==== Front Clin Cardiol Clin Cardiol 10.1002/(ISSN)1932-8737 CLC Clinical Cardiology 0160-9289 1932-8737 Wiley Periodicals, Inc. New York 33094522 10.1002/clc.23492 CLC23492 Clinical Investigations Clinical Investigations Risk factors and electrocardiogram characteristics for mortality in critical inpatients with COVID‐19 Li et al.Li Lingzhi 1 2 Zhang Shudi 3 He Bing 2 Chen Xiaobei 4 Wang Shihong 2 Zhao Qingyan https://orcid.org/0000-0002-9610-2452 1 5 6 ruyan71@163.com 1 Department of Cardiology Renmin Hospital of Wuhan University Wuhan China 2 Department of Pediatrics Renmin Hospital of Wuhan University Wuhan China 3 Department of Neurosurgery Renmin Hospital of Wuhan University Wuhan China 4 Department of Infectious Diseases Renmin Hospital of Wuhan University Wuhan China 5 Cardiovascular Research Institute Wuhan University Wuhan China 6 Hubei Key Laboratory of Cardiology Wuhan University Wuhan China * Correspondence Prof. Qingyan Zhao, M.D. Ph.D., Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, China. Email: ruyan71@163.com 22 10 2020 12 2020 43 12 10.1002/clc.v43.121624 1630 26 7 2020 12 10 2020 14 10 2020 © 2020 The Authors. Clinical Cardiology published by Wiley Periodicals LLC.This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Abstract Background The novel severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has spread worldwide. Hypothesis The possible risk factors that lead to death in critical inpatients with coronavirus disease 2019 (COVID‐19) are not yet fully understood. Methods In this single‐center, retrospective study, we enrolled 113 critical patients with COVID‐19 from Renmin Hospital of Wuhan University between February 1, 2020 and March 15, 2020. Patients who survived or died were compared. Results A total of 113 critical patients with COVID‐19 were recruited; 50 (44.3%) died, and 63 (55.7%) recovered. The proportion of patients with ventricular arrhythmia was higher in the death group than in the recovery group (P = .021) and was higher among patients with myocardial damage than patients without myocardial damage (P = .013). Multivariate analysis confirmed independent predictors of mortality from COVID‐19: age > 70 years (HR 1.84, 95% CI 1.03‐3.28), initial neutrophil count over 6.5 × 109/L (HR 3.43, 95% CI 1.84‐6.40), C‐reactive protein greater than 100 mg/L (HR 1.93, 95% CI 1.04‐3.59), and lactate dehydrogenase over 300 U/L (HR 2.90, 95% CI 1.26‐6.67). Immunoglobulin treatment (HR 0.39, 95% CI 0.21‐0.73) can reduce the risk of death. Sinus tachycardia (HR 2.94, 95% CI 1.16‐7.46) and ventricular arrhythmia (HR 2.79, 95% CI 1.11‐7.04) were independent ECG risk factors for mortality from COVID‐19. Conclusions Old age (>70 years), neutrophilia, C‐reactive protein greater than 100 mg/L and lactate dehydrogenase over 300 U/L are high‐risk factors for mortality in critical patients with COVID‐19. Sinus tachycardia and ventricular arrhythmia are independent ECG risk factors for mortality from COVID‐19. arrhythmiaCOVID‐19critical typemortalityrisk factorsNational Natural Science Foundation of China 10.13039/50110000180981670303 and 81970277 source-schema-version-number2.0cover-dateDecember 2020details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:5.9.5 mode:remove_FC converted:09.12.2020 Li L , Zhang S , He B , Chen X , Wang S , Zhao Q . Risk factors and electrocardiogram characteristics for mortality in critical inpatients with COVID‐19 . Clin Cardiol . 2020 ;43 :1624 –1630 . 10.1002/clc.23492 Lingzhi Li and Shudi Zhang contributed equally to this study Funding information National Natural Science Foundation of China, Grant/Award Number: 81670303 and 81970277 ==== Body Abbreviations ARDSacute respiratory distress syndrome BNPB‐type natriuretic peptide COPDchronic obstructive pulmonary disease COVID‐19coronavirus disease 2019 ECGelectrocardiogram IMVinvasive mechanical ventilation MOFmultiple organ failure NIVnoninvasive ventilation NPnucleocapsid protein ORFgene and open reading frame SARS‐CoV‐2novel severe acute respiratory syndrome coronavirus 2 1 INTRODUCTION At present, there are outbreaks of the novel severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), and this virus has spread throughout the world. Common clinical manifestations in patients include fever, nonproductive cough, dyspnea, myalgia, fatigue, normal or decreased leukocyte counts, and radiographic evidence of pneumonia. 1 Although most patients are thought to have a favorable prognosis, older patients, and those with chronic underlying conditions may have worse outcomes. Patients with severe illness may develop dyspnea and hypoxemia within 1 week after the onset of the disease, which may quickly progress to acute respiratory distress syndrome (ARDS) or multiple organ failure (MOF). 2 A previous study reported that the mortality was 62% among critically ill patients with coronavirus disease 2019 (COVID‐19) in Wuhan. 3 However, possible risk factors leading to poor clinical outcomes have not been well delineated. Fever, hypoxemia, and myocardial injury caused by COVID‐19 can easily cause arrhythmia. 4 , 5 In a cohort of patients with COVID‐19, Wang observed that arrhythmias were present in 16.7% of patients. 6 However, due to the lack of strict epidemiological investigations, the occurrence, and outcomes of arrhythmia in patients with COVID‐19 are still unclear. In the present study, we aimed to investigate risk factors and electrocardiogram (ECG) characteristics for mortality in critical inpatients with COVID‐19. 2 METHODS 2.1 Patient selection This single‐center, retrospective, observational study was performed at Renmin Hospital of Wuhan University (Wuhan, China). A total of 113 critical inpatients with COVID‐19 from February 1, 2020 to March 15, 2020 were enrolled. All patients were confirmed to have COVID‐19 by performing RT‐PCR on samples from the respiratory tract. The diagnosis of COVID‐19 was based on the WHO interim guidelines. 7 All patients met the clinical criteria for critical‐type COVID‐19. Critical‐type COVID‐19 was defined based on the New Coronavirus Pneumonia Prevention and Control Program in China (sixth edition). 8 Patients who met one of the following criteria were considered to have critical‐type COVID‐19: respiratory failure requiring mechanical ventilation; shock state; and patients with other types of organ failure that need to be monitored in the ICU. This study was approved by the Institutional Ethics Committee of Renmin Hospital of Wuhan University. 2.2 Data collection Demographic characteristics, clinical records, laboratory data, ECG characteristics, treatments, and outcome data were obtained with data collection forms from electronic medical records. Two experienced clinicians entered and reviewed the data. Recorded information included demographic data, medical history, symptoms and signs, chronic diseases, laboratory findings, ECG data, and treatment measures. The date of disease onset was defined as the day when the symptom was noticed. The criteria for a confirmed diagnosis of SARS‐CoV‐2 were that at least one gene site was amplified and considered positive for the nucleocapsid protein (NP) gene and open reading frame (ORF) gene. 9 Myocardial injury was defined as blood levels of cardiac biomarkers (hs‐TnI) above the 99th‐percentile upper reference limit, regardless of new abnormalities in electrocardiography and echocardiography. 4 QT prolongation was defined as an absolute QTc interval > 500 ms (or a JTc interval > 410 ms to adjust for patients with QRS duration >120 ms). 10 2.3 Outcomes The outcomes were death or discharge. Discharge standards were defined according to the guidance of the New Coronavirus Pneumonia Prevention and Control Program in China (sixth edition). 8 2.4 Statistical analysis Categorical variables are expressed as numbers (%) and were compared by Pearson's Chi‐square test or Fisher's exact test. Univariate analysis was used to evaluate demographics and clinical factors associated with COVID‐19 mortality. We used Kaplan‐Meier survival analysis to estimate the patient survival fraction and the stratified log‐rank test to compare the difference in survival distributions between different groups. Time to events (death) was defined as the time from illness onset to events. Two groups were created, 'death' and 'recovery', to study the relationship between high‐risk factors and mortality from COVID‐19 using hazard ratios (HRs) generated by a Cox proportional hazards regression model. A forward selection procedure was then used to construct an initial model. Based on clinical experience, a final model was selected. Proportional hazards assumptions were systematically verified for the proposed models. Hypothesis testing was conducted using a two‐sided test, and an alpha value of 0.05 indicated statistical significance. A forest plot was created based on multivariate Cox regression results. All analyses were performed using the SPSS (version 20.0) and GraphPad Prism 8.0. 3 RESULTS 3.1 Baseline characteristics A total of 113 patients with critical COVID‐19 were included in this study; 68 were male, and 45 were female. The mean age was 67.3 ± 14.1 years, ranging from 29 to 95 years. 50 patients (44.3%) died, and 63 (55.7%) recovered before March 15, 2020. The median length of stay was 17 days (IQR, 5‐28 days). The median length of stay of the death group was 4 days (IQR, 3‐7 days), and in the recovery group, the median length of stay was 26 days (IQR, 22‐39 days). Baseline characteristics of the 113 confirmed cases are shown in (Supplemental Table S1). There was a significant difference among age groups (>70 years and < 70 years) and clinical outcomes (death and recovery) (P = .008). Hypertension (P = .042) and temperature greater than 39°C (P = .039) were more common in patients who died. There were no significant differences in sex, chronic diseases, (such as, diabetes, cerebrovascular disease, COPD, chronic kidney disease and chronic liver disease), or initial symptoms, (such as, fever, cough, fatigue, anorexia, myalgia, dyspnea, pharyngalgia, diarrhea, vomiting, and dizziness) between the death group and recovery group. 3.2 Laboratory findings As shown in Table 1, the following factors were associated with a high risk of death from COVID‐19: white blood cell count greater than 9.5 × 109/L (P = .001), initial neutrophil count greater than 6.5 × 109/L (P < .001), initial lymphocyte count less than 0.6 × 109/L (P = .011), C‐reactive protein greater than 100 mg/L (P < .001), D‐dimer greater than 20 mg/L (P = .003), hypersensitive troponin I greater than 0.04 pg/mL (P = .004), blood urea nitrogen greater than 8 mmol/L (P = .011), lactate dehydrogenase greater than 300 U/L (P < .001), and lactic acid greater than 3 mmol/L (P = .014). However, there were no differences in hemoglobin less than 120 g/L, platelet count less than 100 × 109/L, procalcitonin greater than 0.5 ng/mL, creatine kinase‐MB greater than 5 ng/mL, alanine aminotransferase greater than 50 U/L, aspartate aminotransferase greater than 40 U/L, albumin less than 30 g/L, creatinine greater than 100 μmol/L, creatine kinase greater than 200 U/L, and B‐type natriuretic peptide (BNP) greater than 900 pg/mL between the death group and recovery group. TABLE 1 Characteristics of laboratory results in patients with COVID‐19 Laboratory results No.(%) P‐value All cases (n = 113) Death cases (n = 50) Recovery cases (n = 63) White blood cell count>9.5 × 109/L 22(19.47) 17(34.00) 5(7.94) .001 Initial neutrophil count>6.5 × 109/L 38(33.63) 28(56.00) 10(15.87) <.001 Initial lymphocyte count<0.6 × 109/L 44(38.94) 26(52.00) 18(28.57) .011 Hemoglobin<120 g/L 49(43.36) 20(40.00) 29(46.03) .520 Platelet count<100 × 109/L 18(15.93) 11(22.00) 7(11.11) .116 C‐reactive protein>100 mg/L 44(38.94) 31(62.00) 13(20.63) <.001 Procalcitonin>0.5 ng/mL 21(18.58) 12(24.00) 9(14.29) .187 D‐dimer>20 mg/L 15(13.27) 12(24.00) 3(4.76) .003 Creatine kinase‐MB > 5 ng/mL 15(13.27) 8(16.00) 7(11.11) .447 Hypersensitive troponin I > 0.04 pg/mL 38(33.63) 24(48.00) 14(22.22) .004 Alanine aminotransferase>50 U/L 39(34.51) 17(34.00) 22(34.92) .919 Aspartate minotransferase>40 U/L 57(50.44) 29(58.00) 28(44.44) .152 Albumin<30 g/L 21(18.58) 12(24.00) 9(14.29) .187 Blood urea nitrogen>8 mmol/L 44(38.94) 26(52.00) 18(28.57) .011 Creatinine>100 μmol/L 20(17.70) 11(22.00) 9(14.29) .286 Creatine kinase>200 U/L 24(21.24) 14(28.00) 10(15.87) .117 Lactate dehydrogenase>300 U/L 73(64.60) 42(84.00) 31(49.21) <.001 BNP > 900 pg/mL 39(34.51) 21(42.00) 18(28.57) .136 Lactic acid>3 mmol/L 34(30.09) 21(42.00) 13(20.63) .014 Abbreviation: BNP, brain natriuretic peptide. 3.3 Characteristics of ECG outcomes ECG data were available for 70 patients, of whom 35.7% died and 64.3% survived. Table 2 shows the ECG characteristics. Ventricular arrhythmias were recorded in 8 patients. In the death group, there were 5 cases of premature ventricular contraction and 1 case of ventricular tachycardia. In the recovery group, 2 patients had premature ventricular contraction. The proportion of patients with ventricular arrhythmia was higher in the death group than in the recovery group (24.0% vs 4.4%; P = .021) and was higher among myocardial damage patients than nonmyocardial damage patients (26.1% vs 4.3%; P = .013). There was a significant difference in abnormal ECG results between the death group and recovery group (P = .041). However, there was no significant difference in ST‐T abnormalities between patients with and without myocardial damage. Furthermore, other arrhythmic events, such as, sinus tachycardia, atrioventricular block, and atrial arrhythmia, showed no difference between the death group and recovery group. There was no significant difference in the QT interval between patients with and without hydroxychloroquine treatment. TABLE 2 Characteristics of ECG outcome with the study population ECG characteristics No.(%) Myocardial damage cases (n = 23) Non‐myocardial damage cases (n = 47) P‐value No.(%) Death cases (n = 25) Recovery cases (n = 45) P‐value Abnormal ECG 18(78.26) 27(57.45) .088 20(80.00) 25(55.56) .041 Abnormal ST‐T 11(47.83) 16(34.04) .266 12(48.00) 15(33.33) .227 Anterior ST‐T changes 5(21.74) 2(4.26) 4(16.00) 3(6.67) Inferior ST‐T changes 3(13.04) 1(2.13) 2(8.00) 2(4.44) All lead ST‐T changes 3(13.04) 13(27.66) 6(24.00) 10(22.22) Prolonged QT 4(17.39) 6(12.77) .719 a 5(20.00) 5(11.11) .477 a Sinus tachycardia 5(21.74) 4(8.51) .143 a 6(24.00) 3(6.67) .060 a Sinus bradycardia 0(0) 3(6.38) .546 a 0(0) 3(6.67) .548 a Atrioventricular block 3(13.04) 2(4.26) .322 a 3(12.00) 2(4.44) .341 a RBBB 1(4.35) 2(4.26) 2(8.00) 1(2.22) LBBB 2(8.70) 0(0) 2(8.00) 0(0) First degree A‐V block 2(8.70) 0(0) 1(4.00) 1(2.22) Pathological Q wave 3(13.04) 2(4.26) .322 a 3(12.00) 2(4.44) .341 a Atrial arrhythmia 5(21.74) 2(4.26) .035 a 3(12.00) 4(8.89) .694 a Atrial premature beat 2(8.70) 1(2.13) 1(4.00) 2(4.44) Atrial tachycardia 1(4.35) 0(0) 1(4.00) 0(0) Atrial fibrillation 2(8.70) 1(2.13) 1(4.00) 2(4.44) Ventricular arrhythmia 6(26.09) 2(4.26) .013 a 6(24.00) 2(4.44) .021 a PVC 5(21.74) 2(4.26) 5(20.00) 2(4.44) Ventricular tachycardia 1(4.35) 0(0) 1(4.00) 0(0) Note: one case combined with first degree A‐V block, complete RBBB, left anterior fascicular block and prolonged QT. Abbreviations: ECG, electrocardiogram; LBBB, left bundle branch block; PVC, premature ventricular contraction; RBBB, right bundle branch block. a Fisher's exact test. 3.4 Treatment characteristics Table 3 shows that the proportion of patients with Arbidol (82.5% vs 58.0%; P = .004) and hydroxychloroquine treatment (23.8% vs 4.0%; P = .003) was higher in the recovery group than in the death group. Other antiviral drugs, such as lopinavir/ritonavir, ribavirin, interferon α‐2b injection, ganciclovir, and oseltamivir, showed no difference between the death group and recovery group. In addition, glucocorticoid therapy, immunoglobulin, albumin therapy, oxygen therapy, noninvasive ventilation (NIV), and invasive mechanical ventilation (IMV) were not significantly different between critical patients in the death group and recovery group. TABLE 3 Characteristics of treatment with the study population Treatment No.(%) P‐value All cases (n = 113) Death cases (n = 50) Recovery cases (n = 63) Antiviral drug lopinavir/ritonavir 4(3.54) 2(4.00) 2(3.17) 1.000 a ribavirin 56(49.56) 23(46.00) 33(52.38) .500 arbidol 81(71.68) 29(58.00) 52(82.54) .004 hydroxychloroquine 17(15.04) 2(4.00) 15(23.81) .003 interferon α‐2b injection 21(18.58) 9(18.00) 12(19.05) .887 ganciclovir 20(17.70) 11(22.00) 9(14.29) .286 oseltamivir 34(30.09) 18(36.00) 16(25.40) .222 Glucocorticoid therapy 70(61.95) 30(60.00) 40(63.49) .704 Immunoglobulin 73(64.60) 29(58.00) 44(69.84) .191 Albumin therapy 27(23.89) 10(20.00) 17(26.89) .387 Oxygen therapy 53(46.90) 22(44.00) 31(49.21) .582 NIV 56(49.56) 26(52.00) 30(47.62) .644 IMV 4(3.54) 2(4.00) 2(3.17) 1.000 a Abbreviations: IMV, invasive mechanical ventilation; NIV, noninvasive ventilation. a Fisher's exact test. 3.5 Risk factors associated with death Kaplan‐Meier survival analysis was used to analyze patient survival. Supplemental Figure S1 shows the survival curves of patients of different ages (<70 years and >70 years). Elderly patients were more common in the death group than in the recovered group (P = .009). The survival curve of those who had an initial neutrophil count >6.5 × 109/L was lower than that of patients with an initial neutrophil count <6.5 × 109/L (P < .001) (Supplemental Figure S2). The survival curve of patients with C‐reactive protein >100 mg/L was lower than that of patients with C‐reactive protein <100 mg/L (P < .001) (Supplemental Figure S3). The survival curve of patients with lactate dehydrogenase >300 U/L was lower than that of patients with lactate dehydrogenase <300 U/L (P < .001) (Supplemental Figure S4). Immunoglobulin therapy was more common in the recovered group than in the death group (P = .227) (Supplemental Figure S5). All the factors in Tables S1, 1 and 3 were included in multivariate analysis to explore independent predictors of mortality from COVID‐19. As there were only 70 ECG data points, the factors in Table 2 were used in multivariate analysis alone to explore only the ECG risk factors for mortality from COVID‐19. As show in Table 4, the independent mortality predictors of COVID‐19 were age >70 years (HR 1.84, 95% CI 1.03‐3.28), initial neutrophil count greater than 6.5 × 109/L (HR 3.43, 95% CI 1.84‐6.40), C‐reactive protein greater than 100 mg/L (HR 1.93, 95% CI 1.04‐3.59), and lactate dehydrogenase greater than 300 U/L (HR 2.90, 95% CI 1.26‐6.67), which were all distributed to the right of the invalid line, as shown in Supplementary Figure S6. Immunoglobulin treatment (HR 0.39, 95% CI 0.21‐0.73) reduced the risk of death and was distributed to the left of the invalid line in the forest plot. In Table 5, sinus tachycardia (HR 2.94, 95% CI 1.16‐7.46) and ventricular arrhythmia (HR 2.79, 95% CI 1.11‐7.04) were independent ECG risk factors for mortality from COVID‐19. TABLE 4 Cox proportional hazards regression model of risk factors for COVID‐19 Characteristics coefficient SE (coefficient) wald P‐value HR(95%CI) Age >70 years 0.608 0.295 4.246 .039 1.84(1.03‐3.28) Initial neutrophil count>6.5 × 109/L 1.232 0.319 14.930 <.001 3.43(1.84–6.40) C‐reactive protein>100 mg/L 0.656 0.318 4.272 .039 1.93(1.04–3.59) Lactate dehydrogenase>300 U/L 1.063 0.425 6.249 .012 2.90(1.26–6.67) Immunoglobulin treatment −0.935 0.320 8.568 .003 0.39(0.21–0.73) Abbreviations: CI, confidence interval; HR, hazard ratio. TABLE 5 Cox proportional hazards regression model of ECG risk factors for COVID‐19 Characteristics Coefficient SE (coefficient) wald P‐value HR(95%CI) Sinus tachycardia 1.077 0.476 5.126 .024 2.94(1.16‐7.46) Ventricular arrhythmia 1.027 0.471 4.745 .029 2.79(1.11‐7.04) 4 DISCUSSION This present retrospective study identified several risk factors for mortality from COVID‐19. In particular, old age (>70 years), neutrophilia, C‐reactive protein greater than 100 mg/L and lactate dehydrogenase greater than 300 U/L were associated with a higher likelihood of critical in‐hospital death. Our study also showed that the incidence of ventricular arrhythmia was higher in deceased patients than survivors. Sinus tachycardia and ventricular arrhythmia were independent ECG risk factors for mortality from COVID‐19. However, there was no difference in ST‐T and QT interval abnormalities between deceased patients and survivors. In slightly over 3 months, SARS‐CoV‐2 spread worldwide and caused far greater morbidity and mortality than either SARS or MERS. 11 Previous studies have shown that older age, D‐dimer greater than 1 μg/mL and greater cardiac troponin are potential risk factors for inpatients with COVID‐19. 12 , 13 The number of cases has rapidly increased throughout the world, and there are more severe cases. However, the risk factors for death are not fully understood in critical cases. In the present study, we analyzed possible risk factors for death from COVID‐19. All patient characteristics and laboratory findings were included to examine the relationship between risk factors and death from critical COVID‐19 at an early stage. The risk factors related to death included older age, neutrophilia, C‐reactive protein greater than 100 mg/L, and lactate dehydrogenase greater than 300 U/L. Chen suggested that SARS‐CoV‐2 is more likely to infect older adult males with chronic comorbidities as a result of the weaker immune functions of these patients. 2 We also found that the proportion of elderly patients and hypertension patients was higher in patients who died. Therefore, as an independent risk factor, age‐related chronic diseases still play an important role in the outcome of critical cases. In addition, the results of the present study showed that patients with COVID‐19 who died had significantly higher neutrophil counts than survivors. Considering that older age is associated with decreased immune function, 14 older age may be related to death due to less robust immune responses. Cytokine storm and the viral evasion of cellular immune responses are thought to play important roles in disease severity. 15 The present findings showed that CRP greater than 100 mg/L was significantly associated with fatality. A significant increase in CRP levels, as documented for bacterial infections, can also occur with viral infections. 16 CRP is a classic acute phase protein. It can be concluded that a higher CRP value may result from a more severe form of COVID‐19. Lactate dehydrogenase greater than 300 U/L is another independent high‐risk factor for mortality. Increased lactate dehydrogenase was related to heart failure or MOF, which may lead to the fatality from COVID‐19. 17 SARS‐CoV‐2 infection is associated with inflammatory mediators that may play important roles in cardiac and arrhythmic complications. 18 , 19 , 20 A previous study reported that 16.7% of patients with COVID‐19 had arrhythmia, and 7.2% had acute heart injury. 6 However, other studies have reported that the incidence of arrhythmia in patients with COVID‐19 was only 0.3%, which was relatively low. 21 , 22 In our study, to the best of our knowledge, we first reported ECG characteristics in critical patients. We found that the incidence of arrhythmia was approximately 45.7% in critical patients and that the incidence of ventricular arrhythmia was high in patients with myocardial damage and in patients who died. Sinus tachycardia and ventricular arrhythmia were independent ECG risk factors for mortality in critical inpatients with COVID‐19. Furthermore, there was no difference in the QT interval between patients who died and survivors, and there was no significant difference in ST‐T abnormalities between patients with and without myocardial damage. Taken together, these results showed that critical inpatients with COVID‐19 are prone to ventricular arrhythmia or abnormal ECG results, which is caused by myocardial damage. Hydroxychloroquine is known to have anti‐inflammatory and antiviral effects and is used for rheumatoid arthritis and SARS. 23 , 24 The side effects of hydroxychloroquine may include gastrointestinal symptoms and QT prolongation syndrome, especially in patients with renal or hepatic dysfunction. 25 However, our results showed that hydroxychloroquine treatment was not associated with a higher likelihood of survival in critical in‐hospital patients. Furthermore, hydroxychloroquine treatment during hospitalization was not associated with QT prolongation. There were several limitations to this study. First, most of the patients did not have a 24‐hour Holter monitor. Short bursts of arrhythmias may have been missed. Second, few patients were given antiarrhythmic drugs, such as, amiodarone and propafenone. Whether antiarrhythmic drugs affect the occurrence of arrhythmia needs further study. Third, due to the retrospective study design and the limited number of patients, data from larger populations and multiple centers are needed to further confirm the risk of mortality during hospitalization. Finally, this was a retrospective and observational study, and most of the patients were seriously ill at the time of admission. Very few patients had echocardiographic data, and patient height and weight data were also missing, so we could not obtain results of echocardiography and BMI. 5 CONCLUSIONS Old age (>70 years), neutrophilia, C‐reactive protein greater than 100 mg/L, and lactate dehydrogenase greater than 300 U/L are high‐risk factors related to the fatality of critical patients with COVID‐19. Immunoglobulin treatment can reduce the risk of death. The proportion of patients with ventricular arrhythmia was higher in deceased patients than in survivors. Sinus tachycardia and ventricular arrhythmia were independent ECG risk factors for mortality in critical inpatients with COVID‐19. CONFLICT OF INTEREST The authors declare no potential conflict of interest. AUTHOR CONTRIBUTIONS Lingzhi Li, Shudi Zhang, and Bing He conducted the systematic literature search, analyzed data and wrote the manuscript. Xiaobei Chen and Shihong Wang revised the manuscript for intellectual content. Qingyan Zhao is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis Supporting information Appendix S1. Supporting Information Click here for additional data file. ACKNOWLEDGMENT This study was funded by the National Natural Science Foundation of China (No.81670303) and (No.81970277). ==== Refs REFERENCES 1 Huang C , Wang Y , Li X , et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China . Lancet . 2020 ;395 :497 ‐506 .31986264 2 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 . Lancet . 2020 ;395 (10223 ):507 ‐513 .32007143 3 Yang X , Yu Y , Xu J , et al. Clinical course and outcomes of critically ill patients with SARS‐CoV‐2 pneumonia in Wuhan, China: a single‐centered, retrospective, observational study . 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