The prognostic value of the SOFA score in patients with COVID-19: a retrospective, observational study

The prognostic value of the SOFA score in patients with COVID-19: a retrospective, observational study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article The prognostic value of the SOFA score in patients with COVID-19: a retrospective, observational study Zheng Yang, Qinming Hu, Fei Huang, Shouxin Xiong, yi sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-76663/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Aug, 2021 Read the published version in Medicine → Version 1 posted You are reading this latest preprint version Abstract Background Patients with Coronavirus disease 2019 (COVID-19) have a high mortality rate, and thus, it is particularly important to predict the severity and prognosis of COVID-19. The Sequential Organ Failure Assessment (SOFA) score has been used to predict the clinical outcomes of patients with multiple organ failure requiring intensive care. Therefore, we retrospectively analyzed the clinical characteristics, risk factors, and relationship between the SOFA score and the prognosis of COVID-19 patients. Methods Clinical variables were compared between patients with mild and severe COVID-19. Univariate and multivariate logistic regression analyses were performed to identify the risk factors for severe COVID-19. The Cox proportional hazards model was used to analyze risk factors for hospital-related death. Survival analysis was performed by the Kaplan-Meier method, and survival differences were assessed by the log-rank test. Receiver operating characteristic (ROC) curves of the SOFA score in different situations were drawn, and the area under the ROC curve was calculated. Results The median SOFA score of all patients was 2 (IQR, 1–3). Patients with severe COVID-19 exhibited a significantly higher SOFA score than patients with mild COVID-19 [3 (IQR, 2–4) vs 1 (IQR, 0–1); P < 0.001]. The SOFA score increased the risk of severe COVID-19, with an odds ratio of 5.851 (95% CI: 3.044–11.245; P < 0.001). The area under the ROC curve (AUC) was used to evaluate the diagnostic accuracy of the SOFA score in predicting severe COVID-19 [cutoff value = 2; AUC = 0.908 (95% CI: 0.857–0.960); sensitivity: 85.20%; specificity: 80.40%] and the risk of death in COVID-19 patients [cutoff value = 5; AUC = 0.995 (95% CI: 0.985-1.000); sensitivity: 100.00%; specificity: 95.40%]. Regarding the 60-day mortality rates of patients in the two groups classified by the optimal cutoff value of the SOFA score (5), patients in the high SOFA score group (SOFA score ≥ 5) had a significantly greater risk of death than those in the low SOFA score group (SOFA score < 5). Conclusion The SOFA score could be used to evaluate the severity and 60-day mortality of COVID-19. The SOFA score may be an independent risk factor for in-hospital death. Infectious Diseases COVID-19 SOFA score SARS-CoV-2 severity prognosis ARDS Figures Figure 1 Figure 2 Introduction In late December 2019, the World Health Organization (WHO) China National Representative Office was informed that a series of unexplained cases of pneumonia had been found in Wuhan. Gene sequencing analysis showed that it was a novel coronavirus, and it was later classified as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) [ 1 ] . The novel coronavirus disease 2019 was caused by SARS-CoV-2, which was termed COVID-19 on February 11 [ 2 ] . SARS-CoV-2 can simultaneously infect ciliated cells and secretory cells of human respiratory epithelium. Therefore, compared with common coronaviruses, it is more infectious and pathogenic [ 3 ] . As of August14, 2020 (03:37 pm CEST), the WHO website reported that there were 89,625 confirmed cases in China, of which 4,707 patients died. The number of confirmed infections worldwide reached 20,730,456, including 751,154 deaths and 216 countries or regions infected [ 2 ] . In fact, the number of deaths caused by COVID-19 epidemic may be greater than officially reported [ 4 ] . The world is currently in the midst of a COVID-19 pandemic. Patients with COVID-19 may be clinically asymptomatic, but severe patients may have poor clinical prognosis and may experience acute respiratory distress syndrome (ARDS), organ dysfunction, shock, acute kidney injury and acute heart injury or even death [ 5 ] . Patients with severe illness, within 1 week of onset may develop hypoxemia and dyspnea and then rapidly progress to ARDS or end-stage organ failure [ 6 ] . In the early series of cases in Wuhan, China, 26% of patients were admitted to the intensive care unit (ICU), with a mortality rate of 4.3% [ 7 ] . Therefore, reliable prognostic indicators are greatly needed because they can provide an accurate evaluation of the disease and aid in the selection of more effective treatment strategies. However, effective and simple methods to evaluate the severity and prognosis of COVID-19 patients are still challenges for clinicians. The Sequential Organ Failure Assessment (SOFA) score is one of the scoring systems used to evaluate organ failure and can predict the severity and outcome of the disease [ 8 , 9 ] . The SOFA scoring system was launched in 1996, and its performance is based on the evaluation of the following six major organ functions: circulation, respiration, liver, renal function, central nervous system and coagulation function. The score of each organ is between 0 and 4. It is an easy-to-use tool for systematically and continuously evaluating organ functions during hospitalization [ 10 ] . At present, there are no systematic studies to show whether the SOFA score can correctly evaluate the prognosis and severity of COVID-19. Therefore, a retrospective study was conducted to evaluate the accuracy of the SOFA score in predicting the severity and prognosis of COVID-19. Methods Study Population This is a retrospective observational study involving 117 hospitalized patients with COVID-19, all of whom were from Jingzhou Central Hospital between January 16, 2020 and March 23, 2020. The study was approved by the Institutional Ethics Committee of Jingzhou Central Hospital, and the requirement for informed consent from the study participants was waived. All adult patients diagnosed with the coronavirus disease 2019 according to the WHO interim guidelines [11] were screened for multidisciplinary diagnosis and treatment by infectious disease, respiratory and intensive care physicians. Patients who were discharged or died from January 16, 2019 (when the first patient was admitted to the hospital), to March 23, 2020, were included in the study. Clinical outcomes (such as death) were recorded from March 23, 2020, to the last date of follow-up. Data collection Trained physicians collected patient epidemiological, demographic, and clinical and laboratory data through the electronic medical record system. All patients were followed up until May 10, 2020. The SOFA score was evaluated when the patient had the worst physiological data after admission. The patients were classified into two groups: mild (including mild and common) group and severe (including severe and critical) group. The mild group exhibited mild clinical symptoms, no imaging manifestations of pneumonia. The common group had fever and respiratory symptoms, and imaging showed pneumonia. Patients in the severe group had dyspnea, with RR ≥ 30 beats/min at rest, average oxygen saturation ≤ 93%, and arterial partial pressure of oxygen (PaO2)/oxygen concentration (FiO2) ≤ 300 mmHg. Patients in the critical group had the occurrence of shock, respiratory failure requiring mechanical ventilation, and multiple organ failure requiring ICU monitoring and treatment [12] . Procedures Before admission, the nasopharyngeal swabs of all patients were routinely collected for SARS-CoV-2 real-time PCR detection, and patients with positive findings were admitted to the hospital. After admission, laboratory testing and imaging examination (CT scan) were routinely performed in all patients. Routine examinations included liver and renal function, electrolytes, procalcitonin (PCT), myocardial zymogram, interleukin-6 (IL-6), complete blood count, blood gas analysis, high sensitivity C-reactive protein (hs-CRP), etc. Patients reached the discharge criteria when they had a normal body temperature for ≥ 3 days, their respiratory symptoms were relieved, the lung CT manifestations significantly improved, and they had two consecutive nasopharyngeal swabs with negative results from nucleic acid tests (the sampling time was at least 24 hours apart). Statistical analysis Continuous variables with a normal distribution and nonnormal distribution are summarized as the mean ± standard deviation (SD) and the median (interquartile range, IQR), respectively. Categorical variables are expressed as frequencies or percentages. Differences between severe and mild cases were analyzed using the χ2 test , Mann-Whitney U test or Fisher exact test , where appropriate. Univariate and multivariate logistic regression models were used to identify the risk factors associated with the severity of COVID-19. All clinically important covariates and those with a significant correlation in the univariate analyses ( P<0.15 ) were included in the multivariate analysis. Survival curves were drawn and the differences in survival were assessed by using the Kaplan–Meier method and log rank test. The cutoff value of the SOFA score was determined by receiver operating characteristic (ROC) curve analysis. Based on the cutoff value of the SOFA score, the patients were classified into 2 groups. The hazard ratios (HRs) with 95% confidence intervals for the risk factors for death were determined using Cox proportional hazards regression models. The Cox proportional hazards model was used to analyze the potential risk factors associated with the SOFA score for in-hospital death. All statistical analyses were carried out with SPSS (IBM, version 25.0). Graphs were plotted using the GraphPad Prism 8.0 (San Diego, CA, USA). A P value of <0.05 was considered statistically significant, and all tests were two-tailed. Results Demographic, baseline and clinical characteristics of patients with COVID-19 We analyzed 117 confirmed COVID-19 patients, including 60 males, accounting for 51.3%. More than half (61) of the patients were identified as critical patients, and 9 of them died. The majority of severe patients were men, accounting for 55.7%. The median age of the patients was 50.00 years (IQR, 35.50–62.00 years), ranging from 18 to 83 years. The median age of seriously ill patients was significantly higher than that of mild patients [56.00 (IQR, 45.00–68.00) vs 38.00 (IQR, 31.00-54.50)]. Sixty-three patients had complications, and hypertension (27.4%) was the most common complication, followed by diabetes (8.5%), stroke (4.3%), coronary heart disease (3.4%), chronic liver disease (CLD1) (3.4%) and so on. The proportion of hypertension in severe patients was higher than that in mild patients (42.6% vs 10.7%, P < 0.001). It is observed that on admission, fever (82.9%) and dry cough (70.1%) were the most common symptoms, followed by dyspnea (39.3%), fatigue (29.1%), and sore throat (9.4%). The median time from onset to admission of all patients was 5 days (IQR, 3–7 days), no difference found between the mild and severe group (Table 1 ). The median SOFA score of the patients was 2 (IQR, 1–3), the minimum SOFA score was 0, and the maximum SOFA score was 16. The distribution of SOFA scores is shown in Fig. 1 A. The median SOFA score of severe patients was higher than that of mild patients [3 (IQR, 2–4) vs 1 (IQR, 0–1); P < 0.001]. Table 1 Demographics and baseline characteristics of patients with COVID-19 Demographics and clinical characteristics All patients(n = 117) Mild(n = 56) Severe(n = 61) P value Age, years 50.00(35.50–62.00) 38(31.00-54.50) 56.00(45.00–68.00) 0.000 Sex 0.302 Male 60, 51.3% 26, 46.4% 34, 55.7% Female 57, 48.7% 30, 53.6% 27, 44.3% Comorbidity Hypertension 32, 27.4% 6, 10.7% 26, 42.6% 0.000 Diabetes 10, 8.5% 2, 3.6% 8, 13.1% 0.058 Coronary heart disease 4, 3.4% 1, 1.8% 3, 4.9% 0.352 Stroke 5, 4.3% 2, 3.6% 3, 4.9% 0.655 Carcinoma 3, 2.6% 0 3, 4.9% 0.093 Chronic liver disease 4, 3.4% 3, 5.4% 1, 1.6% 0.269 Chronic kidney disease 1, 0.9% 0 1, 1.6% 0.336 Chronic lung disease 2, 1.7% 0 2, 3.2% 0.172 Other 2, 1.7% 0 2, 3.2% 0.172 Signs and symptoms Fever 97, 82.9% 48, 85.7% 49, 80.3% 0.919 Cough 82, 70.1% 35, 62.5% 47, 77.0% 0.185 Dyspnea 46, 39.3% 8, 14.3% 38, 62.3% 0.000 Diarrhea 5, 4.3% 3, 5.4% 2, 3.3% 0.655 Fatigue 34, 29.1% 18, 32.1% 16, 26.2% 0.732 Sore throat 11, 9.4% 7, 12.5% 4, 6.6% 0.366 Myalgia 5, 4.3% 3, 5.4% 2, 3.3% 0.655 Sputum production 6, 5.1% 2, 3.6% 4, 6.6% 0.414 Highest temperature (℃) 38.40(37.75–38.80) 38.25(37.70–38.70) 38.50(37.80–39.00) 0.058 SOFA score 2(1–3) 1(0–1) 3(2–4) 0.000 Lowest SPO2(%) 94(90–95) 95(94–95) 90(86–92) 0.000 Days from symptoms to hospital admission 5(3–7) 4(3–7) 5(3–7) 0.867 Abbreviations: COVID-19, Coronavirus disease 2019; SOFA score, Sequential Organ Failure Assessment score; SPO2, oxygen saturation; Values are medians (interquartile ranges) or number (percentages), where N is the total number of patients with available data. P values comparing severe and mild patients are from χ2, Fisher's exact test, or Mann-Whitney U test. P < 0.05 are considered statistically significant. Laboratory characteristics of the study population Table 2 summarizes the laboratory test results of the patients after admission. The median lymphocyte count (LYM) of the patients was 0.860 × 10 9 /ml. The LYM in severe patients was lower than that in mild patients [0.640 (IQR, 0.420–0.965) vs 1.035 (IQR, 0.690–1.360); P < 0.001]. The hs-CRP, white blood cell (WBC), neutrophil (NE), and gamma-glutamyl transpeptidase (γ-GGT) levels in severe patients were higher than those in mild patients [16.830 (IQR, 4.730-43.665) vs 4.815 (IQR, 0.870-16.967); 9.990 (IQR, 6.525–12.940) vs 6.185 (IQR, 4.400-9.175); 8.320 (IQR, 4.925–11.900) vs 4.495 (IQR, 2.872–7.247); 65.200 (IQR, 38.650–132.200) vs 40.700 (IQR, 17.025–65.475); all P < 0.001]. In addition, the PCT, direct bilirubin (DBIL), alanine aminotransferase (ALP), cystatin C, and creatine kinase muscle-brain isoform (CK-MB) levels of severe patients were higher than those of mild patients[0.080 (IQR, 0.050–0.165) vs 0.040 (IQR, 0.030–0.060); 4.300 (IQR, 2.800–7.150) vs 3.200 (IQR, 2.325-4.400); 59.200 (IQR, 44.350–79.750) vs 53.750 (IQR, 42.275–63.300); 0.960 (IQR, 0.820–1.110) vs 0.820 (IQR, 0.712–0.947); 16.900 (IQR, 12.900–23.300) vs 13.500 (IQR, 9.750-16.312); all P < 0.05], but the levels of severe patients were all within the normal range. Table 2 Laboratory findings of patients with COVID-19 Laboratory findings Normal Range All patients(n = 117) Mild(n = 56) Severe(n = 61) P value Procalcitonin, ng/mL 0-0.5 0.060(0.0350–0.110) 0.040(0.030–0.060) 0.080(0.050–0.165) 0.000 IL-6, pg/L 0–7 27.489(8.685–27.489) 27.489(7.247–27.489) 22.700(8.685–34.290) 0.224 White blood cells, ×10 9 /ml 3.5–9.5 7.300(5.025–11.270) 6.185(4.400-9.175) 9.990(6.525–12.940) 0.000 Neutrophils, ×10 9 /ml 1.8–6.3 6.190(3.460–9.910) 4.495(2.872–7.247) 8.320(4.925–11.900) 0.000 Lymphocytes, ×10 9 /ml 1.1–3.2 0.860(0.560–1.215) 1.035(0.690–1.360) 0.640(0.420–0.965) 0.000 Monocytes, ×10 9 /ml 0.1–0.6 0.400(0.300–0.530) 0.400(0.292–0.497) 0.380(0.305–0.535) 0.733 Eosinophil, ×10 9 /mL 0.02–0.52 0.000(0.000-0.055) 0.005(0.000-0.067) 0.000(0.000-0.045) 0.233 Basophil, ×10 9 /mL 0-0.06 0.010(0.010–0.030) 0.010(0.010–0.020) 0.020(0.010–0.035) 0.114 Red blood cell, ×10 12 /mL 3.8–5.1 4.214 ± 0.623 4.339 ± 0.530 4.099 ± 0.683 0.035 Hemoglobin, g/L 115–150 134.000(118.500–145.000) 134.000(121.000-146.750) 133.000(116.000-141.500) 0.125 Platelets, ×10 9 /ml 125–350 198.000(157.500–259.000) 194.500(156.000-231.500) 217.000(159.000-279.000) 0.200 hs-CRP, mg/L 0–10 8.760(2.560–25.410) 4.815(0.870-16.967) 16.830(4.730-43.665) 0.000 Total bilirubin, umol/L < 21 11.400(9.100–16.200) 11.000(8.650-13.925) 13.000(9.300-18.150) 0.053 Direct bilirubin, umol/L < 7.5 3.700(2.500–5.800) 3.200(2.325-4.400) 4.300(2.800–7.150) 0.009 Indirect bilirubin, umol/L < 18.9 7.800(6.250–10.600) 7.700(6.000-9.875) 8.200(6.500-11.250) 0.180 Alanine aminotransferase, U/L 7–40 63.400(28.700-117.450) 55.600(22.050-104.475) 68.500(33.700-127.500) 0.167 Aspartate aminotransferase, U/L 13–35 33.200(21.050–47.650) 28.200(19.075–43.400) 36.700(22.250-51.000) 0.114 Alkaline phosphatase, U/L 40–150 54.800(42.900–71.300) 53.750(42.275–63.300) 59.200(44.350–79.750) 0.041 Gamma-glutamyl transpeptidase, U/L 7–45 54.600(28.500–87.400) 40.700(17.025–65.475) 65.200(38.650–132.200) 0.000 Urea nitrogen, mmol/L 2.6–7.5 5.140(4.130–6.515) 4.620(3.790–5.520) 5.540(4.645–7.665) 0.000 Creatinine, umol/L 41–73 60.800(49.050–67.700) 61.250(49.250-68.175) 60.600(48.200–66.900) 0.631 Uric acid, umol/L 142–339 238.740 ± 92.149 264.960 ± 81.093 214.660 ± 95.694 0.003 Cystatin C, mg/L 0.54–1.15 0.870(0.750–1.030) 0.820(0.712–0.947) 0.960(0.820–1.110) 0.000 Glomerular filtration rate, ml/min/ > 90 130.500(107.350-153.050) 129.250(114.125-148.875) 132.000(103.000-161.450) 0.849 Potassium, mmol/L 3.5–5.3 4.340 ± 0.600 4.431 ± 0.575 4.257 ± 0.615 0.119 Sodium, mmol/L 137.0-147.0 140.182 ± 3.315 141.094 ± 2.592 139.344 ± 3.368 0.002 Chlorine, mmol/L 99.0-110.0 103.800(101.100-105.400) 104.350(102.425-105.775) 102.200(100.600-104.800) 0.006 Calcium, mmol/L 2.11–2.52 2.000(1.900-2.155) 2.025(1.940–2.187) 1.980(1.850–2.145) 0.122 Magnesium, mmol/L 0.70–1.15 0.986 ± 0.093 0.988 ± 0.093 0.984 ± 0.093 0.857 Phosphorus, mmol/L 0.85–1.51 1.107 ± 0.247 1.183 ± 0.185 1.036 ± 0.277 0.001 CK, U/L < 167 73.800(48.500-109.925) 74.600(45.175-105.331) 72.900(49.400–124.000) 0.561 CK-MB, U/L < 24 14.400(10.500-18.775) 13.500(9.750-16.312) 16.900(12.900–23.300) 0.001 cTnI, ug/L < 0.04 0.010(0.010–0.040) 0.010(0.010–0.035) 0.010(0.010–0.042) 0.579 Myoglobin, ug/L 1.5–70.0 31.000(18.900-54.750) 27.250(17.475–42.600) 33.650(24.300–55.500) 0.069 Abbreviations: COVID-19, Coronavirus disease 2019; IL-6, interleukin-6; hs-CRP, high-sensitivity C-reactive protein; CK, creatine kinase; CK-MB, creatine kinase muscle-brain isoform; cTnI, cardiac troponin I; Data are mean ± SD, or median (IQR). P values comparing severe and mild patients are from Fisher's exact test or Mann-Whitney U test. P < 0.05 are considered statistically significant. Analysis of risk factors for severe COVID-19 To determine the risk factors for the bad prognosis of patients with severe disease, we compared the clinical and laboratory characteristics of mild and severe patients. Univariate logistic regression analysis showed that age, hypertension, SOFA score, and the levels of IL-6, WBC, NE, LYM, red blood cell (RBC), hs-CRP, DBIL, ALP, GGT, urea nitrogen, uric acid, cystatin C, sodium (Na), chlorine (Cl), phosphorus (P) and CK-MB were related to the aggravation of the patient's condition. Multivariable logistic regression analysis showed that SOFA score, advanced age, and hypertension were independently associated with the risk of severe COVID-19. In addition, the levels of WBC, NE, LYM, ALP, P, urea nitrogen, cystatin C and CK-MB were also related with severe COVID-19.Univariate analysis showed that SOFA score was a risk factor for patients with severe COVID-19, with an odds ratio of 5.328 (95% CI: 2.932–9.681; P < 0.001). Multivariate analysis also revealed that SOFA score was a risk factor for patients with severe COVID-19 (OR = 5.851; 95% CI: 3.044–11.245; P < 0.001 ) (Table 3 ). Table 3 Risk factors associated with severe COVID-19 patients Risk factors Univariable OR (95% CI) p value Multivariable OR (95% CI) p value Age, years 1.064(1.034–1.094) 0.000 1.069(1.036–1.103) 0.000 Hypertension 6.190(2.307–16.614) 0.000 7.310(1.705–31.350) 0.007 Diabetes 4.075(0.827–20.088) 0.084 SOFA score 5.328(2.932–9.681) 0.000 5.851(3.044–11.245) 0.000 IL-6 1.021(1.002–1.041) 0.029 White blood cells 1.196(1.078–1.327) 0.001 1.195(1.060–1.346) 0.004 Neutrophils 1.232(1.107–1.371) 0.000 1.210(1.084–1.351) 0.001 Lymphocytes 0.222(0.094–0.526) 0.001 0.280(0.107–0.730) 0.009 Red blood cell 0.520(0.278–0.973) 0.041 hs-CRP 1.026(1.008–1.045) 0.006 Direct bilirubin 1.207(1.016–1.434) 0.032 Alkaline phosphatase 1.020(1.002–1.039) 0.032 1.032(1.007–1.057) 0.013 Gamma-glutamyl transpeptidase 1.006(1.001–1.012) 0.027 Urea nitrogen 1.484(1.173–1.876) 0.001 1.480(1.154–1.898) 0.002 Uric acid 0.993(0.989–0.998) 0.005 Cystatin C 33.239(4.316–255.970) 0.001 30.893(2.988-319.403) 0.004 Sodium 0.819(0.715–0.937) 0.004 Chlorine 0.844(0.743–0.960) 0.010 Phosphorus 0.068(0.012–0.380) 0.002 0.055(0.007–0.462) 0.008 CK-MB 1.092(1.027–1.161) 0.005 1.086(1.017–1.161) 0.030 Procalcitonin 5.689(0.710-45.575) 0.102 Hemoglobin 0.980(0.960-1.000) 0.056 Platelets 1.004(0.999–1.009) 0.096 Total bilirubin 1.057(0.995–1.123) 0.072 Indirect bilirubin 1.068(0.981–1.163) 0.128 Potassium 0.608(0.325–1.139) 0.120 Calcium 0.220(0.032–1.485) 0.120 Myoglobin 1.004(0.999–1.009) 0.112 Abbreviations: COVID-19, Coronavirus disease 2019; SOFA score, Sequential Organ Failure Assessment score; IL-6, interleukin-6; hs-CRP, high-sensitivity C-reactive protein; CK-MB, creatine kinase muscle-brain isoform; OR, odds ratio; CI, confidence interval. ROC curves and cumulative survival curves for predicting the severity and prognosis of COVID-19 ROC curves were constructed to evaluate the predictive value of the SOFA score for the severity and prognosis of COVID-19 (Fig. 1 B and Fig. 1 C). The area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic accuracy of the SOFA score for predicting severe COVID-19 [cutoff value = 2; AUC = 0.908 (95% CI: 0.857–0.960); sensitivity: 85.20%; specificity: 80.40%] and the risk of death in COVID-19 patients [cutoff value = 5; AUC = 0.995 (95% CI: 0.985-1.000); sensitivity: 100.00%; specificity: 95.40%]. Kaplan-Meier curve analysis and the log-rank test were performed to assess the cumulative survival rates and compare the 60-day survival curves between the high SOFA score group (SOFA score ≥ 5) and the low SOFA score group (SOFA score < 5). Patients in the high SOFA score group had a significantly higher risk of death than those in the low SOFA score group ( log-rank, P < 0.001) (Fig. 1 D). Results of Cox proportional hazards regression analysis Cox proportional hazards regression analysis was used to assess the potential association between the SOFA score and hospital death. The univariate analysis indicated that the SOFA score was associated with a higher risk of hospital death (HR = 1.279, 95% CI: 1.123–1.456, P < 0.001). In addition, patient age, lowest SPO2, hypertension, CLD1, chronic lung disease (CLD2), chronic kidney disease (CKD), and the levels of RBC, hemoglobin (Hb), hs-CRP, total bilirubin (TBIL), DBIL, aspartate aminotransferase (AST), ALP, urea nitrogen, creatinine, cystatin C, cardiac troponin I (cTnI) and myoglobin were associated with the risk of death in the hospital ( Table 4 ). Multivariate Cox proportional hazards regression analysis was used to evaluate the independent prognostic effect of the SOFA score. After adjusting for CLD2, CKD, and CLD1 (model 1), the HR of the SOFA score for predicting hospital deaths was 1.405 (95% CI: 1.132–1.744, P = 0.002 ). After adjusting for age, CLD1, and CLD2 (model 2), the HR was 1.336 (95% CI: 1.069–1.670, P = 0.011 ). After adjusting for hypertension, CLD2, and CKD (model 3), the HR was 1.292 (95% CI: 1.090–1.532, P = 0.003 ). After adjusting for cystatin C (model 4), the HR was 1.276 (95% CI: 1.083–1.504, P = 0.004 ). After adjusting for LYM, hs-CRP, creatine kinase (CK), and CK-MB (model 5), the HR was 1.341 (95% CI: 1.045–1.721, P = 0.021 ). After adjusting for CK-MB (model 6), the HR was 1.270 (95% CI: 1.096–1.472, P = 0.001 ). After adjusting for Na (model 7), the HR was 1.320 (95% CI: 1.127–1.546, P = 0.001 ). In this process, age, CLD1, CKD, CLD2, cystatin C, hs-CRP, CK and CK-MB also showed significance for independently predicting hospital death, while Na showed a protective effect ( Table 5 ). Forest plots depicting the results of the multivariate analysis of each SOFA score model assessed by the Cox proportional hazards regression model are shown in Fig. 2 . Table 4 Results of univariate Cox proportional-hazards regression analyzing the effect of variables on in hospital death Risk factors HR (95%CI) p value Age 1.101（1.026-1.181） 0.007 SOFA score 1.279（1.123-1.456） 0.000 Lowest SPO2 0.910（0.872-0.950） 0.000 Hypertension 6.083（1.173-31.540） 0.032 Chronic lung disease 13.079（1.450-117.959） 0.022 Chronic kidney disease 24.937（2.768-224.666） 0.004 Chronic liver disease 12.467（1.270-122.355） 0.030 Red blood cell 0.141（0.045-0.441） 0.001 Hemoglobin 0.942（0.909-0.976） 0.001 hs-CRP 1.020（1.004-1.037） 0.015 Total bilirubin 1.022（1.004-1.041） 0.019 Direct bilirubin 1.034（1.008-1.062） 0.010 Aspartate aminotransferase 1.006（1.002-1.011） 0.005 Alkaline phosphatase 1.007（1.002-1.011） 0.002 Urea nitrogen 1.318（1.172-1.483） 0.000 Creatinine 1.006（1.003-1.009） 0.000 Cystatin C 2.400（1.618-3.558） 0.000 Sodium 0.779（0.626-0.969） 0.025 CK 1.007（1.003-1.011） 0.000 CK-MB 1.040（1.017-1.064） 0.001 cTnI 1.072（1.022-1.125） 0.004 Myoglobin 1.007（1.004-1.010） 0.000 Highest temperature 0.449（0.194-1.041） 0.062 Fever 0.212（0.043-1.051） 0.058 Dyspnea 6.088（0.692-53.598） 0.104 Lymphocytes 0.128（0.009-1.904） 0.136 Eosinophil 55.502（0.613-5029.250） 0.081 Platelets 0.990（0.978-1.002） 0.103 Indirect bilirubin 1.060（0.997-1.127） 0.064 Abbreviations: SOFA score, Sequential Organ Failure Assessment score; SPO2, oxygen saturation; hs-CRP, high-sensitivity C-reactive protein; CK, creatine kinase; CK-MB, creatine kinase muscle-brain isoform; cTnI, cardiac troponin I; HR, hazard ratio; CI, confidence interval. Table 5 Results of multivariate Cox proportional-hazards regression analyzing the effect of variables on in hospital death Risk factors HR (95%CI) p value Not Adjusted SOFA score 1.279（1.123-1.456） 0.000 Mode 1 SOFA score 1.405(1.132-1.744) 0.002 Chronic lung disease 93.516（4.063-2152.432） 0.005 Chronic kidney disease 10.216（0.957-109.050） 0.054 Chronic liver disease 69.136（3.226-1481.626） 0.007 Mode 2 SOFA score 1.336（1.069-1.670） 0.011 Age 1.152（1.018-1.302） 0.024 Chronic lung disease 43.406（1.869-1008.004） 0.019 Chronic liver disease 1515.310（10.430-220156.492） 0.004 Mode 3 SOFA score 1.292（1.090-1.532） 0.003 Hypertension 5.995（0.655-54.887） 0.113 Chronic lung disease 16.956（1.197-240.105） 0.036 Chronic kidney disease 21.147（1.224-365.495） 0.036 Mode 4 SOFA score 1.276（1.083-1.504） 0.004 Cystatin C 1.913（1.304-2.806） 0.001 Mode 5 SOFA score 1.341（1.045-1.721） 0.021 Lymphocytes 22.140（0.756-647.954） 0.072 hs-CRP 1.035（1.001-1.071） 0.043 CK 1.007（1.002-1.012） 0.009 CK-MB 1.055（1.006-1.107） 0.027 Mode 6 SOFA score 1.270（1.096-1.472） 0.001 CK-MB 1.034（1.007-1.061） 0.014 Mode 7 SOFA score 1.320（1.127-1.546） 0.001 Sodium 0.775（0.623-0.964） 0.022 Abbreviations: SOFA score, Sequential Organ Failure Assessment score; hs-CRP, high-sensitivity C-reactive protein; CK, creatine kinase; CK-MB, creatine kinase muscle-brain isoform; HR, hazard ratio; CI, confidence interval. Discussion Severe COVID-19 can easily cause ARDS, multiple organ dysfunction, acute heart injury, acute kidney injury and even death [ 5 ] . Identifying the death risk associated with critically ill patients early and giving these patients priority treatment in a timely manner are particularly important in global health emergencies. Studies have shown that a total of 60 predictors can assess the severity of COVID-19, of which 7 factors are considered to be highly correlated and consistent, including SOFA score, age, D-dimer, hs-CRP, body temperature, albumin and diabetes [ 13 ] . The results of this study revealed that SOFA score, age, CKD, CLD1, CLD2, cystatin C, hs-CRP, CK, CK-MB and other factors were independent risk factors for in-hospital death, which was similar to the results of the above study. Zhou et al. showed that older age, higher D-dimer levels and higher SOFA scores in COVID-19 patients at admission were associated with high in-hospital mortality. In addition, increased levels of cTnI, lactate dehydrogenase and lymphocytopenia were more common in patients with severe COVID-19 [ 14 ] . Also, studies have shown that SARS-CoV-2 can participate in and induce the activation of complement and coagulation system, which is related to the severity of COVID-19 patients [ 15 ] . Myocardial injury was another an independent risk factor for deterioration and death in patients with COVID-19. The risk of death of hospitalized patients with myocardial injury was 6.6–26.9 times higher than that of patients without myocardial injury [ 16 ] . In this study, the median age of seriously ill patients was 56.00 years, which was significantly higher than that of mild patients (38 years). Hypertension was the most common complication of COVID-19, especially in severe patients. A review of the literature showed that COVID-19 patients with hypertension, especially elderly patients, had a 2.5-fold increased risk of serious or even fatal events [ 17 ] . Another large cohort study showed that in addition to some factors, such as advanced age, male, asthma, diabetes, increased the risk of death in COVID-19 patients, poverty and ethnicity (Blacks and South Asians) were also associated with death of COVID-19 patients [ 18 ] . At present, published studies have not systematically evaluated the accuracy of the SOFA score in the diagnosis of COVID-19 severity and its predictive value. The SOFA score was originally used to assess the severity of organ dysfunction in patients with severe sepsis and has been validated in ICU patients in multiple regions [ 19 ] . As critically ill patients usually have multiple organ dysfunction, the SOFA score has been widely used to predict the clinical outcomes of critically ill patients, such as predicting mortality in patients with chronic liver failure and hematological malignancies [ 20 , 21 ] . Gupta et al. summarized the clinical characteristics of SARS-CoV-2 infection, which could not only cause severe lung injury, but also damaged the heart, liver, kidney, nervous system, endocrine system, blood system and skin, resulting in arrhythmia, acute coronary syndrome, thrombosis, gastrointestinal symptoms, hyperglycemia and skin rash [ 22 ] . Thus, the SOFA score can comprehensively assess multiple organ dysfunction caused by SARS-CoV-2. In our study, the SOFA score was also recognized as a valuable prognostic tool for the outcome of patients with COVID-19. Univariate regression analysis showed that the increase in SOFA score and IL-6 and the decrease in lymphocyte count were related to the aggravation of the patient's condition. Multivariable regression analysis demonstrated that SOFA score, advanced age, and hypertension were independently associated with the risk of severe COVID-19. At present, it is believed that COVID-19 leads to organ failure, which is mainly related to cytokine storm and immunosuppression; the clinical manifestations are persistent fever, hemocytopenia and organ involvement [ 23 ] . The laboratory results were characterized by increased levels of inflammatory factors such as granulocyte colony-stimulating factor (G-CSF), interleukin-2 (IL-2), IL-6, interleukin-7 (IL-7), interferon-γ–inducible protein-10 (IP-10), tumor necrosis factor α (TNF-α), macrophage inflammatory protein-1 α (MIP-1 α) and monocyte chemoattractant protein 1 (MCP-1) [ 24 , 25 ] . The analysis of the immune system of patients with severe COVID-19 showed that the number of innate immune cells increased, while T cells decreased. In COVID-19 patients, the early increase of cytokines was positively correlated with poor prognosis [ 26 ] . This may well explain why only dexamethasone has been found to reduce mortality in many clinical trials for COIVD-19 [ 27 ] . Therefore, the SOFA score can reflect not only multiple organ failure but also the degree of inflammation and can accurately predict the severity of the patient’s disease. Sepsis is a life-threatening organ dysfunction, caused by dysregulated host response to infection. Rapid change in SOFA score ≥ 2 points after infection is regarded as the clinical criterion of sepsis-associated organ dysfunction. The SOFA score ≥ 2 reflects approximately 10% of the overall risk of death of suspected infected patients in general hospitals, and even patients with moderate organ dysfunction may further deteriorate. Therefore, it emphasizes the seriousness of this situation and reminds clinicians to intervene in a timely and appropriate manner [ 28 ] . In this study, the area under the ROC curve (AUC) of the SOFA score was 0.908 (95% CI: 0.857–0.960) with a diagnostic cut-off value of 2 and a sensitivity and specificity of 85.20% and 80.40%, respectively. This result suggests that a SOFA score ≥ 2 can predict the severity of COVID-19 patients. Another study also showed that among 184,875 patients admitted to the ICU, an increase of 2 or more in the SOFA score had greater prognostic accuracy for in-hospital mortality than quick SOFA (qSOFA) score or the systemic inflammatory response syndrome standard [ 29 ] . When the cut-off value of the optimal SOFA score is 5 (AUC: 0.995, 95% CI: 0.985-1.000, sensitivity: 100.00%, specificity: 95.40%), the risk of mortality in patients with COVID-19 can be predicted. Regarding the 60-day mortality rate of patients in the high and low SOFA score groups, patients in the high SOFA score group (SOFA score ≥ 5) had a significantly higher risk of death than those in the low SOFA score group (SOFA score 5 within 1–7 days after liver transplantation was significantly lower than that of patients with SOFA score ≤ 5 [ 30 ] . Therefore, SOFA score ≥ 5 can be used as a good predictor of hospital mortality in COVID-19 patients. In addition, univariate and multivariate Cox proportional hazards regression analyses demonstrated that there was a high correlation between the SOFA score and hospital mortality, and the SOFA score was a risk factor for death in COVID-19 patients. These results provide strong evidence for priority in treatment and early special care for patients. Nevertheless, some limitations should be considered when interpreting the results of this study. First, this is a single center retrospective study involving a relatively small number of patients. Second, our study was limited by its retroactive design, which resulted in some data unavailable in the electronic medical records. In some cases, if the patient’s condition was stable during hospitalization without dyspnea and hypoxia, blood gas analysis was not performed, so the SOFA score could not be calculated accurately and had to be estimated by the EM algorithm. However, in our study, the data loss rate of this variable was less than 25%. Finally, the retrospective nature of our study may lead to selection bias, and the findings need to be verified and refined by future prospective studies. Conclusions The world is currently in the midst of a pandemic of COVID-19. The impact of the COVID-19 epidemic on human health,modern society and global economy is self-evident. At present, there is no specific drug for anti-viral treatment of COVID-19. Clinically, respiratory support and symptomatic treatment are mainly used in patients with COVID-19, while the related prophylactic vaccine is still in clinical trial. Given this background, a simple and practical tool for predicting the prognosis of patients with COVID-19 is particularly important. It will help hospitals improve their priority setting, and, based on this, special care will be provided for critically ill patients to reduce economic burden to patients and society. In conclusion, the SOFA score may be an independent risk factor for hospital death and can be well used to assess the severity and prognosis of COVID-19. Abbreviations COVID-19: Coronavirus disease 2019; SOFA: Sequential Organ Failure Assessment; ROC: Receiver operating characteristic; AUC: area under the ROC curve; WHO: World Health Organization; SARS-CoV-2:severe acute respiratory syndrome coronavirus-2; FiO2: oxygen (PaO2)/oxygen concentration; ARDS: acute respiratory distress syndrome; ICU: intensive care unit; SD: standard deviation; IQR: interquartile range; HRs: hazard ratios; HR: hazard ratio; qSOFA: quick SOFA; G-CSF: granulocyte colony-stimulating factor; IL-2:interleukin-2; IL-6: interleukin-6; IL-7 : interleukin-7; IP-10: interferon-γ–inducible protein-10; TNF-α: tumor necrosis factor α; MIP-1 α: macrophage inflammatory protein-1 α; MCP-1: monocyte chemoattractant protein 1; SPO2: oxygen saturation; hs-CRP: high-sensitivity C-reactive protein; CK: creatine kinase; CK-MB: creatine kinase muscle-brain isoform; cTnI: cardiac troponin I; OR: odds ratio; CI: confidence interval; CLD1: Chronic liver disease; CLD2: Chronic lung disease; CKD: Chronic kidney disease; HP: Hypertension; LYM: Lymphocytes; Hb: hemoglobin; WBC: white blood cell; NE: neutrophil; γ-GGT: gamma-glutamyl transpeptidase; DBIL: direct bilirubin; ALP: alanine aminotransferase; PCT: procalcitonin; RBC: red blood cell; Na: sodium; Cl: chlorine; P: phosphorus Declarations Acknowledgements We thank all patients and medical staff at Jingzhou Central Hospital who were involved in this study. Authors’ contribution Zheng Yang, Qinming Hu and Yi Sun conceived the study idea, and performed interpretation, manuscript writing and final approval. Fei Huang, and Shouxin Xiong performed data analysis and collection. All authors reviewed and approved the final version of the manuscript. Funding This work was supported by Hubei Province Health and Family Planning Scientific Research Project (WJ2018H178 to Yi Sun) and the Natural Science Foundation of Hubei Province (2019CFB567 to Yi Sun). Availability of supporting data Due to the finalization of the clinical study report, the data analyzed during the current study are not publicly available, but can be obtained from the correspondent author under reasonable request. Ethics approval and consent to participate The study was reviewed and approved for publication by the Institutional Review Board of Jinghzou Central Hospital, and the requirement for informed consent from the study participants was waived. Consent for publication Not applicable Competing interests The authors declare that there is no conflict of interest. References World Health Organization. Novel coronavirus. Available at: https://www.who.int/westernpacific/emergencies/novecoronavirus . Accessed 16 January 2020. World Health Organization. Coronavirus disease (COVID-19) Pandemic. Available at: https://www.who.int/emergencies/diseases/novel-coronavirus-2019 . Accessed 14 August 2020. Zhu N, Wang W, Liu Z, et al. Morphogenesis and cytopathic effect of SARS-CoV-2 infection in human airway epithelial cells. Nat Commun. 2020;11(1):3910. Weinberger DM, Chen J, Cohen T, et al. Estimation of Excess Deaths Associated With the COVID-19 Pandemic in the United States, March to May 2020. Jama Intern Med.2020. Guan W, Ni Z, Hu Y, et al. Clinical characteristics of coronavirus disease 2019 in China. New Engl J Med. 2020;382(18):1708–20. Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet. 2020;395(10223):507–13. Wang D, Hu B, Hu C, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan. China Jama. 2020;323(11):1061–69. Jones AE, Trzeciak S, Kline JA, et al. The Sequential Organ Failure Assessment score for predicting outcome in patients with severe sepsis and evidence of hypoperfusion at the time of emergency department presentation. Crit Care Med. 2009;37(5):1649. Chen S, Chao T, Chiang M, et al. Prediction of Patient Outcome from Acinetobacter baumannii Bacteremia with Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) II Scores. Internal medicine (Tokyo, 1992).2011; 50(8):871–77. Vincent J, Moreno R, Takala J, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. 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Pathogenesis and Management of Myocardial Injury in Coronavirus Disease 2019. Eur J Heart Fail.2020. Lippi G, Wong J, Henry BM, et al. Hypertension and its severity or mortality in Coronavirus Disease 2019 (COVID-19): a pooled analysis. Pol Arch Intern Med. 2020;130(4):304–09. Williamson EJ, Walker AJ, Bhaskaran K, et al. OpenSAFELY: factors associated with COVID-19 death in 17 million patients. Nature.2020:1–11. Vincent J, De Mendonça A, Cantraine F, et al. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Crit Care Med. 1998;26(11):1793–800. Pan HC, Jenq CC, Tsai MH, et al. Scoring systems for 6-month mortality in critically ill cirrhotic patients: a prospective analysis of chronic liver failure–sequential organ failure assessment score (CLIF‐SOFA). Aliment Pharm Ther. 2014;40(9):1056–65. Demandt AM, Geerse DA, Janssen BJ, et al. The prognostic value of a trend in modified SOFA score for patients with hematological malignancies in the intensive care unit. Eur J Haematol. 2017;99(4):315–22. Gupta A, Madhavan MV, Sehgal K, et al. Extrapulmonary manifestations of COVID-19. Nat Med. 2020;26(7):1017–32. Mehta P, McAuley DF, Brown M, et al. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet (London, England).2020; 395(10229):1033. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan. China The lancet. 2020;395(10223):497–506. Ruan Q, Yang K, Wang W, et al. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intens Care Med. 2020;46(5):846–48. Lucas C, Wong P, Klein J, et al. Longitudinal analyses reveal immunological misfiring in severe COVID-19. Nature.2020:1–09. Recovery Collaborative Group. Dexamethasone in hospitalized patients with Covid-19—preliminary report. New Engl J Med.2020. Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). Jama.2016; 315(8):801–10. Raith EP, Udy AA, Bailey M, et al. Prognostic accuracy of the SOFA score, SIRS criteria, and qSOFA score for in-hospital mortality among adults with suspected infection admitted to the intensive care unit. Jama. 2017;317(3):290–300. Wang X, Niu X, Li J, et al. SOFA Score can effectively predict the incidence of sepsis and 30-day mortality in liver transplant patients: a retrospective study. Adv Ther. 2019;36(3):645–51. Cite Share Download PDF Status: Published Journal Publication published 13 Aug, 2021 Read the published version in Medicine → 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. 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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-76663","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":3818449,"identity":"05424281-4d75-40bc-83aa-49c0bc4d1ded","order_by":0,"name":"Zheng Yang","email":"","orcid":"","institution":"Department of Infectious Disease,Jingzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Yang","suffix":""},{"id":3818450,"identity":"045d79bc-216c-4133-ba17-2ec7ea5600cd","order_by":1,"name":"Qinming Hu","email":"","orcid":"","institution":"Department of Infectious Disease, Jingzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qinming","middleName":"","lastName":"Hu","suffix":""},{"id":3818451,"identity":"f9c1dc23-020e-4a05-b8ac-5fd73b6075bd","order_by":2,"name":"Fei Huang","email":"","orcid":"","institution":"Department of Infectious Disease, Jingzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Huang","suffix":""},{"id":3818452,"identity":"bf85f928-aeba-4b71-9985-7d35056e051b","order_by":3,"name":"Shouxin Xiong","email":"","orcid":"","institution":"Medical department, Jingzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shouxin","middleName":"","lastName":"Xiong","suffix":""},{"id":3818453,"identity":"68470960-0c8f-41a6-a4f6-22de5dd5613b","order_by":4,"name":"yi sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBACxgYog5+Z+fAD0rRItrOlGZBmncF5HgUJolQy9x9/Js3bVpe4+TAPgwFDjU00EQ47kGzM23Y4cdth3gMPGI6l5TYQ1NLYcPAxb9sBoBa+BAPGhsNEaGkGKgM7rJnHQII4LW3MjEBbmBM3MBOtpYeN2XDOucPGMw4DAzmBGL8YAkNM4k1ZnWx//+HDDz7U2BChBaiCiZeNwRGsMoGQchCQBznuxx8Ge2IUj4JRMApGwQgFACr4QDg9vz6MAAAAAElFTkSuQmCC","orcid":"","institution":"Jingzhou Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"yi","middleName":"","lastName":"sun","suffix":""}],"badges":[],"createdAt":"2020-09-12 10:37:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-76663/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-76663/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1097/MD.0000000000026900","type":"published","date":"2021-08-13T22:53:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":3193069,"identity":"9b904b75-e590-48b7-af5e-bc4119c3537f","added_by":"auto","created_at":"2020-10-26 13:19:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56174,"visible":true,"origin":"","legend":"A. Distribution of SOFA scores in patients with COVID-19\nB. ROC curves for patients with SOFA score =2 in predicting the severity of COVID-19\nC. ROC curves for patients with SOFA score =5 in predicting the death in the hospital \nD. Kaplan-Meier survival curves for patients with COVID-19 in the SOFA score ≥5 group and in the SOFA score \u003c5 group","description":"","filename":"Onlinefloatimage1.Png","url":"https://assets-eu.researchsquare.com/files/rs-76663/v1/81686814c20ed85440755487.Png"},{"id":3193070,"identity":"bc3c501a-a952-4936-801f-24dc47dfe4fd","added_by":"auto","created_at":"2020-10-26 13:19:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38752,"visible":true,"origin":"","legend":"Forest plots demonstrating the association of SOFA score with the death of COVID-19 patients in the hospital\nAbbreviations: SOFA score, Sequential Organ Failure Assessment score; COVID-19, Coronavirus disease 2019; CLD1, Chronic liver disease; CLD2, Chronic lung disease; CKD, Chronic kidney disease; HP, Hypertension; CK, creatine kinase; CK-MB, creatine kinase muscle-brain isoform; hs-CRP, high-sensitivity C-reactive protein; LYM, Lymphocytes.\n","description":"","filename":"Onlinefloatimage2.Png","url":"https://assets-eu.researchsquare.com/files/rs-76663/v1/6aa397e91148344d55655239.Png"},{"id":13605771,"identity":"cedf4972-b761-410c-bf9c-b619537d1df1","added_by":"auto","created_at":"2021-09-17 06:04:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":799679,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-76663/v1/85738b07-6e83-4082-afac-d8154af5c1ac.pdf"}],"financialInterests":"","formattedTitle":"The prognostic value of the SOFA score in patients with COVID-19: a retrospective, observational study","fulltext":[{"header":"Introduction","content":" \u003cp\u003eIn late December 2019, the World Health Organization (WHO) China National Representative Office was informed that a series of unexplained cases of pneumonia had been found in Wuhan. Gene sequencing analysis showed that it was a novel coronavirus, and it was later classified as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The novel coronavirus disease 2019 was caused by SARS-CoV-2, which was termed COVID-19 on February 11\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. SARS-CoV-2 can simultaneously infect ciliated cells and secretory cells of human respiratory epithelium. Therefore, compared with common coronaviruses, it is more infectious and pathogenic \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. As of August14, 2020 (03:37\u0026nbsp;pm CEST), the WHO website reported that there were 89,625 confirmed cases in China, of which 4,707 patients died. The number of confirmed infections worldwide reached 20,730,456, including 751,154 deaths and 216 countries or regions infected \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In fact, the number of deaths caused by COVID-19 epidemic may be greater than officially reported \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The world is currently in the midst of a COVID-19 pandemic. Patients with COVID-19 may be clinically asymptomatic, but severe patients may have poor clinical prognosis and may experience acute respiratory distress syndrome (ARDS), organ dysfunction, shock, acute kidney injury and acute heart injury or even death \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Patients with severe illness, within 1 week of onset may develop hypoxemia and dyspnea and then rapidly progress to ARDS or end-stage organ failure \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In the early series of cases in Wuhan, China, 26% of patients were admitted to the intensive care unit (ICU), with a mortality rate of 4.3% \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Therefore, reliable prognostic indicators are greatly needed because they can provide an accurate evaluation of the disease and aid in the selection of more effective treatment strategies. However, effective and simple methods to evaluate the severity and prognosis of COVID-19 patients are still challenges for clinicians. The Sequential Organ Failure Assessment (SOFA) score is one of the scoring systems used to evaluate organ failure and can predict the severity and outcome of the disease \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The SOFA scoring system was launched in 1996, and its performance is based on the evaluation of the following six major organ functions: circulation, respiration, liver, renal function, central nervous system and coagulation function. The score of each organ is between 0 and 4. It is an easy-to-use tool for systematically and continuously evaluating organ functions during hospitalization \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. At present, there are no systematic studies to show whether the SOFA score can correctly evaluate the prognosis and severity of COVID-19. Therefore, a retrospective study was conducted to evaluate the accuracy of the SOFA score in predicting the severity and prognosis of COVID-19.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a retrospective observational study involving 117 hospitalized patients with COVID-19, all of whom were from Jingzhou Central Hospital between January 16, 2020 and March 23, 2020. The study was approved by the Institutional Ethics Committee of Jingzhou Central Hospital, and the requirement for informed consent from the study participants was waived. All adult patients diagnosed with the coronavirus disease 2019 according to the WHO interim guidelines\u003csup\u003e [11]\u003c/sup\u003e were screened for multidisciplinary diagnosis and treatment by infectious disease, respiratory and intensive care physicians. Patients who were discharged or died from January 16, 2019 (when the first patient was admitted to the hospital), to March 23, 2020, were included in the study. Clinical outcomes (such as death) were recorded from March 23, 2020, to the last date of follow-up.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrained physicians collected patient epidemiological, demographic, and clinical and laboratory data through the electronic medical record system. All patients were followed up until May 10, 2020. The SOFA score was evaluated when the patient had the worst physiological data after admission. The patients were classified into two groups: mild (including mild and common) group and severe (including severe and critical) group. The mild group exhibited mild clinical symptoms, no imaging manifestations of pneumonia. The common group had fever and respiratory symptoms, and imaging showed pneumonia. Patients in the severe group had dyspnea, with RR \u0026ge; 30 beats/min at rest, average oxygen saturation \u0026le; 93%, and arterial partial pressure of oxygen (PaO2)/oxygen concentration (FiO2) \u0026le; 300 mmHg. Patients in the critical group had the occurrence of shock, respiratory failure requiring mechanical ventilation, and multiple organ failure requiring ICU monitoring and treatment\u003csup\u003e [12]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore admission, the nasopharyngeal swabs of all patients were routinely collected for SARS-CoV-2 real-time PCR detection, and patients with positive findings were admitted to the hospital. After admission, laboratory testing and imaging examination (CT scan) were routinely performed in all patients. Routine examinations included liver and renal function, electrolytes, procalcitonin (PCT), myocardial zymogram, interleukin-6 (IL-6),\u0026nbsp;complete blood count, blood gas analysis, high sensitivity C-reactive protein (hs-CRP), etc. Patients reached the discharge criteria when they had a normal body temperature for \u0026ge; 3 days, their respiratory symptoms were relieved, the lung CT manifestations significantly improved, and they had two consecutive nasopharyngeal swabs with negative results from nucleic acid tests (the sampling time was at least 24 hours apart).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables with a normal distribution and nonnormal distribution are summarized as the mean \u0026plusmn; standard deviation (SD) and the median (interquartile range, IQR), respectively. Categorical variables are expressed as frequencies or percentages. Differences between severe and mild cases were analyzed using the \u0026chi;2 \u003cem\u003etest\u003c/em\u003e, \u003cem\u003eMann-Whitney U\u003c/em\u003e\u003cem\u003etest\u003c/em\u003e or \u003cem\u003eFisher \u003c/em\u003eexact \u003cem\u003etest\u003c/em\u003e, where appropriate. Univariate and multivariate logistic regression models were used to identify the risk factors associated with the severity of COVID-19. All clinically important covariates and those with a significant correlation in the univariate analyses (\u003cem\u003eP\u0026lt;0.15\u003c/em\u003e) were included in the multivariate analysis. Survival curves were drawn and the differences in survival were assessed by using the Kaplan\u0026ndash;Meier method and log rank test. The cutoff value of the SOFA score was determined by receiver operating characteristic (ROC) curve analysis. Based on the cutoff value of the SOFA score, the patients were classified into 2 groups. The hazard ratios (HRs) with 95% confidence intervals for the risk factors for death were determined using Cox proportional hazards regression models. The Cox proportional hazards model was used to analyze the potential risk factors associated with the SOFA score for in-hospital death. All statistical analyses were carried out with SPSS (IBM, version 25.0). Graphs were plotted using the GraphPad Prism 8.0 (San Diego, CA, USA). A\u003cem\u003e P\u003c/em\u003e value of \u0026lt;0.05 was considered statistically significant, and all tests were two-tailed.\u003c/p\u003e"},{"header":"Results","content":" \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographic, baseline and clinical characteristics of patients with COVID-19\u003c/h2\u003e \u003cp\u003eWe analyzed 117 confirmed COVID-19 patients, including 60 males, accounting for 51.3%. More than half (61) of the patients were identified as critical patients, and 9 of them died. The majority of severe patients were men, accounting for 55.7%. The median age of the patients was 50.00\u0026nbsp;years (IQR, 35.50\u0026ndash;62.00\u0026nbsp;years), ranging from 18 to 83\u0026nbsp;years. The median age of seriously ill patients was significantly higher than that of mild patients [56.00 (IQR, 45.00\u0026ndash;68.00) vs 38.00 (IQR, 31.00-54.50)]. Sixty-three patients had complications, and hypertension (27.4%) was the most common complication, followed by diabetes (8.5%), stroke (4.3%), coronary heart disease (3.4%), chronic liver disease (CLD1) (3.4%) and so on. The proportion of hypertension in severe patients was higher than that in mild patients (42.6% vs 10.7%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). It is observed that on admission, fever (82.9%) and dry cough (70.1%) were the most common symptoms, followed by dyspnea (39.3%), fatigue (29.1%), and sore throat (9.4%). The median time from onset to admission of all patients was 5\u0026nbsp;days (IQR, 3\u0026ndash;7\u0026nbsp;days), no difference found between the mild and severe group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The median SOFA score of the patients was 2 (IQR, 1\u0026ndash;3), the minimum SOFA score was 0, and the maximum SOFA score was 16. The distribution of SOFA scores is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. The median SOFA score of severe patients was higher than that of mild patients [3 (IQR, 2\u0026ndash;4) vs 1 (IQR, 0\u0026ndash;1); P\u0026thinsp;\u0026lt;\u0026thinsp;0.001].\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\u003eDemographics and baseline characteristics of patients with COVID-19\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics and clinical characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll patients(n\u0026thinsp;=\u0026thinsp;117)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild(n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSevere(n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e 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\u003e50.00(35.50\u0026ndash;62.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(31.00-54.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.00(45.00\u0026ndash;68.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60, 51.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26, 46.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34, 55.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57, 48.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30, 53.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27, 44.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidity\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 \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\u003e32, 27.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6, 10.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26, 42.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\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\u003e10, 8.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 3.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8, 13.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4, 3.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 1.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3, 4.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5, 4.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 3.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3, 4.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3, 2.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3, 4.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic liver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4, 3.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3, 5.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1, 1.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1, 0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1, 1.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2, 1.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2, 3.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2, 1.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2, 3.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSigns and symptoms\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97, 82.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48, 85.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49, 80.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82, 70.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35, 62.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47, 77.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46, 39.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8, 14.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38, 62.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiarrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5, 4.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3, 5.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2, 3.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34, 29.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18, 32.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16, 26.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSore throat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11, 9.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7, 12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4, 6.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyalgia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5, 4.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3, 5.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2, 3.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSputum production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6, 5.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 3.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4, 6.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest temperature (℃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.40(37.75\u0026ndash;38.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.25(37.70\u0026ndash;38.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.50(37.80\u0026ndash;39.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOFA score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(2\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLowest SPO2(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94(90\u0026ndash;95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95(94\u0026ndash;95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90(86\u0026ndash;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDays from symptoms to hospital admission\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(3\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(3\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(3\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: COVID-19, Coronavirus disease 2019; SOFA score, Sequential Organ Failure Assessment score; SPO2, oxygen saturation; Values are medians (interquartile ranges) or number (percentages), where N is the total number of patients with available data. P values comparing severe and mild patients are from χ2, Fisher's exact test, or Mann-Whitney U test. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are considered statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory characteristics of the study population\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the laboratory test results of the patients after admission. The median lymphocyte count (LYM) of the patients was 0.860\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e9\u003c/sup\u003e/ml. The LYM in severe patients was lower than that in mild patients [0.640 (IQR, 0.420\u0026ndash;0.965) vs 1.035 (IQR, 0.690\u0026ndash;1.360); P\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. The hs-CRP, white blood cell (WBC), neutrophil (NE), and gamma-glutamyl transpeptidase (γ-GGT) levels in severe patients were higher than those in mild patients [16.830 (IQR, 4.730-43.665) vs 4.815 (IQR, 0.870-16.967); 9.990 (IQR, 6.525\u0026ndash;12.940) vs 6.185 (IQR, 4.400-9.175); 8.320 (IQR, 4.925\u0026ndash;11.900) vs 4.495 (IQR, 2.872\u0026ndash;7.247); 65.200 (IQR, 38.650\u0026ndash;132.200) vs 40.700 (IQR, 17.025\u0026ndash;65.475); all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. In addition, the PCT, direct bilirubin (DBIL), alanine aminotransferase (ALP), cystatin C, and creatine kinase muscle-brain isoform (CK-MB) levels of severe patients were higher than those of mild patients[0.080 (IQR, 0.050\u0026ndash;0.165) vs 0.040 (IQR, 0.030\u0026ndash;0.060); 4.300 (IQR, 2.800\u0026ndash;7.150) vs 3.200 (IQR, 2.325-4.400); 59.200 (IQR, 44.350\u0026ndash;79.750) vs 53.750 (IQR, 42.275\u0026ndash;63.300); 0.960 (IQR, 0.820\u0026ndash;1.110) vs 0.820 (IQR, 0.712\u0026ndash;0.947); 16.900 (IQR, 12.900\u0026ndash;23.300) vs 13.500 (IQR, 9.750-16.312); all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05], but the levels of severe patients were all within the normal range.\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\u003eLaboratory findings of patients with COVID-19\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory findings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAll patients(n\u0026thinsp;=\u0026thinsp;117)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMild(n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSevere(n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin, ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.060(0.0350\u0026ndash;0.110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040(0.030\u0026ndash;0.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.080(0.050\u0026ndash;0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6, pg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.489(8.685\u0026ndash;27.489)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.489(7.247\u0026ndash;27.489)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.700(8.685\u0026ndash;34.290)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cells, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u0026ndash;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.300(5.025\u0026ndash;11.270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.185(4.400-9.175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.990(6.525\u0026ndash;12.940)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u0026ndash;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.190(3.460\u0026ndash;9.910)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.495(2.872\u0026ndash;7.247)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.320(4.925\u0026ndash;11.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u0026ndash;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.860(0.560\u0026ndash;1.215)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.035(0.690\u0026ndash;1.360)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.640(0.420\u0026ndash;0.965)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026ndash;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.400(0.300\u0026ndash;0.530)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.400(0.292\u0026ndash;0.497)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.380(0.305\u0026ndash;0.535)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophil, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u0026ndash;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000(0.000-0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005(0.000-0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000(0.000-0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophil, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010(0.010\u0026ndash;0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010(0.010\u0026ndash;0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.020(0.010\u0026ndash;0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed blood cell, \u0026times;10\u003csup\u003e12\u003c/sup\u003e/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8\u0026ndash;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.214\u0026thinsp;\u0026plusmn;\u0026thinsp;0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.339\u0026thinsp;\u0026plusmn;\u0026thinsp;0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.099\u0026thinsp;\u0026plusmn;\u0026thinsp;0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115\u0026ndash;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134.000(118.500\u0026ndash;145.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e134.000(121.000-146.750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e133.000(116.000-141.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125\u0026ndash;350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e198.000(157.500\u0026ndash;259.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e194.500(156.000-231.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e217.000(159.000-279.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.760(2.560\u0026ndash;25.410)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.815(0.870-16.967)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.830(4.730-43.665)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.400(9.100\u0026ndash;16.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.000(8.650-13.925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.000(9.300-18.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect bilirubin, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.700(2.500\u0026ndash;5.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.200(2.325-4.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.300(2.800\u0026ndash;7.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect bilirubin, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.800(6.250\u0026ndash;10.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.700(6.000-9.875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.200(6.500-11.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine aminotransferase, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.400(28.700-117.450)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.600(22.050-104.475)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68.500(33.700-127.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartate aminotransferase, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.200(21.050\u0026ndash;47.650)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.200(19.075\u0026ndash;43.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.700(22.250-51.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlkaline phosphatase, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u0026ndash;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.800(42.900\u0026ndash;71.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.750(42.275\u0026ndash;63.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59.200(44.350\u0026ndash;79.750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGamma-glutamyl transpeptidase, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.600(28.500\u0026ndash;87.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.700(17.025\u0026ndash;65.475)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.200(38.650\u0026ndash;132.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea nitrogen, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u0026ndash;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.140(4.130\u0026ndash;6.515)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.620(3.790\u0026ndash;5.520)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.540(4.645\u0026ndash;7.665)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u0026ndash;73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.800(49.050\u0026ndash;67.700)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.250(49.250-68.175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.600(48.200\u0026ndash;66.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142\u0026ndash;339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e238.740\u0026thinsp;\u0026plusmn;\u0026thinsp;92.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e264.960\u0026thinsp;\u0026plusmn;\u0026thinsp;81.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e214.660\u0026thinsp;\u0026plusmn;\u0026thinsp;95.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystatin C, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u0026ndash;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.870(0.750\u0026ndash;1.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.820(0.712\u0026ndash;0.947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.960(0.820\u0026ndash;1.110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerular filtration rate, ml/min/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130.500(107.350-153.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e129.250(114.125-148.875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e132.000(103.000-161.450)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u0026ndash;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.340\u0026thinsp;\u0026plusmn;\u0026thinsp;0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.431\u0026thinsp;\u0026plusmn;\u0026thinsp;0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.257\u0026thinsp;\u0026plusmn;\u0026thinsp;0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137.0-147.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e140.182\u0026thinsp;\u0026plusmn;\u0026thinsp;3.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e141.094\u0026thinsp;\u0026plusmn;\u0026thinsp;2.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e139.344\u0026thinsp;\u0026plusmn;\u0026thinsp;3.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorine, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.0-110.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103.800(101.100-105.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e104.350(102.425-105.775)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102.200(100.600-104.800)\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\u003eCalcium, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11\u0026ndash;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.000(1.900-2.155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.025(1.940\u0026ndash;2.187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.980(1.850\u0026ndash;2.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u0026ndash;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.986\u0026thinsp;\u0026plusmn;\u0026thinsp;0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.988\u0026thinsp;\u0026plusmn;\u0026thinsp;0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.984\u0026thinsp;\u0026plusmn;\u0026thinsp;0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026ndash;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.107\u0026thinsp;\u0026plusmn;\u0026thinsp;0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.183\u0026thinsp;\u0026plusmn;\u0026thinsp;0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.036\u0026thinsp;\u0026plusmn;\u0026thinsp;0.277\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\u003eCK, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.800(48.500-109.925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.600(45.175-105.331)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e72.900(49.400\u0026ndash;124.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK-MB, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.400(10.500-18.775)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.500(9.750-16.312)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.900(12.900\u0026ndash;23.300)\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\u003ecTnI, ug/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010(0.010\u0026ndash;0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010(0.010\u0026ndash;0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010(0.010\u0026ndash;0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyoglobin, ug/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026ndash;70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.000(18.900-54.750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.250(17.475\u0026ndash;42.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.650(24.300\u0026ndash;55.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: COVID-19, Coronavirus disease 2019; IL-6, interleukin-6; hs-CRP, high-sensitivity C-reactive protein; CK, creatine kinase; CK-MB, creatine kinase muscle-brain isoform; cTnI, cardiac troponin I; Data are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, or median (IQR). P values comparing severe and mild patients are from Fisher's exact test or Mann-Whitney U test. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are considered statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of risk factors for severe COVID-19\u003c/h2\u003e \u003cp\u003eTo determine the risk factors for the bad prognosis of patients with severe disease, we compared the clinical and laboratory characteristics of mild and severe patients. Univariate logistic regression analysis showed that age, hypertension, SOFA score, and the levels of IL-6, WBC, NE, LYM, red blood cell (RBC), hs-CRP, DBIL, ALP, GGT, urea nitrogen, uric acid, cystatin C, sodium (Na), chlorine (Cl), phosphorus (P) and CK-MB were related to the aggravation of the patient's condition. Multivariable logistic regression analysis showed that SOFA score, advanced age, and hypertension were independently associated with the risk of severe COVID-19. In addition, the levels of WBC, NE, LYM, ALP, P, urea nitrogen, cystatin C and CK-MB were also related with severe COVID-19.Univariate analysis showed that SOFA score was a risk factor for patients with severe COVID-19, with an odds ratio of 5.328 (95% CI: 2.932\u0026ndash;9.681; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariate analysis also revealed that SOFA score was a risk factor for patients with severe COVID-19 (OR\u0026thinsp;=\u0026thinsp;5.851; 95% CI: 3.044\u0026ndash;11.245; \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk factors associated with severe COVID-19 patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariable OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultivariable OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e 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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.064(1.034\u0026ndash;1.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.069(1.036\u0026ndash;1.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.190(2.307\u0026ndash;16.614)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.310(1.705\u0026ndash;31.350)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.075(0.827\u0026ndash;20.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.328(2.932\u0026ndash;9.681)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.851(3.044\u0026ndash;11.245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.021(1.002\u0026ndash;1.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.196(1.078\u0026ndash;1.327)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.195(1.060\u0026ndash;1.346)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.232(1.107\u0026ndash;1.371)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.210(1.084\u0026ndash;1.351)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.222(0.094\u0026ndash;0.526)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.280(0.107\u0026ndash;0.730)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed blood cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.520(0.278\u0026ndash;0.973)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.026(1.008\u0026ndash;1.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.207(1.016\u0026ndash;1.434)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlkaline phosphatase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.020(1.002\u0026ndash;1.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.032(1.007\u0026ndash;1.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGamma-glutamyl transpeptidase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.006(1.001\u0026ndash;1.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.484(1.173\u0026ndash;1.876)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.480(1.154\u0026ndash;1.898)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.993(0.989\u0026ndash;0.998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystatin C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.239(4.316\u0026ndash;255.970)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.893(2.988-319.403)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.819(0.715\u0026ndash;0.937)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.844(0.743\u0026ndash;0.960)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.068(0.012\u0026ndash;0.380)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055(0.007\u0026ndash;0.462)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK-MB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.092(1.027\u0026ndash;1.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.086(1.017\u0026ndash;1.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.689(0.710-45.575)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.980(0.960-1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.004(0.999\u0026ndash;1.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.057(0.995\u0026ndash;1.123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect bilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.068(0.981\u0026ndash;1.163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.608(0.325\u0026ndash;1.139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.220(0.032\u0026ndash;1.485)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.004(0.999\u0026ndash;1.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: COVID-19, Coronavirus disease 2019; SOFA score, Sequential Organ Failure Assessment score; IL-6, interleukin-6; hs-CRP, high-sensitivity C-reactive protein; CK-MB, creatine kinase muscle-brain isoform; OR, odds ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eROC curves and cumulative survival curves for predicting the severity and prognosis of COVID-19\u003c/h2\u003e \u003cp\u003eROC curves were constructed to evaluate the predictive value of the SOFA score for the severity and prognosis of COVID-19 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eB \u003cem\u003eand\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic accuracy of the SOFA score for predicting severe COVID-19 [cutoff value\u0026thinsp;=\u0026thinsp;2; AUC\u0026thinsp;=\u0026thinsp;0.908 (95% CI: 0.857\u0026ndash;0.960); sensitivity: 85.20%; specificity: 80.40%] and the risk of death in COVID-19 patients [cutoff value\u0026thinsp;=\u0026thinsp;5; AUC\u0026thinsp;=\u0026thinsp;0.995 (95% CI: 0.985-1.000); sensitivity: 100.00%; specificity: 95.40%]. Kaplan-Meier curve analysis and the log-rank test were performed to assess the cumulative survival rates and compare the 60-day survival curves between the high SOFA score group (SOFA score\u0026thinsp;\u0026ge;\u0026thinsp;5) and the low SOFA score group (SOFA score\u0026thinsp;\u0026lt;\u0026thinsp;5). Patients in the high SOFA score group had a significantly higher risk of death than those in the low SOFA score group (\u003cem\u003elog-rank, P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eResults of Cox proportional hazards regression analysis\u003c/h2\u003e \u003cp\u003eCox proportional hazards regression analysis was used to assess the potential association between the SOFA score and hospital death. The univariate analysis indicated that the SOFA score was associated with a higher risk of hospital death (HR\u0026thinsp;=\u0026thinsp;1.279, 95% CI: 1.123\u0026ndash;1.456, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, patient age, lowest SPO2, hypertension, CLD1, chronic lung disease (CLD2), chronic kidney disease (CKD), and the levels of RBC, hemoglobin (Hb), hs-CRP, total bilirubin (TBIL), DBIL, aspartate aminotransferase (AST), ALP, urea nitrogen, creatinine, cystatin C, cardiac troponin I (cTnI) and myoglobin were associated with the risk of death in the hospital (\u003cem\u003eTable\u0026nbsp;4\u003c/em\u003e). Multivariate Cox proportional hazards regression analysis was used to evaluate the independent prognostic effect of the SOFA score. After adjusting for CLD2, CKD, and CLD1 (model 1), the HR of the SOFA score for predicting hospital deaths was 1.405 (95% CI: 1.132\u0026ndash;1.744, \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.002\u003c/em\u003e). After adjusting for age, CLD1, and CLD2 (model 2), the HR was 1.336 (95% CI: 1.069\u0026ndash;1.670, \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.011\u003c/em\u003e). After adjusting for hypertension, CLD2, and CKD (model 3), the HR was 1.292 (95% CI: 1.090\u0026ndash;1.532, \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.003\u003c/em\u003e). After adjusting for cystatin C (model 4), the HR was 1.276 (95% CI: 1.083\u0026ndash;1.504, \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.004\u003c/em\u003e). After adjusting for LYM, hs-CRP, creatine kinase (CK), and CK-MB (model 5), the HR was 1.341 (95% CI: 1.045\u0026ndash;1.721, \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.021\u003c/em\u003e). After adjusting for CK-MB (model 6), the HR was 1.270 (95% CI: 1.096\u0026ndash;1.472, \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.001\u003c/em\u003e). After adjusting for Na (model 7), the HR was 1.320 (95% CI: 1.127\u0026ndash;1.546, \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.001\u003c/em\u003e). In this process, age, CLD1, CKD, CLD2, cystatin C, hs-CRP, CK and CK-MB also showed significance for independently predicting hospital death, while Na showed a protective effect (\u003cem\u003eTable\u0026nbsp;5\u003c/em\u003e). Forest plots depicting the results of the multivariate analysis of each SOFA score model assessed by the Cox proportional hazards regression model are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003cstrong\u003eTable 4 Results of univariate Cox proportional-hazards regression\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eanalyzing the effect of variables on in hospital death\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"774\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e\u003cstrong\u003eRisk factors\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e1.101（1.026-1.181）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eSOFA score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e1.279（1.123-1.456）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eLowest SPO2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.910（0.872-0.950）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e6.083（1.173-31.540）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.032\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eChronic lung disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e13.079（1.450-117.959）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eChronic kidney disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e24.937（2.768-224.666）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eChronic liver disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e12.467（1.270-122.355）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.030\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eRed blood cell\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.141（0.045-0.441）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eHemoglobin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.942（0.909-0.976）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003ehs-CRP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e1.020（1.004-1.037）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eTotal bilirubin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e1.022（1.004-1.041）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eDirect bilirubin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e1.034（1.008-1.062）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eAspartate aminotransferase\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e1.006（1.002-1.011）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eAlkaline phosphatase\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e1.007（1.002-1.011）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eUrea nitrogen\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e1.318（1.172-1.483）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eCreatinine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e1.006（1.003-1.009）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eCystatin C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e2.400（1.618-3.558）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eSodium\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.779（0.626-0.969）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eCK\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e1.007（1.003-1.011）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eCK-MB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e1.040（1.017-1.064）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003ecTnI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e1.072（1.022-1.125）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eMyoglobin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e1.007（1.004-1.010）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eHighest temperature\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.449（0.194-1.041）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.062\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eFever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.212（0.043-1.051）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.058\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eDyspnea\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e6.088（0.692-53.598）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.104\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eLymphocytes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.128（0.009-1.904）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.136\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eEosinophil\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e55.502（0.613-5029.250）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.081\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003ePlatelets\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.990（0.978-1.002）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.103\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003eIndirect bilirubin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e1.060（0.997-1.127）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.064\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SOFA score, Sequential Organ Failure Assessment score; SPO2, oxygen saturation; hs-CRP, high-sensitivity C-reactive protein; CK, creatine kinase; CK-MB, creatine kinase muscle-brain isoform; cTnI, cardiac troponin\u0026nbsp;I; HR, hazard ratio; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 Results of multivariate Cox proportional-hazards regression analyzing the effect of variables on in hospital death\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"461\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e\u003cstrong\u003eRisk factors\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eNot Adjusted SOFA score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e1.279（1.123-1.456）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eMode 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eSOFA score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e1.405(1.132-1.744)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eChronic lung disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e93.516（4.063-2152.432）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eChronic kidney disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e10.216（0.957-109.050）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.054\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eChronic liver disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e69.136（3.226-1481.626）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eMode 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eSOFA score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e1.336（1.069-1.670）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e1.152（1.018-1.302）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eChronic lung disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e43.406（1.869-1008.004）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eChronic liver disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e1515.310（10.430-220156.492）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eMode 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eSOFA score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e1.292（1.090-1.532）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e5.995（0.655-54.887）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.113\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eChronic lung disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e16.956（1.197-240.105）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.036\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eChronic kidney disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e21.147（1.224-365.495）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.036\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eMode 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eSOFA score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e1.276（1.083-1.504）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eCystatin C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e1.913（1.304-2.806）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eMode 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eSOFA score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e1.341（1.045-1.721）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.021\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eLymphocytes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e22.140（0.756-647.954）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.072\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003ehs-CRP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e1.035（1.001-1.071）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.043\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eCK\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e1.007（1.002-1.012）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eCK-MB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e1.055（1.006-1.107）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.027\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eMode 6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eSOFA score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e1.270（1.096-1.472）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eCK-MB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e1.034（1.007-1.061）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eMode 7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eSOFA score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e1.320（1.127-1.546）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eSodium\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"192\"\u003e\n\u003cp\u003e0.775（0.623-0.964）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SOFA score, Sequential Organ Failure Assessment score; hs-CRP, high-sensitivity C-reactive protein; CK, creatine kinase; CK-MB, creatine kinase muscle-brain isoform; HR, hazard ratio; CI, confidence interval.\u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eSevere COVID-19 can easily cause ARDS, multiple organ dysfunction, acute heart injury, acute kidney injury and even death \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Identifying the death risk associated with critically ill patients early and giving these patients priority treatment in a timely manner are particularly important in global health emergencies. Studies have shown that a total of 60 predictors can assess the severity of COVID-19, of which 7 factors are considered to be highly correlated and consistent, including SOFA score, age, D-dimer, hs-CRP, body temperature, albumin and diabetes \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The results of this study revealed that SOFA score, age, CKD, CLD1, CLD2, cystatin C, hs-CRP, CK, CK-MB and other factors were independent risk factors for in-hospital death, which was similar to the results of the above study.\u003c/p\u003e \u003cp\u003eZhou et al. showed that older age, higher D-dimer levels and higher SOFA scores in COVID-19 patients at admission were associated with high in-hospital mortality. In addition, increased levels of cTnI, lactate dehydrogenase and lymphocytopenia were more common in patients with severe COVID-19 \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Also, studies have shown that SARS-CoV-2 can participate in and induce the activation of complement and coagulation system, which is related to the severity of COVID-19 patients \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Myocardial injury was another an independent risk factor for deterioration and death in patients with COVID-19. The risk of death of hospitalized patients with myocardial injury was 6.6\u0026ndash;26.9 times higher than that of patients without myocardial injury \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, the median age of seriously ill patients was 56.00 years, which was significantly higher than that of mild patients (38\u0026nbsp;years). Hypertension was the most common complication of COVID-19, especially in severe patients. A review of the literature showed that COVID-19 patients with hypertension, especially elderly patients, had a 2.5-fold increased risk of serious or even fatal events \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Another large cohort study showed that in addition to some factors, such as advanced age, male, asthma, diabetes, increased the risk of death in COVID-19 patients, poverty and ethnicity (Blacks and South Asians) were also associated with death of COVID-19 patients \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt present, published studies have not systematically evaluated the accuracy of the SOFA score in the diagnosis of COVID-19 severity and its predictive value. The SOFA score was originally used to assess the severity of organ dysfunction in patients with severe sepsis and has been validated in ICU patients in multiple regions \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. As critically ill patients usually have multiple organ dysfunction, the SOFA score has been widely used to predict the clinical outcomes of critically ill patients, such as predicting mortality in patients with chronic liver failure and hematological malignancies \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Gupta et al. summarized the clinical characteristics of SARS-CoV-2 infection, which could not only cause severe lung injury, but also damaged the heart, liver, kidney, nervous system, endocrine system, blood system and skin, resulting in arrhythmia, acute coronary syndrome, thrombosis, gastrointestinal symptoms, hyperglycemia and skin rash \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Thus, the SOFA score can comprehensively assess multiple organ dysfunction caused by SARS-CoV-2.\u003c/p\u003e \u003cp\u003eIn our study, the SOFA score was also recognized as a valuable prognostic tool for the outcome of patients with COVID-19. Univariate regression analysis showed that the increase in SOFA score and IL-6 and the decrease in lymphocyte count were related to the aggravation of the patient's condition. Multivariable regression analysis demonstrated that SOFA score, advanced age, and hypertension were independently associated with the risk of severe COVID-19. At present, it is believed that COVID-19 leads to organ failure, which is mainly related to cytokine storm and immunosuppression; the clinical manifestations are persistent fever, hemocytopenia and organ involvement \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. The laboratory results were characterized by increased levels of inflammatory factors such as granulocyte colony-stimulating factor (G-CSF), interleukin-2 (IL-2), IL-6, interleukin-7 (IL-7), interferon-γ\u0026ndash;inducible protein-10 (IP-10), tumor necrosis factor α (TNF-α), macrophage inflammatory protein-1 α (MIP-1 α) and monocyte chemoattractant protein 1 (MCP-1) \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. The analysis of the immune system of patients with severe COVID-19 showed that the number of innate immune cells increased, while T cells decreased. In COVID-19 patients, the early increase of cytokines was positively correlated with poor prognosis \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. This may well explain why only dexamethasone has been found to reduce mortality in many clinical trials for COIVD-19 \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Therefore, the SOFA score can reflect not only multiple organ failure but also the degree of inflammation and can accurately predict the severity of the patient\u0026rsquo;s disease.\u003c/p\u003e \u003cp\u003eSepsis is a life-threatening organ dysfunction, caused by dysregulated host response to infection. Rapid change in SOFA score\u0026thinsp;\u0026ge;\u0026thinsp;2 points after infection is regarded as the clinical criterion of sepsis-associated organ dysfunction. The SOFA score\u0026thinsp;\u0026ge;\u0026thinsp;2 reflects approximately 10% of the overall risk of death of suspected infected patients in general hospitals, and even patients with moderate organ dysfunction may further deteriorate. Therefore, it emphasizes the seriousness of this situation and reminds clinicians to intervene in a timely and appropriate manner \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In this study, the area under the ROC curve (AUC) of the SOFA score was 0.908 (95% CI: 0.857\u0026ndash;0.960) with a diagnostic cut-off value of 2 and a sensitivity and specificity of 85.20% and 80.40%, respectively. This result suggests that a SOFA score\u0026thinsp;\u0026ge;\u0026thinsp;2 can predict the severity of COVID-19 patients. Another study also showed that among 184,875 patients admitted to the ICU, an increase of 2 or more in the SOFA score had greater prognostic accuracy for in-hospital mortality than quick SOFA (qSOFA) score or the systemic inflammatory response syndrome standard \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. When the cut-off value of the optimal SOFA score is 5 (AUC: 0.995, 95% CI: 0.985-1.000, sensitivity: 100.00%, specificity: 95.40%), the risk of mortality in patients with COVID-19 can be predicted. Regarding the 60-day mortality rate of patients in the high and low SOFA score groups, patients in the high SOFA score group (SOFA score\u0026thinsp;\u0026ge;\u0026thinsp;5) had a significantly higher risk of death than those in the low SOFA score group (SOFA score\u0026thinsp;\u0026lt;\u0026thinsp;5). Wang et al. used the SOFA score to assess the predictive value of early sepsis and 30-day mortality after liver transplantation, indicating that the survival rate of patients with SOFA score\u0026thinsp;\u0026gt;\u0026thinsp;5 within 1\u0026ndash;7 days after liver transplantation was significantly lower than that of patients with SOFA score\u0026thinsp;\u0026le;\u0026thinsp;5 \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Therefore, SOFA score\u0026thinsp;\u0026ge;\u0026thinsp;5 can be used as a good predictor of hospital mortality in COVID-19 patients. In addition, univariate and multivariate Cox proportional hazards regression analyses demonstrated that there was a high correlation between the SOFA score and hospital mortality, and the SOFA score was a risk factor for death in COVID-19 patients. These results provide strong evidence for priority in treatment and early special care for patients.\u003c/p\u003e \u003cp\u003eNevertheless, some limitations should be considered when interpreting the results of this study. First, this is a single center retrospective study involving a relatively small number of patients. Second, our study was limited by its retroactive design, which resulted in some data unavailable in the electronic medical records. In some cases, if the patient\u0026rsquo;s condition was stable during hospitalization without dyspnea and hypoxia, blood gas analysis was not performed, so the SOFA score could not be calculated accurately and had to be estimated by the EM algorithm. However, in our study, the data loss rate of this variable was less than 25%. Finally, the retrospective nature of our study may lead to selection bias, and the findings need to be verified and refined by future prospective studies.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eThe world is currently in the midst of a pandemic of COVID-19. The impact of the COVID-19 epidemic on human health,modern society and global economy is self-evident. At present, there is no specific drug for anti-viral treatment of COVID-19. Clinically, respiratory support and symptomatic treatment are mainly used in patients with COVID-19, while the related prophylactic vaccine is still in clinical trial. Given this background, a simple and practical tool for predicting the prognosis of patients with COVID-19 is particularly important. It will help hospitals improve their priority setting, and, based on this, special care will be provided for critically ill patients to reduce economic burden to patients and society. In conclusion, the SOFA score may be an independent risk factor for hospital death and can be well used to assess the severity and prognosis of COVID-19.\u003c/p\u003e "},{"header":"Abbreviations","content":" \u003cp\u003eCOVID-19: Coronavirus disease 2019; SOFA: Sequential Organ Failure Assessment; ROC: Receiver operating characteristic; AUC: area under the ROC curve; WHO: World Health Organization; SARS-CoV-2:severe acute respiratory syndrome coronavirus-2; FiO2: oxygen (PaO2)/oxygen concentration; ARDS: acute respiratory distress syndrome; ICU: intensive care unit; SD: standard deviation; IQR: interquartile range; HRs: hazard ratios; HR: hazard ratio; qSOFA: quick SOFA; G-CSF: granulocyte colony-stimulating factor; IL-2:interleukin-2; IL-6: interleukin-6; IL-7 : interleukin-7; IP-10: interferon-γ\u0026ndash;inducible protein-10; TNF-α: tumor necrosis factor α; MIP-1 α: macrophage inflammatory protein-1 α; MCP-1: monocyte chemoattractant protein 1; SPO2: oxygen saturation; hs-CRP: high-sensitivity C-reactive protein; CK: creatine kinase; CK-MB: creatine kinase muscle-brain isoform; cTnI: cardiac troponin I; OR: odds ratio; CI: confidence interval; CLD1: Chronic liver disease; CLD2: Chronic lung disease; CKD: Chronic kidney disease; HP: Hypertension; LYM: Lymphocytes; Hb: hemoglobin; WBC: white blood cell; NE: neutrophil; γ-GGT: gamma-glutamyl transpeptidase; DBIL: direct bilirubin; ALP: alanine aminotransferase; PCT: procalcitonin; RBC: red blood cell; Na: sodium; Cl: chlorine; P: phosphorus\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all patients and medical staff at Jingzhou Central Hospital who were involved in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZheng Yang, Qinming Hu and Yi Sun conceived the study idea, and performed interpretation, manuscript writing and final approval. Fei Huang, and Shouxin Xiong performed data analysis and collection. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Hubei Province Health and Family Planning Scientific Research Project (WJ2018H178 to Yi Sun) and the Natural Science Foundation of Hubei Province (2019CFB567 to Yi Sun).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of supporting data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the finalization of the clinical study report, the data analyzed during the current study are not publicly available, but can be obtained from the correspondent author under reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was reviewed and approved for publication by the Institutional Review Board of Jinghzou Central Hospital, and the requirement for informed consent from the study participants was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e \u003cspan\u003eWorld Health Organization. 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Aliment Pharm Ther. 2014;40(9):1056\u0026ndash;65.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eDemandt AM, Geerse DA, Janssen BJ, et al. The prognostic value of a trend in modified SOFA score for patients with hematological malignancies in the intensive care unit. Eur J Haematol. 2017;99(4):315\u0026ndash;22.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eGupta A, Madhavan MV, Sehgal K, et al. Extrapulmonary manifestations of COVID-19. Nat Med. 2020;26(7):1017\u0026ndash;32.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMehta P, McAuley DF, Brown M, et al. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet (London, England).2020; 395(10229):1033.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eHuang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan. China The lancet. 2020;395(10223):497\u0026ndash;506.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eRuan Q, Yang K, Wang W, et al. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intens Care Med. 2020;46(5):846\u0026ndash;48.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLucas C, Wong P, Klein J, et al. Longitudinal analyses reveal immunological misfiring in severe COVID-19. Nature.2020:1\u0026ndash;09.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eRecovery Collaborative Group. Dexamethasone in hospitalized patients with Covid-19\u0026mdash;preliminary report. New Engl J Med.2020.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSinger M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). Jama.2016; 315(8):801\u0026ndash;10.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eRaith EP, Udy AA, Bailey M, et al. Prognostic accuracy of the SOFA score, SIRS criteria, and qSOFA score for in-hospital mortality among adults with suspected infection admitted to the intensive care unit. Jama. 2017;317(3):290\u0026ndash;300.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eWang X, Niu X, Li J, et al. SOFA Score can effectively predict the incidence of sepsis and 30-day mortality in liver transplant patients: a retrospective study. Adv Ther. 2019;36(3):645\u0026ndash;51.\u003c/span\u003e \u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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, SOFA score, SARS-CoV-2, severity, prognosis, ARDS","lastPublishedDoi":"10.21203/rs.3.rs-76663/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-76663/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePatients with Coronavirus disease 2019 (COVID-19) have a high mortality rate, and thus, it is particularly important to predict the severity and prognosis of COVID-19. The Sequential Organ Failure Assessment (SOFA) score has been used to predict the clinical outcomes of patients with multiple organ failure requiring intensive care. Therefore, we retrospectively analyzed the clinical characteristics, risk factors, and relationship between the SOFA score and the prognosis of COVID-19 patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eClinical variables were compared between patients with mild and severe COVID-19. Univariate and multivariate logistic regression analyses were performed to identify the risk factors for severe COVID-19. The Cox proportional hazards model was used to analyze risk factors for hospital-related death. Survival analysis was performed by the Kaplan-Meier method, and survival differences were assessed by the log-rank test. Receiver operating characteristic (ROC) curves of the SOFA score in different situations were drawn, and the area under the ROC curve was calculated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe median SOFA score of all patients was 2 (IQR, 1\u0026ndash;3). Patients with severe COVID-19 exhibited a significantly higher SOFA score than patients with mild COVID-19 [3 (IQR, 2\u0026ndash;4) vs 1 (IQR, 0\u0026ndash;1); \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001]. The SOFA score increased the risk of severe COVID-19, with an odds ratio of 5.851 (95% CI: 3.044\u0026ndash;11.245; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The area under the ROC curve (AUC) was used to evaluate the diagnostic accuracy of the SOFA score in predicting severe COVID-19 [cutoff value\u0026thinsp;=\u0026thinsp;2; AUC\u0026thinsp;=\u0026thinsp;0.908 (95% CI: 0.857\u0026ndash;0.960); sensitivity: 85.20%; specificity: 80.40%] and the risk of death in COVID-19 patients [cutoff value\u0026thinsp;=\u0026thinsp;5; AUC\u0026thinsp;=\u0026thinsp;0.995 (95% CI: 0.985-1.000); sensitivity: 100.00%; specificity: 95.40%]. Regarding the 60-day mortality rates of patients in the two groups classified by the optimal cutoff value of the SOFA score (5), patients in the high SOFA score group (SOFA score\u0026thinsp;\u0026ge;\u0026thinsp;5) had a significantly greater risk of death than those in the low SOFA score group (SOFA score\u0026thinsp;\u0026lt;\u0026thinsp;5).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe SOFA score could be used to evaluate the severity and 60-day mortality of COVID-19. The SOFA score may be an independent risk factor for in-hospital death.\u003c/p\u003e","manuscriptTitle":"The prognostic value of the SOFA score in patients with COVID-19: a retrospective, observational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-10-26 13:19:19","doi":"10.21203/rs.3.rs-76663/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":"880208d4-5dea-4bb0-9876-598147df24c8","owner":[],"postedDate":"October 26th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":876081,"name":"Infectious Diseases"}],"tags":[],"updatedAt":"2021-08-16T22:53:46+00:00","versionOfRecord":{"articleIdentity":"rs-76663","link":"https://doi.org/10.1097/MD.0000000000026900","journal":{"identity":"medicine","isVorOnly":true,"title":"Medicine"},"publishedOn":"2021-08-13 22:53:46","publishedOnDateReadable":"August 13th, 2021"},"versionCreatedAt":"2020-10-26 13:19:19","video":"","vorDoi":"10.1097/MD.0000000000026900","vorDoiUrl":"https://doi.org/10.1097/MD.0000000000026900","workflowStages":[]},"version":"v1","identity":"rs-76663","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-76663","identity":"rs-76663","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}