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. 2021 Jan 28;16(1):e0246030. doi: 10.1371/journal.pone.0246030

Risk factors for mortality of coronavirus disease-2019 (COVID-19) patients in two centers of Hubei province, China: A retrospective analysis

Xiao-Bin Zhang 1,2,*,#, Lan Hu 3,#, Quan Ming 4,#, Xiao-Jie Wei 5,#, Zhen-Yu Zhang 6,#, Li-Da Chen 7,#, Ming-Hui Wang 1,2,#, Weng-Zhen Yao 1,2,#, Qiu-Fen Huang 1,2,#, Zhang-Qiang Ye 1,2,#, Yu-Qing Cai 1,2,#, Hui-Qing Zeng 1,2,*,#
Editor: Alexandra Lucas8
PMCID: PMC7842894  PMID: 33507974

Abstract

Purpose

Since the outbreak in late December 2019 in Wuhan, China, coronavirus disease-2019 (COVID-19) has become a global pandemic. We analyzed and compared the clinical, laboratory, and radiological characteristics between survivors and non-survivors and identify risk factors for mortality.

Methods

Clinical and laboratory variables, radiological features, treatment approach, and complications were retrospectively collected in two centers of Hubei province, China. Cox regression analysis was conducted to identify the risk factors for mortality.

Results

A total of 432 patients were enrolled, and the median patient age was 54 years. The overall mortality rate was 5.09% (22/432). As compared with the survivor group (n = 410), those in the non-survivor group (n = 22) were older, and they had a higher frequency of comorbidities and were more prone to suffer from dyspnea. Several abnormal laboratory variables indicated that acute cardiac injury, hepatic damage, and acute renal insufficiency were detected in the non-survivor group. Non-surviving patients also had a high computed tomography (CT) score and higher rate of consolidation. The most common complication causing death was acute respiratory distress syndrome (ARDS) (18/22, 81.8%). Multivariate Cox regression analysis revealed that hemoglobin (Hb) <90 g/L (hazard ratio, 10.776; 95% confidence interval, 3.075–37.766; p<0.0001), creatine kinase (CK-MB) >8 U/L (9.155; 2.424–34.584; p = 0.001), lactate dehydrogenase (LDH) >245 U/L (5.963; 2.029–17.529; p = 0.001), procalcitonin (PCT) >0.5 ng/ml (7.080; 1.671–29.992; p = 0.008), and CT score >10 (39.503; 12.430–125.539; p<0.0001) were independent risk factors for the mortality of COVID-19.

Conclusions

Low Hb, high LDH, PCT, and CT score on admission were the predictors for mortality and could assist clinicians in early identification of poor prognosis among COVID-19 patients.

Introduction

In December 2019, coronavirus disease-2019 (COVID-19) was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak in Wuhan, Hubei Province, China [1]. Subsequently, it rapidly spread all over the world and became a global pandemic. As of April 29, 2020, more than 3 million COVID-19 cases have been reported worldwide, causing more than 200,000 deaths. This pandemic started from zoonotic transmission, but human-to-human transmission was soon confirmed [2]. The clinical spectrum of COVID-19 varies from asymptomatic, mild upper airway illness, to severe pneumonia with respiratory failure. Common symptoms of COVID-19 include fever, cough, fatigue, and dyspnea [3]. Being a beta coronavirus, the SARS-CoV-2 virus was closely related (with 88% identity) to two bat-derived severe acute respiratory syndrome (SARS)-like coronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21, collected in 2018 in Zhoushan, eastern China, but were more distant from SARS-CoV (about 79%) and MERS-CoV (about 50%) [4].

Evidence shows that the overall mortality rate of COVID-19 is 3.77–5.4% [5–7], however, it increases up to 41.1–61.5% among severe or critically ill patients [8–10]. To reduce the overall mortality rate, identifying the risk factors related to disease severity and mortality in COVID-19 patients is urgently required. Previous studies have shown that older age, underlying comorbidities, high D-dimer level, and abnormalities of several biochemical variables were closely associated with disease severity or even death of COVID-19 patients [6, 9, 11–13]. Most of the previous studies were single-center investigations with small sample sizes in Wuhan, China. The present study analyzed and compared the demographic, clinical, and laboratory variables, and radiological features between surviving and no-surviving patients with laboratory-confirmed COVID-19 in two hospitals with a relatively larger sample size. Potential risk factors for death on admission were determined. We tried to provide some useful information to predict the death of COVID-19 patients through this retrospective cohort study of 432 cases in two centers in Hubei province, China.

Methods

Study population

Adult inpatients (≥18 years old) with COVID-19 were retrospectively analyzed from the following two centers: Optic Valley division of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, and Wuhan and Yichang Third People's Hospital, Hubei Province from January 20, 2020, to March 30, 2020. All patients were treated by the Fujian and Xiamen Medical Team that reached to help Hubei province. COVID-19 diagnosis was based on the New Coronavirus Pneumonia Prevention and Control Program published by the Chinese National Health Commission (version 6) [14]. All patients were laboratory-confirmed positive cases of SARS-CoV-2 by quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) of nasopharyngeal swab samples and had a definite outcome (dead or discharged). The study was approved by the Institutional Ethics Committee of both Optic Valley division of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, and Wuhan and Yichang Third People's Hospital.

Data collection

All clinical information, including age, sex, medical history, comorbidities, laboratory findings, and thoracic computed tomography (CT) results, treatment approaches, and complications, of COVID-19 patients who had been discharged from or had died at the two centers was extracted from electronic medical records. All data were collected using an electronic data collection form. In order to verify the accuracy, two researchers independently reviewed the data collection forms.

Definition

The discharge criteria were absence of fever for 3 days, substantial improvement in both lungs on thoracic CT, clinical remission of respiratory symptoms, and two consecutive nasopharyngeal swab samples negative for SARS-CoV-2 RNA obtained at least 24 hours apart. COVID-19 severity was defined according to the New Coronavirus Pneumonia Prevention and Control Program published by the Chinese National Health Commission (version 6). Acute respiratory distress syndrome (ARDS) was diagnosed according to the Berlin definition [15].

Laboratory testing

Nasopharyngeal swab samples were collected for SARS-CoV-2 viral nucleic acid detection using real-time reverse transcriptase-polymerase chain reaction (RT-PCR). Blood was obtained from all patients on admission for analysis. The BC 3000 auto hematology analyzer (Mindray Medical International, Inc., Shenzhen, China) was used for routine blood tests. Serum renal and liver function, creatine kinase (CK), CK-MB, lactate dehydrogenase (LDH), C-reactive protein (CRP), procalcitonin (PCT), and erythrocyte sedimentation rate (ESR) were measured on a Beckman Coulter AU5800 (Beckman Coulter Co, Brea, CA, USA). Lymphocyte subsets were analyzed on a BD FACS Canto II flow cytometry system (BD Biosciences, CA, USA). Blood coagulation profiles were analyzed by immunoturbidimetry using the ACL TOP system (Intrstumentation Laboratory, Milan, Italy). Arterial blood gas analysis was performed with a hamo gas analyzer (Nova Biomedical, USA). The levels of serum cytokines [Interleukin-1 (IL-1), IL-2 receptor, IL-6, IL-8, IL-10, and tumor necrosis factor-〈 (TNF-〈)] were measured by the chemilumnescent immunoassay (CLIA) on Siemens Immulite 1000 analyzer according to the manufacturer's instructions.

CT image acquisition and scoring

A thoracic CT scan was performed before or after 2 days of admission in all patients. Two researchers reviewed and scored the thoracic CT images independently with the same CT score criteria: lobar involvement was classified as 0-none (0%), 1-minimal (1–25%), 2-mild (26–50%), 3-moderate (51–75%), or 4-severe (76–100%) of each lobe. Finally, a total score of 0–20 for the five lobes was summarized.

Data statistical analysis

SPSS 22.0 software was used for statistical analysis. Continuous data are presented as means± standard deviation or median (interquartile range, IQR), while categorical data are presented as number (%). Means of continuous variables were compared using the Mann-Whitney test. Categorical variables were compared using the chi-square test or Fisher's exact test between the groups. Cox regression analysis was used to explore the risk factors for COVID-19 mortality. Univariate and multivariate regression models were used. A variable was entered into the multivariable Cox regression model when it satisfied one of the following items: 1. univariate Cox regression showed a significant difference; 2. Variable was closely associated with survival according to previous reports [6, 10–13] and clinical experience. If the proportion of missing data of a variable was less than 20%, the missing data were replaced by the mean value; however, when the proportion of missing data was more than 20%, the variable was not entered into Cox regression. A p-value of less than 0.05 was considered statistically significant.

Results

Clinical features on admission

As shown in Table 1, a total of 432 patients were included in the final analysis (128 from the Optic Valley division of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan and 304 from Yichang Third People's Hospital). A total of 22 (5.09%) patients died during hospitalization and 410 (94.91%) patients were discharged. Male patients accounted for 53.2% of all patients. The median age was 54 years (IQR 39–66 years), and patients in the non-survivor group were much older than those in the survivor group [66 years (54–70 years) vs. 53 years (38–66 years), p = 0.003]. The comorbidity rate in the non-survivor group was higher than that in the survivor group (77.3% vs. 31.0%, p<0.0001). Higher rates of hypertension, cardiovascular or cerebrovascular diseases, diabetes, chronic kidney disease, and other diseases were observed in the non-survivor group than in the survivor group (all p≤0.001). The top five symptoms in all patients were fever (71.3%), dry cough (62.5%), sputum production (30.1%), fatigue (29.6%), and chest tightness (10.9%). We found that patients in the non-survivor group had significantly higher dyspnea rates than those in the survivor group (36.4% vs. 6.6%, p<0.0001). Patients in the non-survivor group had higher heart rate and respiratory rate than those in the survivor group (all p<0.05). The median number of days from the onset of illness to hospital admission was 5 days (IQR 2–10 days). The median time from illness onset to discharge was 26 days (IQR 20–36 days), whereas the median time to death was 18 days (IQR 10–26 days) (p = 0.001). Regarding the disease severity between groups, the proportions of severe type and critical type in the non-survivor group were significantly higher than those in the survivor group (72.7% vs. 24.6% for the severe type and 27.3% vs. 0.5% for the critical type, all p<0.0001).

Table 1. Comparison of demographic and clinical characteristics of COVID-19 patients between the survivor and non-survivor groups.

Variable Total (n = 432) Non-survivors (n = 22) Survivors (n = 410) p value
Age, years 54 (39–66) 66 (54–70) 53 (38–66) 0.003
Gender, n (%) 0.754
    Male 230 (53.2) 11 (50.0) 219 (53.4) ..
    Female 202 (46.8) 11 (50.0) 191 (46.6) ..
Comorbidity, n (%) 144 (33.3) 17 (77.3) 127 (31.0) <0.0001
    Hypertension 97 (22.5) 13 (59.1) 84 (20.5) <0.0001
    ACEI or ARB administration 20 (20.6) 3 (23.1) 17 (20.2) 0.814
    Cardiovascular or cerebrovascular diseases 25 (5.8) 7 (38.1) 18 (4.4) <0.0001
    Diabetes 56 (13.0) 8 (36.4) 48 (11.7) 0.001
    Chronic obstructive pulmonary disease 25 (5.8) 3 (13.6) 22 (5.4) 0.106
    Carcinoma 5 (1.2) 0 (0.0) 5 (1.2) 0.602
    Chronic kidney disease 9 (2.1) 5 (22.7) 4 (1.0) <0.0001
    Others 25 (5.8) 7 (31.8) 18 (4.4) <0.0001
Symptom
    Fever 308 (71.3) 15 (68.2) 293 (71.5) 0.740
    Dry cough 270 (62.5) 15 (68.2) 255 (62.2) 0.572
    Fatigue 128 (29.6) 7 (31.8) 121 (29.5) 0.818
    Dyspnea 35 (8.1) 8 (36.4) 27 (6.6) <0.0001
    Sputum production 130 (30.1) 6 (27.3) 124 (30.2) 0.767
    Sore throat 34 (7.9) 2 (9.1) 32 (7.8) 0.827
    Chest tightness 47 (10.9) 2 (9.1) 45 (11.0) 0.782
    Diarrhoea 20 (4.6) 1 (4.5) 19 (4.6) 0.985
    Myalgia 46 (10.6) 3 (13.6) 43 (10.5) 0.641
    Headache 19 (4.4) 1 (4.5) 18 (4.4) 0.972
Temperature, °C 36.8 (36.5–37.3) 37.0 (36.7–37.6) 36.8 (36.5–37.3) 0.221
Heart rate, beats/min 88 (80–97) 91 (85–101) 88 (80–97) 0.046
Respiratory rate, breaths/min 21 (20–23) 22 (21–24) 21 (20–22) 0.016
Systolic blood pressure, mmHg 126 (118–137) 129 (120–137) 126 (118–138) 0.941
Diastolic blood pressure, mmHg 80 (71–87) 77 (67–81) 80 (71–87) 0.178
Illness onset to hospital admission, days 5 (2–10) 5 (2–7) 5 (2–10) 0.482
Illness onset to hospital discharge/death, days 26 (20–36) 18 (10–26) 26 (20–36) 0.001
Disease severity status*
    Mild 16 (3.7) 0 (0.0) 16 (3.9) 0.345
    Moderate 291 (67.4) 0 (0.0) 291 (71.0) <0.0001
    Severe 117 (27.1) 16 (72.7) 101 (24.6) <0.0001
    Critical 8 (1.9) 6 (27.3) 2 (0.5) <0.0001
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Abbreviations: COVID-19: coronavirus disease-2019; ACEI: angiotensin converting enzyme inhibitors; ARB: angiotensin receptor blocker.

*: Classification of COVID-19 severity is according to the New Coronavirus Pneumonia Prevention and Control Program published by the Chinese National Health Commission (version 6).

Laboratory findings

Compared with the results in the survivor group, lymphocyte count, CD4+ T cells, CD8+ T cells, hemoglobin, platelet count, albumin, arterial partial pressure of oxygen (PaO2), and the ratio of PaO2 to the fraction of inspired O2 (FiO2) were significantly lower in the non-survivor group. The levels of variables reflecting liver function (total bilirubin, direct bilirubin, and aspartate aminotransferase), renal function (cystatin C), cardiac injury (creatine kinase-MB, cardiac troponin I, and brain natriuretic peptide), inflammation (CK, LDH, CRP, ESR, and PCT), cytokine levels [IL-2 receptor, IL-6, IL-8, and IL-10], and coagulation (D-dimer) were significantly higher in the non-survivor group as compared with the survivor group (all p<0.05) (Table 2). The other variables, including white blood cell count, alaine aminotransferase, creatinine, myoglobin, serum lactate, prothrombin time, activated partial thromboplastin time, and arterial partial pressure of carbon dioxide, were not different between the survivor and non-survivor groups (all p>0.05).

Table 2. Comparison of laboratory findings of COVID-19 patients between the survivor and non-survivor groups.

Variable Total (n = 432) Non-survivors (n = 22) Survivors (n = 410) p value
White blood cell count, ×109/L 4.70 (3.59–6.10) 4.45 (3.15–8.98) 4.70 (3.60–6.04) 0.927
Lymphocyte count, ×109/L 1.21 (0.84–1.63) 0.66 (0.53–0.89) 1.23 (0.87–1.65) <0.0001
T cell subsets
    CD4+ T cells, cell/μL 593 (339–848) 85 (68–125) 657 (484–875) <0.0001
    CD8+ T cells, cell/μL 339 (217–490) 125 (98–198) 369 (257–515) <0.0001
Haemoglobin, g/L 124 (112–136) 113 (104–122) 125 (113–137) 0.001
Platelet count, ×109/L 158 (119–208) 132 (80–169) 160 (121–209) 0.01
Albumin, g/L 38.2 (33.9–41.4) 30.9 (28.0–37.4) 38.5 (34.3–41.5) <0.0001
Total bilirubin, μmol/L 9.30 (6.81–13.15) 14.40 (8.60–20.00) 9.23 (6.80–12.60) 0.001
Direct bilirubin, μmol/L 2.71 (1.88–3.96) 4.87 (2.44–7.41) 2.68 (1.86–3.88) 0.004
Alaine aminotransferase, U/L 22.0 (14.0–35.0) 25.5 (14.0–50.0) 22.0 (14.0–35.0) 0.194
Aspartate aminotransferase, U/L 22.0 (16.0–30.0) 36.0 (21.3–67.3) 21.0 (16.0–29.0) 0.001
Creatinine, μmol/L 68.5 (55.9–82.5) 67.5 (62.8–92.3) 68.5 (55.5–81.9) 0.127
Cystatin C, mg/L 1.05 (0.89–1.29) 1.31 (1.05–2.28) 1.03 (0.88–1.23) 0.002
Creatine kinase-MB, U/L 8.9 (1.3–12.2) 14.2 (8.8–35.3) 8.8 (1.2–11.4) <0.0001
Myoglobin, ng/mL 36.0 (27.8–79.1) 49.8 (30.2–125.3) 35.0 (27.8–77.5) 0.258
Cardiac troponin I, pg/mL 4.5 (1.9–11.1) 13.0 (4.6–26.3) 4.3 (1.9–10.3) 0.001
Brain natriuretic peptide, pg/mL 104.5 (29.0–311.0) 614.5 (304.6–1177.5) 96.5 (25.3–244.5) <0.0001
Creatine kinase, U/L 62.0 (43.0–98.0) 117.0 (52.0–201.8) 60.0 (42.3–94.8) 0.04
Lactate dehydrogenase, U/L 206.0 (169.0–265.8) 399.0 (235.0–595.0) 203.5 (167.0–260.0) <0.0001
C reactive protein, mg/L 12.5 (3.1–38.7) 63.9 (24.7–101.6) 11.7 (2.8–35.8) <0.0001
Erythrocyte sedimentation rate, mm/h 25.0 (14.5–48.0) 40.6 (23.0–55.5) 24.0 (13.0–46.5) 0.015
Procalcitonin, ng/mL 0.07 (0.05–0.11) 0.17 (0.10–0.33) 0.07 (0.05–0.10) <0.0001
Serum lactate, mmol/L 1.84 (1.49–2.34) 1.96 (1.49–2.46) 1.81 (1.48–2.27) 0.509
Interleukin-2 receptor, U/mL 413.0 (266.8–697.3) 737.0 (697.0–840.6) 380.0 (261.0–646.0) 0.01
Interleukin-6, pg/mL 3.5 (1.6–11.9) 165.0 (115.3–257.9) 2.8 (1.5–6.7) <0.0001
Interleukin-8, pg/mL 9.3 (6.3–12.9) 23.5 (9.9–65.7) 9.0 (6.0–12.1) 0.003
Interleukin-10, pg/mL 5.0 (5.0–5.0)* 6.4 (5.0–8.4) 5.0 (5.0–5.0)* 0.009
TNF-〈, pg/mL 7.6 (6.1–9.7) 8.3 (7.0–19.0) 7.5 (6.0–9.7) 0.110
Prothrombin time, s 11.1 (10.6–13.0) 11.2 (10.7–12.0) 11.1 (10.5–13.0) 0.975
Activated partial thromboplastin time, s 32.3 (27.7–37.0) 30.7 (24.0–36.1) 32.4 (28.0–37.1) 0.234
D-dimer, μg/ml 0.54 (0.44–0.80 1.46 (0.74–5.09) 0.53 (0.43–0.70 <0.0001
PaO2, mmHg 92.0 (76.5–110.0) 65.4 (56.1–77.8) 94.1 (80.0–112.0) <0.0001
PaCO2, mmHg 40.4 (36.9–43.6) 37.6 (29.9–47.1) 40.5 (37.4–43.6) 0.147
PaO2: FiO2, mmHg 306.9 (231.8–362.1) 91.7 (80.5–196.5) 316.1 (258.1–366.2) <0.0001
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*: minimum detection value is 5.0.

All data are presented as median interquartile (IQR).

The normal ranges of all laboratory findings are outlined in S5 Table.

Abbreviations: COVID-19: coronavirus disease-2019; TNF: tumor necrosis factor-〈. PaO2: arterial partial pressure of oxygen; PaCO2: arterial partial pressure of carbon dioxide; FiO2: fraction of inspired O2.

CT image results

The most common finding of the CT scan was ground-glass opacity (98.6%), followed by bilateral lung involvement (82.5%) and consolidation (23.3%). Compared with the survivor group, patients in the non-survivor group had a higher rate of consolidation (45.5% vs. 22.1%, p = 0.012) and increased CT score [14.0 (10.5–16.0) vs. 6.0 (4.0–7.3), p<0.0001] (Table 3).

Table 3. Comparison of CT image results of COVID-19 patients between the survivor and non-survivor groups*.

Variables Total (n = 416) Survivors (n = 394) Non-survivors (n = 22) p value
Bilateral lung involvement 343 (82.5) 322 (81.7) 21 (95.5) 0.099
Ground-glass opacity 410 (98.6) 388 (98.5) 22 (100.0) 0.720
Consolidation 97 (23.3) 87 (22.1) 10 (45.5) 0.012
Pleural effusion 5 (1.2) 4 (1.0) 1 (4.5) 0.139
Pleural thickening 5 (1.2) 5 (1.3) 0 (0.0) 0.761
CT score 6.0 (4.0–8.0) 6.0 (4.0–7.3) 14.0 (10.5–16.0) <0.0001
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*: moderate, severe, and critically ill cases were included in this analysis.

Abbreviations: CT: computed tomography; COVID-19: coronavirus disease-2019.

Treatment and complications

Nearly all patients (96.5%) received antiviral drugs during hospitalization. There were no differences between the survivor and non-survivor groups in terms of usage of the antiviral drug (umifenovir, interferon 〈 nebulization, lopinavir/ritonavir, ribavirin, chloroquine, hydroxychloroquine, and oseltamivir). A total of 95.5% of patients in the non-survivor group and 87.1% of patients in the survivor group received antibiotic therapy (p = 0.247). There was a significant difference in corticosteroid and intravenous immunoglobulin usage between the two groups (all p<0.0001). As compared with the survivor group, more patients in the non-survivor group received non-invasive or invasive mechanical ventilation (all p<0.0001). The frequency of complications in the non-survivor group was noticeably higher than that in the survivor group. The most frequently detected complication in the non-survivor group was ARDS (81.8%), followed by acute hepatic insufficiency, heart failure, thrombocytopenia, acute kidney injury, and acute cardiac injury. The median time of viral shedding after COVID-19 onset was 6 days (IQR 3–10 days), and there was no difference in viral shedding time between the two groups (Table 4).

Table 4. Comparison of treatment and complications of COVID-19 patients between the survivor and non-survivor groups.

Variable Total (n = 432) Non-survivors (n = 22) Survivors (n = 410) p value
Antiviral drugs 417 (96.5) 21 (95.5) 396 (96.6) 0.778
    Umifenovir 237 (54.9) 12 (54.5) 225 (54.9) 0.976
    Interferon 〈 nebulization 265 (61.3) 18 (81.8) 247 (60.2) 0.043
Lopinavir/ritonavir 257 (59.5) 13 (59.1) 244 (59.5) 0.969
    Ribavirin 29 (6.7) 1 (4.5) 28 (6.8) 0.677
    Chloroquine 25 (5.8) 0 (0.0) 25 (6.1) 0.233
    Hydroxychloroquine 28 (6.5) 0 (0.0) 28(6.8) 0.205
    Oselatmivir 239 (55.3) 17 (77.3) 222(54.1) 0.034
Antibiotics 378 (87.5) 21 (95.5) 357 (87.1) 0.247
Corticosteroids 106 (24.5) 18 (81.8) 88 (21.5) <0.0001
Intravenous immunoglobulin 51 (11.8) 9 (40.9) 42 (10.2) <0.0001
Oxygen supply method 359 (83.1) 22 (100.0)) 337 (82.2) 0.03
    Nasal cannula 299 (69.2) 7 (31.8) 292 (71.2) <0.0001
    Nasal and mouth mask 20 (4.6) 2 (9.1) 18 (4.4) 0.271
    High-flow oxygen therapy 5 (1.2) 0 (0.0) 5 (1.2) 0.602
    Noninvasive mechanical ventilation 26 (6.0) 7 (31.8) 19 (4.6) <0.0001
    Invasive mechanical ventilation 6 (1.4) 6 (27.3) 0 (0.0) <0.0001
Continuous renal replacement therapy 9 (2.1) 3 (13.6) 6 (1.5) 0.008
Complications 52 (12.0) 19 (86.4) 33 (8.0) <0.0001
    ARDS 24 (5.6) 18 (81.8) 6 (1.5) <0.0001
    Acute kidney injury 16 (3.7) 3 (13.6) 13 (3.2) 0.042
    Heart failure 8 (1.9) 5 (22.7) 3 (0.7) <0.0001
    Acute hepatic insufficiency 30 (6.9) 6 (27.3) 24 (5.9) <0.0001
    Acute cardiac injury 3 (0.7) 2 (9.1) 1 (0.2) <0.0001
    Thrombocytopenia 9 (2.1) 5 (22.7) 4 (1.0) <0.0001
Duration of viral shedding after COVID-19 onset, days 6 (3–10) 7 (4–7) 6 (3–10) 0.899
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Abbreviations: COVID-19, coronavirus disease-2019; ARDS, acute respiratory distress syndrome.

Prediction of mortality

In order to evaluate the risk factors for hospital admission for mortality, Cox regression analysis was conducted. We initially performed univariate analysis by using the variables that were statistically significant (all p<0.05) between the non-survivor and survivor groups in Tables 1–3. The univariate analysis results (Table 5) showed that the following variables were associated with the mortality of COVID-19 patients: age ≥ 65 years, hypertension, cardiovascular or cerebrovascular diseases, diabetes, chronic kidney disease, other comorbidities, dyspnea, respiratory rate >20 breaths/min, lymphocyte count <1.1×109/L, CD4+ T cells <550 cells/μL, CD8+ T cells <320 cells/μL, hemoglobin (Hb) <90 g/L, platelet count <100×109/L, albumin <35g/L, direct bilirubin >8 μmol/L, aspartate aminotransferase (AST) >40 U/L, cystatin C >1.55 mg/L, CK-MB >8 U/L, brain natriuretic peptide (BNP) >500 pg/mL and >1000 pg/mL, CK >190 U/L, LDH >245 U/L, CRP >10 mg/L, ESR >20 mm/h, PCT >0.5 ng/L, IL-2 >10 pg/mL, IL-8>62 pg/mL, IL-10 >9 pg/mL, D-dimer >1.0 mg/mL, PaO2 <80 and <60 mmHg, PaO2/FiO2 < 200 mmHg, CT consolidation, and CT score >10.

Table 5. Univariate Cox regression analysis of factors associated with mortality.

Variables Total No-survivors Survivors Hazard risk (95% confidence interval) P value
Age, years
    <65 308 (71.3) 10 (45.5) 298 (72.7) 1 (ref) 1 (ref)
    ≥65 124 (28.7) 12 (54.5) 112 (27.3) 2.730 (1.178–6.328) 0.019
Hypertension 97 (22.5) 13 (59.1) 84 (20.5) 4.609 (1.968–10.794) <0.0001
Cardiovascular or cerebrovascular diseases 25 (5.8) 7 (31.8) 18 (4.4) 9.285 (3.781–22.800) <0.0001
Diabetes 56 (13.0) 8 (36.4) 48 (11.7) 3.431 (1.437–8.193) 0.005
Chronic kidney disease 9 (2.1) 5 (22.7) 4 (1.0) 13.448 (4.950–36.535) <0.0001
Others 25 (5.8) 7 (31.8) 18 (4.4) 7.163 (2.918–17.585) <0.0001
Dyspnea 35 (8.1) 8 (36.4) 27 (6.6) 7.497 (3.138–17.912) <0.0001
Heart rate, beats/min
    ≤80 124 (28.7) 3 (13.6) 121 (29.5) 1 (ref) 1 (ref)
    >80 308 (71.3) 19 (86.4) 289 (70.5) 2.648 (0.784–8.951) 0.117
Respiratory rate, breaths/min
    ≤20 208 (48.1) 4 (18.2) 204 (49.8) 1 (ref) 1 (ref)
    >20 224 (51.9) 18 (81.8) 206 (50.2) 4.174 (1.413–12.336) 0.010
Lymphocyte count (<1.1×109/L)
    ≥1.1 248 (57.4) 3 (13.6) 245 (59.8) 1 (ref) 1 (ref)
    <1.1 184 (42.6) 19 (86.4) 165 (40.2) 7.928 (2.346–26.800) 0.001
CD4+ T cells, cell/μL*
    ≥550 61 (58.1) 0 (0.0) 61 (67.8) 1 (ref) 1 (ref)
    <550 44 (41.9) 15 (100.0) 29 (32.2) 96.693 (1.364–6853.906) 0.035
CD8+ T cells, cells/μL*
    ≥320 58 (55.2) 0 (0.0) 58 (64.4) 1 (ref) 1 (ref)
    <320 47 (44.8) 15 (100.0) 32 (35.6) 88.356 (1.300–6004.710) 0.037
Hemoglobin, g/L
    ≥90 418 (96.8) 18 (81.8) 400 (97.6) 1 (ref) 1 (ref)
    <90 14 (3.2) 4 (18.2) 10 (2.4) 11.775 (3.946–35.143) <0.0001
Platelet count, ×109/L
    ≥100 371 (85.9) 13 (59.1) 358 (87.3) 1 (ref) 1 (ref)
    <100 61 (14.1) 9 (40.9) 52 (12.7) 4.646 (1.985–10.874) <0.0001
Albumin, g/L
    ≥35 303 (70.1) 6 (27.3) 297 (72.4) 1 (ref) 1 (ref)
    <35 129 (29.9) 16 (72.7) 113 (27.6) 5.650 (2.209–14.450) <0.0001
Total bilirubin, μmol/L
    ≤26 419 (97.0) 20 (90.9) 399 (97.3) 1 (ref) 1 (ref)
    >26 13 (3.0) 2 (9.1) 11 (2.7) 3.624 (0.846–15.525) 0.083
Direct bilirubin, μmol/L
    ≤8 416 (96.3) 19 (86.4) 397 (96.8) 1 (ref) 1 (ref)
    >8 16 (3.7) 3 (13.6) 13 (3.2) 4.230 (1.251–14.301) 0.020
Aspartate aminotransferase, U/L
    ≤40 373 (86.3) 12 (54.5) 361 (88.0) 1 (ref) 1 (ref)
    >40 59 (13.7) 10 (45.5) 49 (12.0) 4.873 (2.104–11.289) <0.0001
Cystatin C, mg/L*
    ≤1.55 110 (87.3) 10 (62.5) 100 (99.9) 1 (ref) 1 (ref)
    >1.55 16 (12.7) 6 (37.5) 10 (9.1) 4.974 (1.806–13.703) 0.002
Creatine kinase-MB, U/L
    ≤8 159 (39.6) 3 (13.6) 156 (39.6) 1 (ref) 1 (ref)
    >8 224 (58.9) 19 (86.4) 224 (58.9) 4.868 (1.437–16.493) 0.011
Cardiac troponin I, pg/mL
    ≤35 132 (93.6) 13 (81.3) 119 (95.2) 1 (ref) 1 (ref)
    <35 9 (6.4) 3 (18.8) 6 (4.8) 3.056 (0.869–10.741) 0.082
Brain natriuretic peptide, pg/mL*
    ≤500 148 (82.2) 7 (43.8) 141 (86.0) 1 (ref) 1 (ref)
    500–1000 14 (7.8) 5 (31.3) 9 (5.5) 9.367 (2.966–29.581) <0.0001
    >1000 18 (10.0) 4 (25.0) 14 (8.5) 6.552 (1.908–22.500) <0.0001
Creatine kinase, U/L
    ≤190 382 (92.3) 16 (72.7) 366 (93.4) 1 (ref) 1 (ref)
    >190 32 (7.7) 6 (27.3) 26 (6.6) 4.067 (1.590–10.403) 0.003
Lactate dehydrogenase, U/L
    ≤245 277 (67.9) 6 (27.3) 271 (70.2) 1 (ref) 1 (ref)
    >245 131 (32.1) 16 (72.7) 115 (29.8) 5.326 (2.083–13.619) <0.0001
C-reactive protein, mg/L
    ≤10 185 (44.3) 4 (18.2) 181 (45.7) 1 (ref) 1 (ref)
    >10 233 (55.7) 18 (81.8) 215 (54.3) 3.650 (1.235–10.784) 0.019
Erythrocyte sedimentation rate, mm/h*
    ≤20 119 (39.5) 2 (10.0) 117 (41.6) 1 (ref) 1 (ref)
    >20 182 (60.5) 18 (90.0) 164 (58.4) 5.103 (1.83–22.013) 0.029
Procalcitonin, ng/mL
    ≤0.5 373 (94.7) 19 (86.4) 354 (95.2) 1 (ref) 1 (ref)
    >0.5 21 (5.3) 3 (13.6) 18 (4.8) 3.711 (1.094–12.592) 0.035
Interleukin-2 receptor, U/mL*
    ≤710 84 (76.4) 2 (22.2) 82 (81.2) 1 (ref) 1 (ref)
    >710 26 (23.6) 7 (77.8) 19 (18.8) 14.062 (2.893–68.359) 0.001
Interleukin-6, pg/mL*
    ≤7 77 (70.0) 0 (0.0) 77 (76.2) 1 (ref) 1 (ref)
    >7 33 (30.0) 9 (100.0) 24 (23.8) 402.610 (0.198–816985.268) 0.123
Interleukin-8, pg/mL*
    ≤62 107 (97.3) 7 (77.8) 100 (90.0) 1 (ref) 1 (ref)
    >62 3 (2.7) 2 (22.2) 1 (1.0) 13.434 (2.762–65.339) 0.001
Interleukin-10, pg/mL*
    ≤9 103 (93.6) 7 (77.8) 96 (95.0) 1 (ref) 1 (ref)
    >9 7 (6.4) 2 (22.2) 5 (5.0) 5.028 (1.025–24.674) 0.047
D-dimer, μg/ml
    ≤0.5 168 (39.9) 3 (13.6) 165 (41.4) 1 (ref) 1 (ref)
    0.5–1.0 178 (42.3) 7 (31.8) 171 (42.9) 2.78 (0.563–8.428) 0.259
    >1.0 75 (17.8) 12 (54.5) 63 (15.8) 9.825 (2.770–34.853) <0.0001
PaO2, mmHg*
    ≥80 149 (70.0) 5 (22.7) 144 (75.4) 1 (ref) 1 (ref)
    60–79.9 50 (23.5) 11 (50.0) 39 (20.4) 6.456 (2.243–18.580) 0.001
    <60 14 (6.6) 6 (27.3) 8 (4.2) 13.916 (4.245–45.621) <0.0001
PaO2/FiO2, mmHg*
    ≥200 114 (53.5) 0 (0.0) 114 (59.7) 1 (ref) 1 (ref)
    <200 99 (46.5) 22 (100.0) 77 (40.3) 72.483 (2.333–2251.514) 0.015
CT consolidation 97 (23.3) 10 (45.5) 87 (22.1) 2.817 (1.217–6.521) 0.016
CT score
    ≤10 369 (88.7) 5 (22.7) 364 (92.4) 1 (ref) 1 (ref)
    >10 47 (11.3) 17 (77.3) 30 (7.6) 29.171 (10.759–79.092) <0.0001
Open in a new tab

*: More than 20% missing values existed in this variable.

ref: reference.

Before performing multivariate Cox regression analysis, we excluded CD4+ and CD8+ T cells, cystatin C, BNP, ESR, IL-2, IL-6, IL-8, IL-10, PaO2, and PaO2/FiO2 as they had more than 20% missing values (see details in Table 5); subsequently, the remaining variables that had statistical significance in univariate analysis were entered into the multivariate Cox regression analysis. We found that Hb<90 g/L, CK-MB >8 U/L, LDH > 245 U/L, PCT >0.5 ng/mL, and CT score >10 points were predictive of mortality (all p<0.01) (Table 6).

Table 6. Multivariate Cox regression analysis of factors associated with mortality.

Variables Hazard Risk (95% confidence interval) P value
Hemoglobin <90 g/L 10.776 (3.075–37.766) <0.0001
Creatine kinase-MB >8 U/L 9.155 (2.424–34.584) 0.001
Lactate dehydrogenase >245 U/L 5.963 (2.029–17.529) 0.001
Procalcitonin >0.5 ng/mL 7.080 (1.671–29.992) 0.008
CT score >10 39.503 (12.430–125.539) <0.0001
Open in a new tab

Except for variables, such as CD4+ and CD8+ T cells, cystatin C, brain natriuretic peptide, erythrocyte sedimentation rate, interleukin-2, -6, -8, -10, PaO2, and PaO2/FiO2 that had more than 20% missing values (see details in Table 5), all variables that were statistically different in univariate Cox regression analysis were entered into multivariate analysis.

Characteristics of severe and non-severe COVID-19 patients

The differences in the demographic and clinical data, laboratory findings, CT image results, treatment approaches, and complications between the severe and non-severe COVID-19 patients are outlined in S1–S4 Tables.

Discussion

The present study compared the demographic and clinical data, laboratory findings, radiological characteristics, and complications between surviving and non-surviving COVID-19 patients and evaluated the risk factors for mortality in two centers of Hubei province, China. The results showed that as compared with survivors, non-survivors were older; and they had a higher frequency of comorbidities, decreased lymphocyte count with lower T cell subsets, and Hb, platelet, and albumin levels. Biomarkers of inflammation, cytokines, liver and renal dysfunction, and cardiac and muscle injury were also markedly increased in non-surviving patients. A higher CT score accompanied by a higher rate of consolidation was found in dead patients. More non-surviving patients developed ARDS and died of respiratory failure. Multivariate Cox regression analysis demonstrated that low Hb (<90 g/L), high CK-MB (>8 U/L), LDH (>245 U/L), PCT (>0.5 ng/ml), and CT score (>10 points) were closely associated with the mortality in patients with COVID-19.

At the early stage in 2020, the mortality rate of COVID-19 was nearly 3.7–5.4% [5–7]. The mortality rate increased up to 52.4% in patients suffering from ARDS [9] and 61.5% among severe and critical cases [8]. A total of 5.09% of patients died among our study population at two centers in Hubei province, which was consistent with previous studies from China. The mortality rate increased to 17.6% among patients with severe and critical types (S1 Table), which was significantly lower than the early published data. We suspected that this discrepancy was related to the relatively abundant treatment experience during the later period of this pandemic. Our data showed that non-surviving patients were older and had underlying diseases, which was similar to that in most of the published studies [6, 9, 12, 13, 16, 17]. As reported in previous studies presented [3], the top 3 common symptoms were fever, cough, and fatigue. There were no differences in gender, symptoms except dyspnea, and illness onset to admission between the survivor and non-survivor groups [11]. Contradictory to one previous study [10], we failed to find any difference in angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB) drug administration between the two groups.

SARS-CoV-2 can cause systemic multiple organ dysfunction mainly in the lungs by binding to the angiotensin-converting enzyme 2 (ACE2) receptors [4], resulting in cytokine storm and immune damage. In the present study, several aberrant biomarkers that represent organ dysfunction were detected. As compared with surviving patients, non-surviving patients had lower lymphocyte count. The subsets of lymphocytes, and CD4+ and CD8+ T cells, were also dramatically decreased in non-survivors. As the biomarkers of cytokine storm, levels of most of the ILs were increased among COVID patients. The levels of cytokines were significantly higher in non-survivors as compared with survivors. The levels of inflammatory biomarkers, including CRP, ESR, and PCT, were also markedly higher in non-survivors. Furthermore, we observed that high levels of PCT (>0.5 ng/mL) were an important poor prognostic predictor; thus, confirming the results of published data [6, 13]. The results of the above-mentioned biomarkers representing cytokine storm and inflammation were consistent with those in several previous studies [11, 13, 18]. Abnormal laboratory findings in non-survivors included increased total and direct bilirubin, AST, cystatin C, cardiac troponin I, CK-MB, BNP, and D-dimer, indicating aberrant hepatic damage, kidney insufficiency, myocardial injury, and aberrant coagulation, respectively. Although most of the biochemical variables were not entered into the multivariate Cox regression model, CK-MB >8 U/L was one of the predictors of mortality, suggesting that COVID-19 patients who develop acute cardiac injury are prone to have a poor prognosis [13, 16, 18]. In our study, hypoproteinemia and anemia were more frequently detected in patients who did not survive [5]. Moreover, multivariate Cox regression analysis showed that moderate anemia (Hb <90 g/L) was an independent risk factor of mortality. We also found that there were increased LDH and creatine kinase levels in dead and severe cases. LDH (>245 U/L) was also found to be a predictor of mortality of COVID-19 patients. The results confirmed the previous conclusion that LDH and creatine kinase can be prognostic biomarkers in COVID-19 patients [12, 19]. We observed that decreased PaO2 and PaO2: FiO2 were detected in non-surviving COVID-19 patients, suggesting that SARS-CoV-2 mainly caused acute lung injury, especially in severe cases.

In accordance with most of the recent studies [3, 10], the most common CT features of COVID-19 included bilateral ground-glass opacity and consolidation. High rate of consolidation was found in non-surviving patients. Furthermore, we evaluated the CT features with a semi-quantitative score [20, 21]. Undoubtedly, an increased CT score was detected in dead patients. Multivariate Cox regression showed that CT score >10 was an independent risk factor of mortality, indicating that more lung lobes were involved in non-survivors. This finding was consistent with previous research results [3, 10]. Pleural effusion and pleural thickening were less frequently observed in our current study.

No effective therapeutic approach against SARS-CoV-2 has been confirmed. In this retrospective analysis study, 96.5% of patients received antiviral treatment. The antiviral drugs included umifenovir, interferon 〈 nebulization, lopinavir/ritonavir, ribavirin, chloroquine, hydroxychloroquine, and oseltamivir. No significant difference in antiviral drug usage was detected between survivors and non-survivors. Our results were consistent with those of previous studies [10, 17]; thus, confirming that there is no effective drug against SARS-CoV-2. Regarding antibiotic use, we found that no significant difference between survivors and non-survivors. However, the overall rate of using antibiotics in this study and previous other studies was significantly high, indicating that the clinician was more prone to combining antiviral and antibiotic therapies in the current pandemic, especially in those with severe and critical types.

Cytokine storm and systemic inflammation were considered as the novel physiological features of COVID-19. Some therapeutic approaches were attempted to attenuate these features. Evidence has shown that corticosteroids can reduce overreaction of inflammation and cytokine storm; low dose (1–2 mg/kg) of corticosteroids for 3–5 days is recommended in guideline [14]. But the disadvantages are also obvious as they can inhibit the immune function; thus, increasing the chances of secondary infection and prolonging the viral shedding period. There were more number of patients receiving corticosteroid treatment among non-survivors in our study. In addition, intravenous immunoglobulin was also commonly used in non-surviving patients. Our results demonstrated that a high frequency of systemic inflammation and cytokine storm occurred in deceased patients. Clinicians tried using corticosteroids and immunoglobulin to reverse these conditions, but all of these therapies were ineffective.

Yang X et al. [8] demonstrated that 81.0% of non-survivors developed ARDS among the critically ill COVID-19 patients. In accordance with previous studies [8, 9, 22], we found that ARDS (81.8%) was the topmost cause of death. It has been shown that SARS-CoV-2 binds to the ACE2 receptor to invade host cells, especially in the lungs, kidney, heart, contributing to cytokine storm and inflammatory state, both of which are implicated in multi-organ dysfunction [23]. Acute cardiac injury, acute hepatic insufficiency [17], acute kidney injury, and thrombocytopenia were also commonly detected in deceased COVID-19 patients. A systemic review and meta-analysis illustrated that acute cardiac injury and acute kidney injury are tightly associated with an increased risk of COVID-19 related mortality [16]. A study by Cao J et al. [17] showed 13 cases of acute liver injury in 17 non-survivors. Based on the results of our study and previous studies, we confirmed that multi-organ dysfunction involving the lung, heart, liver, kidney, and coagulation caused by SARS-CoV-2 [24], was more frequently observed in the deceased patients.

Several limitations of our study should be mentioned. First, since this was a retrospective study at two centers of Hubei province, all laboratory tests were not performed on all of the patients. Therefore, the effect of missing variables might have been underestimated in the prediction of mortality. Second, the insufficiency of viral RNA detection may have resulted in inaccuracy of viral shedding. Finally, interpretation of our results might have been limited by the small sample size in the non-survivor group.

In conclusion, our study found that COVID-19 patients who did not survive were old and they had more underlying diseases. Several aberrant laboratory findings, which indicated cytokine storm, inflammation, acute cardiac injury, acute hepatic damage, and acute renal insufficiency, were also detected in non-surviving patients. Death in most of the deceased patients resulted from ARDS. Cox regression analysis was conducted to identify the following 5 predictors: Hb <90 g/L, CK-MB >8 U/L, LDH >245 U/L, PCT >0.5 ng/ml, and CT score >10, of increased mortality among the overall population of COVID-19 patients. The results of our study confirmed the previous findings, and they highlighted early biomarkers for the risk of mortality in patients with COVID-19. Furthermore, our study provided evidence for early intervention and reasonable allocation of medical resources in this global pandemic.

Supporting information

S1 Table. Demography and clinical characteristics of different severity of COVID-19 patients.

(DOCX)

Click here for additional data file. (16.3KB, docx)
S2 Table. Laboratory findings of COVID-19 patients between survivors and non-survivors.

(DOCX)

Click here for additional data file. (17.3KB, docx)
S3 Table. CT image results of different severity of COVID-19 patients*.

(DOCX)

Click here for additional data file. (13.2KB, docx)
S4 Table. Treatment and complications between severe and non-severe COVID-19 patients.

(DOCX)

Click here for additional data file. (15.4KB, docx)
S5 Table. Normal range of laboratory findings of COVID-19 patients.

(DOCX)

Click here for additional data file. (14.5KB, docx)

Acknowledgments

Declarations

We sincerely appreciate all front-line medical staff for their hard work and sacrifice.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This work was supported by Grant 2018-2-65 for Youth Research Fund from Fujian Provincial Health Bureau, Grant 2020GGB057 for Young people training project from Fujian Province Health Bureau, and Grant 2018J01393 for Fund from Natural Science Foundation of Fujian Province, China.

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Decision Letter 0

Alexandra Lucas

21 Dec 2020

PONE-D-20-31483

Risk factors for mortality of coronavirus disease-2019 (COVID-19) patients in two centers of Hubei province, China: a retrospective analysis

PLOS ONE

Dear Dr. Zhang

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The following reviews were submitted by external reviewers and the consensus was that this manuscript is requires minor revisions. Please respond ti the reviewers' comments as noted below and submit a revised manuscript in the form as outlined buy PLoS One editorial office. We look forward to receiving your revised manuscript. 

Reviewer 1 - ACADEMIC EDITOR: Love the paper and like the information but could really benefit from a native English speaker edit

----

Line 27--We aim to is awkward-- We analyzed

Line 43- Use standard abbreviation for hemoglobin (Hb) of (Hgb). 

Line 65- I think you should just change dissimilarity to homology. I thin you misinterpreted the original 'Notably, 2019-nCoV was closely related (with 88% identity) to two bat-derived severe acute respiratory syndrome (SARS)-like coronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21, collected in 2018 in Zhoushan, eastern China, but were more distant from SARS-CoV (about 79%) and MERS-CoV (about 50%).'

Line 80- the tense is odd..Instead of We tried. ...We provide

these things continue but as above-just a good edit

Line 118. is it CK or CK-MB?  Would also include normal ranges for all measurements

Reviewer 2 Comments

Zhang et al. report clinical characteristics of 432 COVID-19 patients from two hospitals in Wuhan, China. The authors stratify their analyses by survivors (n=410) and non-survivors(n=22). Using multivariate Cox regression analyses, the authors determined that hemoglobin, creatine kinase-MB, lactate dehydrogenase and procalcitonin levels are predictors of COVID-19 mortality. This is consistent with similar published reports. Small number of non-survivors group is a limitation of the study, not acknowledged in discussion. Nonetheless, the data on the survivors group is informative and worth publishing. Typically, predictive models using the identified risk factors are presented in these studies (reviewer does not think this is needed for acceptance, but that would be helpful). Authors should address the following:

1. Please provide details on laboratory testing methodologies (lines 115-122).

2. Please clarify “ref” values in Table 5.

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Reviewer #1: Love the paper and like the information but could really benefit from a native English speaker edit

----

Line 27--We aim to is awkward-- We analyzed

Line 43- Use standard abbreviation for hemoglobin (Hb) of (Hgb).

Line 65- I think you should just change dissimilarity to homology. I thin you misinterpreted the original 'Notably, 2019-nCoV was closely related (with 88% identity) to two bat-derived severe acute respiratory syndrome (SARS)-like coronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21, collected in 2018 in Zhoushan, eastern China, but were more distant from SARS-CoV (about 79%) and MERS-CoV (about 50%).'

Line 80- the tense is odd..Instead of We tried. ...We provide

these things continue but as above-just a good edit

Line 118. is it CK or CK-MB? Would also include normal ranges for all measurements

Reviewer #2: Zhang et al. report clinical characteristics of 432 COVID-19 patients from two hospitals in Wuhan, China. The authors stratify their analyses by survivors (n=410) and non-survivors(n=22). Using multivariate Cox regression analyses, the authors determined that hemoglobin, creatine kinase-MB, lactate dehydrogenase and procalcitonin levels are predictors of COVID-19 mortality. This is consistent with similar published reports. Small number of non-survivors group is a limitation of the study, not acknowledged in discussion. Nonetheless, the data on the survivors group is informative and worth publishing. Typically, predictive models using the identified risk factors are presented in these studies (reviewer does not think this is needed for acceptance, but that would be helpful). Authors should address the following:

1. Please provide details on laboratory testing methodologies (lines 115-122).

2. Please clarify “ref” values in Table 5.

**********

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Reviewer #1: Yes: Daniel O Griffin

Reviewer #2: No

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PLoS One. 2021 Jan 28;16(1):e0246030. doi: 10.1371/journal.pone.0246030.r002

Author response to Decision Letter 0

7 Jan 2021

Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Xiamen University;

Teaching Hospital of Fujian Medical University;

No.201, Siming District, Xiamen, Fujian Province, 361004, People's Republic of China.

Jan,12th, 2021

Re: Manuscript No. PONE-D-20-31483

Title: Risk factors for mortality of coronavirus disease-2019 (COVID-19) patients in two centers of Hubei province, China: a retrospective analysis

Dear Editor Alexandra Lucas,

Thank you for the timely review of our manuscript.

We have attached our point-by-point responses to the reviewers’ and editors’ suggestions and have also responded to the reviewers’ comments in the text by using the ‘track changes’ function in the ‘revised revision’.

This manuscript has been edited and proofread by Medjaden Bioscience Limited.

We hope that the revised manuscript is now acceptable for publication in your journal.

I look forward to hearing from you soon.

Best regards!

Sincerely yours,

Corresponding author: Xiao-Bin Zhang

Email: zhangxiaobincn@xmu.edu.cn

Response to the reviewers and editor's comments

Response to the reviewers' comments

Reviewer 1 - ACADEMIC EDITOR:

Comment 1: Love the paper and like the information but could really benefit from a native English speaker edit

Response 1: Thank you for your kind comment. Our manuscript has been edited by a native English speaker editor working in a language editing company (Medjaden Incorporate, www.medjaden.com). The language certificate is attached for your reference.

Comment 2: Line 27--We aim to is awkward-- We analyzed

Response 2: Thank you for your valuable suggestion. We have re-written this sentence according to your suggestion.

Comment 3: Line 43- Use standard abbreviation for hemoglobin (Hb) of (Hgb).

Response 3: Thank you for your valuable suggestion. The abbreviation for hemoglobin has been changed to Hb in the whole manuscript.

Comment 4: Line 65- I think you should just change dissimilarity to homology. I thin you misinterpreted the original 'Notably, 2019-nCoV was closely related (with 88% identity) to two bat-derived severe acute respiratory syndrome (SARS)-like coronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21, collected in 2018 in Zhoushan, eastern China, but were more distant from SARS-CoV (about 79%) and MERS-CoV (about 50%).'

Response 4: Thank you for your valuable comment. We apologized for our mistake. The relevant context has been corrected according to your comment as follows: “Being a beta coronavirus, the SARS-CoV-2 virus was closely related (with 88% identity) to two bat-derived severe acute respiratory syndrome (SARS)-like coronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21, collected in 2018 in Zhoushan, eastern China, but were more distant from SARS-CoV (about 79%) and MERS-CoV (about 50%)”.

Comment 5: Line 80- the tense is odd..Instead of We tried. ...We provide these things continue but as above-just a good edit

Response 5: Thank you for your valuable suggestion. This manuscript has been edited and proofread by Medjaden Bioscience Limited to ensure that it is readable.

Comment 6: Line 118. is it CK or CK-MB? Would also include normal ranges for all measurements

Response 6: Thank you for your thoughtful question and valuable suggestion. Both CK and CK-MB were analyzed in our manuscript. The normal ranges for all measurements are outlined in eTable 5.

Reviewer 2 Comments

Comment 1: Zhang et al. report clinical characteristics of 432 COVID-19 patients from two hospitals in Wuhan, China. The authors stratify their analyses by survivors (n=410) and non-survivors(n=22). Using multivariate Cox regression analyses, the authors determined that hemoglobin, creatine kinase-MB, lactate dehydrogenase and procalcitonin levels are predictors of COVID-19 mortality. This is consistent with similar published reports. Small number of non-survivors group is a limitation of the study, not acknowledged in discussion. Nonetheless, the data on the survivors group is informative and worth publishing. Typically, predictive models using the identified risk factors are presented in these studies (reviewer does not think this is needed for acceptance, but that would be helpful).

Response 1: Thank you for your positive feedback. We have acknowledged that our manuscript has several limitations, such as retrospective design, lack of several biochemical markers, and small number of patients in the non-survivor group. The small number of patients in the non-survivor group of our study has been included as a limitation in the Discussion section of the revised manuscript. Since we analyzed the predictive factors of survival in COVID-19 patients, Cox regression was considered the first choice (J Allergy Clin Immunol. 2020 Jul;146(1):110-118).

Authors should address the following:

Comment 2. Please provide details on laboratory testing methodologies (lines 115-122).

Response 2: Thank you for your valuable suggestion. The methodologies of all laboratory tests have been presented in the “Methods” section of the revised manuscript.

Comment 3. Please clarify “ref” values in Table 5.

Response 3: Thank you for your thoughtful comment. The “ref” value in Table 5 is 1.0 (Lancet 2020; 395:1054-1062). This issue has been corrected in the revised manuscript.

Response to the editor's comments

Comment 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response 1: Thank you for your valuable comment. The manuscript has been modified according to the journal style.

Comment 2. Thank you for stating the following in the Acknowledgments Section of your manuscript:

" The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

"This work was supported by Grant 2018-2-65 for Youth Research Fund

from Fujian Provincial Health Bureau, and Grant 2018J01393 for Fund from Natural

Science Foundation of Fujian Province, China."

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

Response 2: Thank you for your valuable suggestion. The Funding information has been deleted from the Acknowledgements section and it has not been presented in any other sections of our manuscript. Meanwhile, we have included the Funding information in the cover letter as follows: “This work was supported by Grant 2018-2-65 for Youth Research Fund from Fujian Provincial Health Bureau, Grant 2020GGB057 for Young people training project from Fujian Province Health Bureau, and Grant 2018J01393 for Fund from Natural Science Foundation of Fujian Province, China.”

Comment 3. Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please move it to the Methods section and delete it from any other section. Please ensure that your ethics statement is included in your manuscript, as the ethics statement entered into the online submission form will not be published alongside your manuscript.

Response 3: Thank you for your valuable suggestion. Our ethics statement only appears in the “Methods” section of the revised manuscript.

We would like to take this opportunity to express our gratitude and appreciation for the meticulous and professional comments provided by the reviewers and editors that have contributed greatly to improving our work.

Attachment

Submitted filename: Response to Reviewers.doc

Click here for additional data file. (306.5KB, doc)

Decision Letter 1

Alexandra Lucas

13 Jan 2021

Risk factors for mortality of coronavirus disease-2019 (COVID-19) patients in two centers of Hubei province, China: a retrospective analysis

PONE-D-20-31483R1

Dear Dr. Zhang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Alexandra Lucas

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Alexandra Lucas

18 Jan 2021

PONE-D-20-31483R1

Risk factors for mortality of coronavirus disease-2019 (COVID-19) patients in two centers of Hubei province, China: a retrospective analysis

Dear Dr. Zhang:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

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Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Demography and clinical characteristics of different severity of COVID-19 patients.

    (DOCX)

    Click here for additional data file. (16.3KB, docx)
    S2 Table. Laboratory findings of COVID-19 patients between survivors and non-survivors.

    (DOCX)

    Click here for additional data file. (17.3KB, docx)
    S3 Table. CT image results of different severity of COVID-19 patients*.

    (DOCX)

    Click here for additional data file. (13.2KB, docx)
    S4 Table. Treatment and complications between severe and non-severe COVID-19 patients.

    (DOCX)

    Click here for additional data file. (15.4KB, docx)
    S5 Table. Normal range of laboratory findings of COVID-19 patients.

    (DOCX)

    Click here for additional data file. (14.5KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.doc

    Click here for additional data file. (306.5KB, doc)

    Data Availability Statement

    All relevant data are within the manuscript and its Supporting Information files.

    Articles from PLoS ONE are provided here courtesy of PLOS

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