u0026ndash;38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from ICU admission, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5 [1.6\u0026ndash;3.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6 [2.4\u0026ndash;5.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5 [1.6\u0026ndash;5.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5 [1.5\u0026ndash;4.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7 [1.6\u0026ndash;4.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeverity at inclusion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMV\u0026thinsp;\u0026lt;\u0026thinsp;48 hours, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77 (89.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e299 (76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II score, points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 [\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA total, points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRespiratory\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCoagulation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLiver\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCardiovascular\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [0\u0026ndash;3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 [0\u0026ndash;3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 [0\u0026ndash;3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 [0\u0026ndash;3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCentral nervous system\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRenal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 [0\u0026ndash;1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaO2/FiO2, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 [105\u0026ndash;164]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122 [96\u0026ndash;159]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 [83\u0026ndash;137]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98 [84\u0026ndash;133]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116 [89\u0026ndash;154]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEEP level, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO progression scale, points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities, n\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (68.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e225 (57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e141 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic renal failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematologic cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTherapy against SARS-Cov-2, n\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydroxychloroquine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzythromycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemdesivir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-IL-6 or anti-IL-6R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDexamethasone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85 (95.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171 (96.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83 (96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e375 (95.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment group and outcome, n\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllocated to CP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e196 (50.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeceased at day-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e149 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eLegend :\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eIMV : invasive mechanical ventilation, BMI : body mass index, NP-PCR: naso-pharyngeal polymerase chain reaction, SARS-CoV-2: severe acute respiratory syndrome coronavirus 2, ICU : intensive care unit, APACHE II : Acute Physiology And Chronic Health Evaluation II, SOFA : sequential organ failure assessment, PEEP : positive end-expiratory pressure, PaO2 : arterial partial pressure of oxygen, FiO2 : fraction of inspired oxygen, WHO : world health organization, COPD : chronic obstructive pulmonary disease, IL-6 : interleukin-6, IL-6R : IL-6 receptor.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe frequency of missing samples was less than 1% for all the data presented, except for BMI (6.9%) and for the quantitative value of the SARS-CoV-2 nasopharyngeal PCR (NP-PCR) patients (32.2%) because the routine laboratory of several centers responded a qualitative (yes/no) or semi-quantitative result. We considered that these missing values were completely at random.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe p-value indicates the significance of the test comparing all the subphenotypes. Post-hoc test of significance between each subphenotypes is provided in Online Resource 5.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSubphenotype 1 was characterized by low IgG level and P-Selectin; and various levels of migration markers (CCL2, CXCL10, CCL4), as well as rather low reaction in biomarkers signature C. In general, the median values of most biomarkers were lower in subphenotype 1 than in other sub-phenotypes.\u003c/p\u003e \u003cp\u003eSubphenotype 2 demonstrated a high level of IgG, suggesting a higher adaptive response. Yet, just as subphenotype 1, patients in this group presented various levels of CRP, CCL3 and IL-1α.\u003c/p\u003e \u003cp\u003eCompared to the subphenotypes 1 and 2, subphenotype 3 was more homogeneous, demonstrating elevated levels of E-selectin, and P-selectin, Signature C pro (IL-12p70, TNF-α, IFN-γ, IL-4) and pro-antiviral response (IL-1β, IFN-α, IL17A) and anti-inflammatory cytokines (IL-13). Altogether, subphenotype 3 represented a higher innate antiviral, pro and anti-inflammatory response, and adhesion molecule activation.\u003c/p\u003e \u003cp\u003eSimilarly, subphenotype 4 was distinguished from others by higher levels of cell adhesion markers (ICAM-1), migration markers (CCL2, CXCL10, CCL4), signature C pro (IL-6) and anti-inflammatory cytokines (IL-10), suggesting a higher pro and anti-inflammatory response, migration protein and adhesion molecule activation) (Figs.\u0026nbsp;1 and 2). These profiles are consistent with the immune profiles described in COVID-19 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBy comparison to the other sub-phenotypes (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;2), sub-phenotype 1 had a lower respiratory severity as expressed by a higher PaO2/FiO2 ratio; sub-phenotype 2 patients were included later in the disease course (they were more frequently under IMV for 2\u0026ndash;5 days) and had higher circulating platelet counts. These 2 sub-phenotypes had a lower severity in terms of APACHE II and SOFA scores, and PaO2/FiO2 ratio than sub-phenotypes 3 and 4. Sub-phenotype 4 had higher circulating CRP levels. The post-hoc test of significance between each subphenotypes is provided in Online Resource 5.\u003c/p\u003e \u003cp\u003eThe patients in the 4 sub-phenotypes did not differ regarding age, gender, ABO blood group, BMI and prior length of hospital stay. Their severity at inclusion as assessed by APACHE II and SOFA scores was higher in the sub-phenotypes 3 and 4. Comorbidities were similar across the phenotypes except that prior hematological malignancy was more prominent in sub-phenotype 4. The use of concomitant medications against SARS-CoV-2 was similar across the sub-phenotypes, except that anti-IL-6 or IL-6R drugs were more frequently administered to the patients of sub-phenotype 4. The patients of sub-phenotype 4 were more frequently included in the first 48 hours from the start of invasive ventilation, suggesting either a faster disease kinetics or a later presentation. The allocation of the trial intervention - CP or SC - was similar across the 4 sub-phenotypes (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between clusters and treatment effect\u003c/h2\u003e \u003cp\u003eAs in the entire population of the trial [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], the results favor CP over SC in all 4 sub-phenotypes. The OR in each sub-phenotype did not reach statistical significance. The heterogeneity of the response to CP between the four sub-phenotypes was insignificant (Q\u0026thinsp;=\u0026thinsp;0.24, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.97) (Fig.\u0026nbsp;3 and Online Resource 6).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eExploring the immunological response of patients presenting with C-ARDS and requiring IMV, we identified 4 biomarker signatures and 4 sub-phenotypes of patients with different immune profiles. While these sub-phenotypes were associated with differences in terms of acute severity and kinetics of the disease, their response to CP was consistent with its positive effect in the CONFIDENT trial [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and did not differ between the phenotypes.\u003c/p\u003e \u003cp\u003eWe have chosen biomarkers which make it possible to qualify the immune response during sepsis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] as well as during COVID-19 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We used a multiplex panel which is proposed to explore the \u0026ldquo;immune dysregulation\u0026rdquo; observed in sepsis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] because it includes molecules describing the syndrome proposed under the term \u0026ldquo;cytokine storm\u0026rdquo; in COVID-19 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These include mediators which amplify inflammation as well as the anti-inflammatory response. The regulation of this simultaneous response is supposed to make it possible to eliminate the pathogen while avoiding the dangers of an excessive inflammation. Additionally, a series of chemokines, have the particularity of facilitating the migration of effector cells in the body and certainly participate in the compartmentalization of the anti-infectious response [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our panel of markers also includes markers of cell adhesion, an indicator of endothelial activation and coagulation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The production of these biomarkers characterized the various responses observed in bacterial sepsis and in COVID-19 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Finally, our panel included alpha interferon, which is known to act in the innate anti-viral response [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe fact that we were able to identify clusters based on these biomarkers with unsupervised statistical analysis techniques was expected because this has already been observed in sepsis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and in COVID-19 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This requires large patient cohorts and good clinical and pathophysiological homogeneity. These characteristics were observed during the first years of the COVID-19 pandemic. The different profiles that we observed are compatible with what has already been published during covid [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhen we initiated this secondary analysis, we assumed that we could individualize certain clusters with a better response to CP and confirm other results [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This was not the case, and we believe this may be due to at least two reasons. First, our cohort is more homogeneous than Fish\u0026rsquo;s population [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] in terms of the infection kinetics because our patients were all recruited within a narrow time range when ARDS appeared. This may have led to too small differences between the groups of patients identified by sub-phenotypes. Second, the panel of biomarkers that we used studies the innate immune and inflammatory response to viral infection, while CP is supposed to act via antibodies neutralizing SARS-CoV-2 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] by reducing the quantity of viable virus in infected tissues, in this case the lung parenchyma. This action is located upstream of the inflammatory response. Therefore, it may have an impact whatever the inflammatory response secondary to the viral infection. This is consistent with the delay in effect on mortality of approximately 15 days that we observed with CP during ARDS in the CONFIDENT trial, the same delay as that between viral infection and appearance of ARDS [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur results discourage us to propose selecting certain profiles of innate immune response to treat or to study the effects of treatments with CP. In the absence of studies on cohorts treated with passive immunization other than CP, this result probably cannot be generalized to these types of treatments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn a cohort of patients with C-ARDS included in a CP trial, we identified 4 sub-phenotypes based on their immune response. These sub-phenotypes were associated with different clinical profiles. The response to CP, as assessed by the day-28 mortality, was similar across the 4 sub-phenotypes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTNF-\u0026alpha; = Tumor necrosis factor alpha\u003c/p\u003e\n\u003cp\u003eIL-1\u0026alpha;\u0026nbsp;= Interleukin 1 alpha\u003c/p\u003e\n\u003cp\u003eIL-1\u0026beta;\u0026nbsp;= Interleukin 1 alpha\u003c/p\u003e\n\u003cp\u003eIL-6 = Interleukin 6\u003c/p\u003e\n\u003cp\u003eIL-12p70 = Heterodimeric 70-kDa Interleukin 12\u003c/p\u003e\n\u003cp\u003eIL-17A = Interleukin 17A (also Interleukin 17 or CTLA8 (cytotoxic T-lymphocyte-associated antigen 8)\u003c/p\u003e\n\u003cp\u003eGM-CSF = Granulocyte-macrophage colony-stimulating factor\u003c/p\u003e\n\u003cp\u003eIFN-\u0026gamma; = Interferon gamma\u003c/p\u003e\n\u003cp\u003eIL-4 = Interleukin 4\u003c/p\u003e\n\u003cp\u003eIL-10 = Interleukin 10\u003c/p\u003e\n\u003cp\u003eIL-13 = Interleukin 13\u003c/p\u003e\n\u003cp\u003eMCP-1 = monocyte chemoattractant protein 1 (C-C motif chemokine ligand 2 (CCL2))\u003c/p\u003e\n\u003cp\u003eMIP-1\u0026alpha; = Macrophage inflammatory protein 1 alpha (C-C motif chemokine ligand 3 (CCL3))\u003c/p\u003e\n\u003cp\u003eMIP-1\u0026beta; = Macrophage inflammatory protein 1 beta (C-C motif chemokine ligand 4 (CCL4))\u003c/p\u003e\n\u003cp\u003eIL-8 = Interleukin 8 (C-X-C motif chemokine ligand 8 (CXCL8))\u003c/p\u003e\n\u003cp\u003eIP-10 = Interferon gamma-induced protein 10 (C-X-C motif chemokine ligand 10 (CXCL10))\u003c/p\u003e\n\u003cp\u003eICAM-1 = Intercellular Adhesion Molecule 1 (Cluster of Differentiation 54 (CD54))\u003c/p\u003e\n\u003cp\u003eCD62E = CD62 antigen-like family member E (E-selectin)\u003c/p\u003e\n\u003cp\u003eCD62P = CD62 antigen-like family member P (P-selectin)\u003c/p\u003e\n\u003cp\u003eIFN-\u0026alpha; = Interferon alpha\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eNo conflict of interest\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eKCE 2020, 1-0-1 AXA 2021, Li\u0026egrave;ge University 2021\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eConception and design: BM, AND, SR, AFD, MM, PFL\u003c/p\u003e\u003ch2\u003eEthics\u003c/h2\u003e\n\u003cp\u003eEthics Committee of the University Hospital of Liège CE 2020/239. Clinicaltrials.gov NCT04558476.\u003c/p\u003e\u003ch2\u003ePresentation at scientific conferences\u003c/h2\u003e\n\u003cp\u003ePart of the present work was presented at the annual meeting of the European Society of Intensive Care (ESICM) in Milan on October 23, 2023.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cul\u003e\n \u003cli\u003eConception and design: BM, AND, SR, AFD, MM, PFL\u003c/li\u003e\n \u003cli\u003eAcquisition: AB, MP, EH, IM, EDW, AD, PGJ, EVDH, FV, WS, NDS, NDM, NL, JBM,\u003c/li\u003e\n \u003cli\u003eAnalysis: BM, AND, AB, EC, AFD\u003c/li\u003e\n \u003cli\u003eInterpretation: BM, AND, AFD\u003c/li\u003e\n \u003cli\u003eDrafting the work and reviewing for important intellectual content: BM, AND, EC, AFD\u003c/li\u003e\n \u003cli\u003eFinal approval of the version to be published: BM, AND, AB, MP, EH, IM, EDW, AD, PGJ, EVDH, FV, WS, NDS, NDM, NL, JBM, SB, EC, AFD, MM, PFL\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003eTweet\u003c/h2\u003e\n\u003cp\u003eThe immune profile does not influence the beneficial effect of convalescent plasma in ARDS due to COVID-19 (secondary analysis of CONFIDENT)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTan E, Song J, Deane AM, Plummer MP (2021) Global Impact of Coronavirus Disease 2019 Infection Requiring Admission to the ICU. 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BioRxiv Prepr Serv Biol 2023.\u003cdiv class=\"ExternalRefDOI\"\u003e08.22.553458. https://doi.org/10.1101/2023.08.22.553458\u003c/div\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, convalescent plasma, ARDS, immune response, phenotypes","lastPublishedDoi":"10.21203/rs.3.rs-3793271/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3793271/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eConvalescent Plasma (CP) reduced the mortality in COVID-19 induced ARDS (C-ARDS) patients treated in the CONFIDENT trial. As patients are immunologically heterogeneous, we hypothesized that clusters may differ in their treatment responses to CP.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e We measured 20 cytokines, chemokines and cell adhesion markers using a multiplex technique at the time of inclusion in the CONFIDENT trial in patients of centers having accepted to participate in this secondary study. We performed descriptive statistics, unsupervised hierarchical cluster analysis, and examined the association between the clusters and CP effect on day-28 mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 475 patients included in CONFIDENT, 391 (82%) were sampled, and 196/391 (50.1%) had been assigned to CP. We identified four sub-phenotypes representing 89 (22.8%), 178 (45.5%), 38 (9.7%), and 86 (22.0%) patients. The most contributing biomarkers in the principal component analysis were IL-1β, IL-12p70, IL-6, IFN-α, IL-17A, IFN-γ, IL-13, TFN-α, total IgG, and CXCL10. Sub-phenotype-1 displayed a lower immune response, sub-phenotype-2 a higher adaptive response, subphenotype-3 the highest innate antiviral, pro and anti-inflammatory response, and adhesion molecule activation, and sub-phenotype-4 a higher pro and anti-inflammatory response, migration protein and adhesion molecule activation. Sub-phenotype-2 and sub-phenotype-4 had higher severity at the time of inclusion. The effect of CP treatment on mortality appeared higher than standard care in each sub-phenotype, without heterogeneity between sub-phenotypes (p\u0026thinsp;=\u0026thinsp;0.97).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn patients with C-ARDS, we identified 4 sub-phenotypes based on their immune response. These sub-phenotypes were associated with different clinical profiles. The response to CP was similar across the 4 sub-phenotypes.\u003c/p\u003e","manuscriptTitle":"Immunological sub-phenotypes and response to Convalescent Plasma in COVID-19 induced ARDS: a secondary analysis of the CONFIDENT trial.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-03 01:19:25","doi":"10.21203/rs.3.rs-3793271/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d7840664-4a24-4726-9ce5-20653d3ca3ac","owner":[],"postedDate":"January 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-22T23:11:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-03 01:19:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3793271","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3793271","identity":"rs-3793271","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}