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. 2020 Nov 30;15(11):e0243124. doi: 10.1371/journal.pone.0243124

Risk factors for predicting mortality of COVID-19 patients: A systematic review and meta-analysis

Lan Yang 1,#, Jing Jin 1,#, Wenxin Luo 1, Yuncui Gan 1, Bojiang Chen 1,‡,*, Weimin Li 1,‡,*
Editor: Raffaele Serra2
PMCID: PMC7703957  PMID: 33253244

Abstract

Background

Early and accurate prognosis prediction of the patients was urgently warranted due to the widespread popularity of COVID-19. We performed a meta-analysis aimed at comprehensively summarizing the clinical characteristics and laboratory abnormalities correlated with increased risk of mortality in COVID-19 patients.

Methods

PubMed, Scopus, Web of Science, and Embase were systematically searched for studies considering the relationship between COVID-19 and mortality up to 4 June 2020. Data were extracted including clinical characteristics and laboratory examination.

Results

Thirty-one studies involving 9407 COVID-19 patients were included. Dyspnea (OR = 4.52, 95%CI [3.15, 6.48], P < 0.001), chest tightness (OR = 2.50, 95%CI [1.78, 3.52], P<0.001), hemoptysis (OR = 2.00, 95%CI [1.02, 3.93], P = 0.045), expectoration (OR = 1.52, 95%CI [1.17, 1.97], P = 0.002) and fatigue (OR = 1.27, 95%CI [1.09, 1.48], P = 0.003) were significantly related to increased risk of mortality in COVID-19 patients. Furthermore, increased pretreatment absolute leukocyte count (OR = 11.11, 95%CI [6.85,18.03], P<0.001) and decreased pretreatment absolute lymphocyte count (OR = 9.83, 95%CI [6.72, 14.38], P<0.001) were also associated with increased mortality of COVID-19. We also compared the mean value of them between survivors and non-survivors, and found that non-survivors showed significantly raise in pretreatment absolute leukocyte count (WMD: 3.27×109/L, 95%CI [2.34, 4.21], P<0.001) and reduction in pretreatment absolute lymphocyte count (WMD = -0.39×109/L, 95%CI [-0.46, -0.33], P<0.001) compared with survivors. The results of pretreatment lactate dehydrogenase (LDH), procalcitonin (PCT), D-Dimer and ferritin showed the similar trend with pretreatment absolute leukocyte count.

Conclusions

Among the common symptoms of COVID-19 infections, fatigue, expectoration, hemoptysis, dyspnea and chest tightness were independent predictors of death. As for laboratory examinations, significantly increased pretreatment absolute leukocytosis count, LDH, PCT, D-Dimer and ferritin, and decreased pretreatment absolute lymphocyte count were found in non-survivors, which also have an unbeneficial impact on mortality among COVID-19 patients. Motoring these indicators during the hospitalization plays a very important role in predicting the prognosis of patients.

Introduction

Since December 2019, the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia has caused more than 8 million infections, with 440,290 deaths worldwide until June 17th, 2020 [1]. Increasing evidence is investigating the clinical features and laboratory abnormalities in patients with COVID-19 infection. Considering the widespread of COVID-19, early and accurate prognosis prediction is urgently warranted. However, the specific symptoms and laboratory biomarkers which may help predict the poor prognosis of COVID-19 patients were unclear. Therefore, we aimed to perform a systematic meta-analysis to summarize the clinical characteristics and laboratory test before treatment among COVID-19 patients and identify the possible risk factors for mortality.

Materials and methods

Search strategy

We conducted a systematic search in PubMed, Scopus, Web of Science and Embase to identify studies in patients with COVID-19 infection up to 4 June 2020. The following keywords were used: “2019 novel coronavirus disease”, “severe acute respiratory syndrome coronavirus 2”, “COVID-19”, “2019-nCoV”, “SARS-CoV-2” and “clinical”, “laboratory”, “risk factor”, and “mortality”, “mortal”, “fatality”, “fatal”, “lethality” or “death”. No restrictions on publication status were imposed. Only studies published in English and Chinese were retrieved for this meta-analysis. In addition, reference lists of relevant records were manually screened for further potentially eligible articles.

Inclusion criteria and exclusion criteria

Two researchers reviewed all articles independently based on titles and abstracts. The inclusion criteria were as follows: 1) all patients were confirmed with COVID-19; 2) studies reported the clinical characteristics, hematological and serological abnormalities both in survivors and non-survivors.

The exclusion criteria were as follows: 1) reviews, letters, case reports, conference abstracts and duplicated publications; 2) insufficient data were provided for extrapolating the mean±SD for hematologic parameters.

Data extraction and assessment of risk of bias

Studies that met the inclusion and exclusion criteria underwent full-text rescreening. Data extraction was performed by two investigators independently. The following data were collected: the name of first author, publication year, region of studies, number of the patients with COVID-19, clinical characteristics together with of the laboratory examination in each group. Continuous data were extracted as mean ± standard deviation (SD). While data were expressed as median, range and/or interquartile range (IQR), mean and SD were extrapolated according to Wan et al. [2]. Any disagreements were resolved via discussion and consensus. The risk of bias of each included study was assessed by utilizing the MINORS score [3].

Statistical analysis

ORs together with the weighted mean difference (WMD) and the 95% confidence interval (CI) were merged and we assessed heterogeneity by using Cochran’s Q statistic test and the I2 statistic. When p-values for heterogeneity were no greater than 0.05 or I2 value exceeded 50%, random-model was applied. Otherwise, the fixed-effects model was adopted. We explored the publication bias by the Egger’s regression test and the funnel plot. All statistical analyses were conducted by Review Manager (version 5.3), and R (version 3.6.1). Two-tailed P values ≤0.05 were considered statistically significant.

Results

Literature search and assessment of risk of bias

A total of 3093 potentially relevant publications were yielded according to our search strategy from PubMed, Scopus, Web of Science and Embase up to 4 June 2020. One additional relevant study was identified from the reference list of included articles. We discarded 1177 articles as duplicates. Two researchers reviewed 1917 articles based on titles and abstracts. After 1839 irrelevant records were excluded, we screened the full text versions of the remaining 78 articles. The following studies were eliminated: reviews, meta-analyses or case reports and studies lacking sufficient data for further analysis. Ultimately, thirty-one qualified articles [4–34] were included in this meta-analysis. The detailed process of the literature search was presented in Fig 1. All included studies were non-randomized. The MINORS scores varied between 18 and 21, suggesting a low risk of bias overall (Table 1 and S1 Table).

Fig 1. Flow chart of the literature search.

Fig 1

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Table 1. Characteristics of all included studies.

Author Year Country City MINORS score Total
S NS		 Age
S NS		 Male (%)
S NS		 Hypertension (%)
S NS		 Diabetes (%)
S NS		 Malignancy (%)
S NS		 CCD (%)
S NS		 CB (%)
S NS		 CRD (%)
S NS		 COPD (%)
S NS
Cao JL 2020 China Wuhan 18 85 17 53 (47, 66) 72 (63, 81) 47 77 20 65 6 35 4 6 2 18 4 18 1 18 NA NA
Chen RC 2020 China Guangzhou 19 445 103 53.5 (13.9) 66.9 (12.1) 55 67 23 44 9 19 3 3 NA NA 2 8 3 2 0 5
Chen RC (2) 2020 China Guangzhou 21 1540 50 48 (1–94) a 69 (51–86) a 57 78 16 56 8 26 1 6 NA NA 2 12 1 10 1 12
Chen T 2020 China Wuhan 21 161 113 51 (37, 66) 68 (62, 77) 55 74 24 48 14 21 1 4 4 14 0 4 1 4 NA NA
Deng Y 2020 China Wuhan 18 116 109 40 (33, 57) 69 (62, 74) 44 67 16 37 8 16 2 6 3 12 NA NA NA NA NA NA
Du RH 2020 China Wuhan 19 158 21 56 (13.5) 70.2 (7.7) 55 48 29 62 17 29 2 5 NA NA NA NA NA NA NA NA
Fan H * 2020 China Wuhan 19 26 47 46.2 (12) 65.5 (9.7) 65 68 12 45 NA NA NA NA NA NA NA NA NA NA NA NA
Goicoechea M 2020 Spain Madrid 18 25 11 69 (14) 75 (6) 68 55 100 91 68 55 NA NA NA NA NA NA NA NA 32 9
Giacomelli A 2020 Italy Milan 19 185 48 NA NA 34 19 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Huang J 2020 China Yichang 18 283 16 52.5 (16.6) 69.2 (9.7) 53 69 22 69 11 25 2 25 NA NA 4 13 NA NA 2 19
Javanian M 2020 Iran Babol 18 81 19 57.7 (13.6) 69.3 (11.1) 49 57 25 63 33 53 1 16 15 42 1 11 9 26 9 26
Li LL 2020 China Wuhan 19 68 25 43.7 (13.1) 69 (10.5) 38 60 0 20 9 20 4 4 0 16 NA NA NA NA 9 8
Nowak B 2020 Poland Warsaw 18 123 46 59.3 (20.1) 75.3 (11.9) 46 65 43 59 13 35 16 33 29 48 NA NA 22 17 11 20
Ruan QR 2020 China Wuhan 19 82 68 50 (44, 81) 67 (15, 81) 65 72 28 43 16 18 1 3 0 19 6 10 0 3 1 3
Shi Q 2020 China Wuhan 21 259 47 NA NA 47 60 38 68 NA NA 4 9 12 36 3 15 3 11 NA NA
Shi SB * 2020 China Shanghai 21 609 62 61 (49, 70) 74 (66, 81) 47 57 27 60 13 27 3 7 NA NA 2 13 3 19 3 3
Sun H 2020 China Wuhan 18 123 121 67 (64, 72) 72 (66, 78) 42 68 50 63 20 23 NA NA NA NA NA NA NA NA NA NA
Tang N 2020 China Wuhan 19 162 21 52.4 (15.6) 64 (20.7) 51 76 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Wang DW 2020 China Wuhan 19 88 19 44.5 (35, 58.8) 73 (64, 81) 47 84 18 53 7 26 NA NA 7 37 3 16 2 5 2 5
Wang K 2020 China Wuhan 21 470 78 58 (46–67) a 67 (61.8–78) a 48 71 27 49 14 24 5 4 NA NA NA NA 1 6 2 9
Wang L (1) 1 2020 China Wuhan 18 274 65 68 (64, 74) 76 (70, 83) 46 60 39 50 16 17 4 5 12 33 4 16 3 6 4 17
Wang L (2) 2020 China Wuhan 19 169 33 61 (49, 67) 74 (65, 84) 39 70 26 49 11 12 4 6 7 15 2 21 3 12 2 15
Wang Y * 2020 China Nanjing 19 211 133 57 (47–69) 70 (62–77) 50 56 34 52 16 23 NA NA 9 17 NA NA NA NA 1 10
Wu CM * 2020 China Wuhan 19 40 44 50 (40.3, 56.8) 68.5 (59.3, 75) 78 66 18 36 13 25 NA NA 10 9 NA NA NA NA NA NA
Xu PP 2020 China MC 21 659 33 45 (14.6) 64.7 (13.4) 53 73 14 52 7 36 1 3 3 36 NA NA 1 9 1 12
Xu B 2020 China Wuhan 21 117 28 56 (43, 66) 73 (68, 77.3) 50 61 18 36 NA NA NA NA NA NA NA NA 2 7 NA NA
Yang XB * 2020 China Wuhan 19 20 32 51.9 (12.9) 64.6 (11.2) 70 66 NA NA 10 22 5 3 10 9 0 22 NA NA NA NA
Yang KY 2020 China MCr 21 165 40 62 (57, 69) 63 (53, 75) 41 73 34 28 12 5 NA NA NA NA NA NA NA NA 3 0
Yan XS 2020 China Wuhan 19 964 40 62 (50, 70) 68 (58, 79) 48 68 22 50 10 25 1 3 NA NA 2 23 NA NA 1 0
Zhang J * 2020 China Wuhan 18 11 8 68 (38, 87) 77 (66, 91) 55 63 55 63 9 38 NA NA 0 38 9 25 NA NA NA NA
Zhou F 2020 China Wuhan 21 137 54 52 (45, 58) 69 (63, 76) 59 70 23 48 14 32 2 0 NA NA NA NA 0 4 2 7
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Abbreviation: COVID-19, coronavirus disease 2019; S: survivors; NS: non-survivors; CCD: Chronic cardiac disease; CB: Cerebrovascular disease; CRD: Chronic renal disease; COPD: Chronic obstructive pulmonary disease; MINORS: Methodological Index for Non-Randomized Studies; NA, Not available; MC: Multi-center.

a: Reported as median (range). Other studies were reported as median (IQR) or mean (SD).

*: All patients with ARDS or severe/critically ill patients.

1: All patients were over 60 years old.

Characteristics of included studies

As shown in Table 1, the included studies were carried out in China (n = 27), Spain (n = 1), Italy (n = 1), Iran (n = 1) and Poland (n = 1). In total, 9407 confirmed COVID-19 patients were included, of which 7856 were survivors and 1551 were non-survivors. The mean or median age of survivors varied from 40 to 69 years, and that of the non-survivors ranged between 63 to 75.3 years. The proportions of male patients in survivors and non-survivors were 52% and 65%, respectively. For comorbidities, similar to the findings of Ielapi N et al. [35], a history of hypertension was more common among non-survivors (52%) than among survivors (29%). Similar to hypertension, non-survivors were more likely to report having diabetes, malignancy, chronic obstructive pulmonary disease, chronic cardiac disease, cerebrovascular disease and chronic renal disease (Table 1). Clinical characteristics included fever, cough, dyspnea, fatigue, diarrhea, myalgia, expectoration, headache, emesis, pharyngalgia, anorexia, abdominal pain, dizziness, hemoptysis, nausea, chest pain, chest tightness and shiver. As for laboratory test, we focused on leukocytes, lymphocytes, procalcitonin (PCT), D-Dimer, lactate dehydrogenase (LDH) and ferritin. Of these studies, nineteen studies provided the clinical characteristics and the laboratory findings of COVID-19 patients [6–9, 11–13, 15, 17, 20, 22, 24, 26–28, 31–34], seven studies only targeted the clinical characteristics [4, 5, 14, 16, 18, 23, 29], and another five studies only focused on the laboratory findings [10, 19, 21, 25, 30].

Meta-analysis results of clinical characteristics

Twenty-six studies involving 7274 COVID-19 patients (5926 survivors and 1348 non-survivors) provided data regarding clinical characteristics (S2 Table). The association between various clinical characteristics and the risk of mortality in COVID-19 patients were shown in Fig 2. Compared with survivors, non-survivors were more likely to present with dyspnea (66% vs. 34%), chest tightness (46% vs. 30%), hemoptysis (4% vs. 3%), expectoration (42% vs. 32%) and fatigue (50% vs. 44%) (S2 Table). In addition, dyspnea, chest tightness, hemoptysis, expectoration and fatigue were observed as significant poor risk factors of mortality (dyspnea: OR = 4.52, 95%CI [3.15, 6.48], P<0.001; chest tightness: OR = 2.50, 95%CI [1.78, 3.52], P<0.001; hemoptysis: OR = 2.00, 95%CI [1.02, 3.93], P = 0.045; expectoration: OR = 1.52, 95%CI [1.17, 1.97], P = 0.002; and fatigue: OR = 1.27, 95%CI [1.09, 1.48], P = 0.003). The heterogeneity test results of dyspnea, chest tightness, hemoptysis, expectoration and fatigue evaluated by I2 were 79%, 2%, 0%, 51% and 10%, respectively. However, no significant relationships were found between mortality and fever, cough, diarrhea, headache, abdominal pain, dizziness, nausea, chest pain and so on (Fig 2).

Fig 2. Meta-analysis results of the relationship between clinical manifestation and the increasing risk of mortality in COVID-19 patients.

Fig 2

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Abbreviation: OR, odds ratio; CI, confidence interval.

Meta-analysis results of laboratory findings

A total of twenty-four studies consisting of 5900 cases (4639 survivors and 1261 non-survivors) reported laboratory findings of COVID-19 patients (S3 Table). We compared the pretreatment absolute leukocytes count, absolute leukocytes count, LDH, D-Dimer, PCT and ferritin between survivors and non-survivors. Compared with survivors, significant increases were found in non-survivors in pretreatment absolute leukocytes count (WMD = 3.27×109/L, 95% CI [2.34, 4.21], P<0.001) (Table 2, S1 Fig) and we further observed significant negative correlation between the risk of mortality and decreased pretreatment absolute leukocytes count (OR = 0.32, 95%CI [0.22, 0.46], P<0.001; I2 = 44%, P = 0.11) (Fig 3). The mean value of pretreatment absolute lymphocytes count was significantly decreased in non-survivors with a WMD of -0.39×109/L, 95% CI [-0.46, -0.33]; P<0.001) compared with survivors (Table 2, S1 Fig) and the reduction of pretreatment absolute lymphocytes count was also significantly related to the increased risk of mortality (OR = 9.83, 95%CI [6.72, 14.38], P<0.001). No pronounced heterogeneity was observed by the heterogeneity test (I2 = 0%, P = 0.515) (Fig 3). What’s more, LDH, D-Dimer, PCT and ferritin were also found to be elevated in non-survivors (Table 2, S1 Fig) and the increased indicators mentioned above were also associated with increased risk of mortality (Fig 3).

Table 2. Meta-analysis results of comparing laboratory abnormalities between survivor and non-survivor COVID-19 patients.

Laboratory findings No. of the studies No. of the patients WMD P Test of heterogeneity
I2 (%) P
Leukocytes (×109/L) 19 5408 3.27 (2.34, 4.21) <0.001 90 <0.001
Lymphocytes (×109/L) 20 4825 -0.39 (-0.46, -0.33) <0.001 83 <0.001
Lactate dehydrogenase (LDH) (U/L) 13 3336 211.60 (148.63, 274.57) <0.001 68 0.008
Procalcitonin (ng/mL) 11 3330 0.31 (0.20, 0.42) <0.001 88 <0.001
D-Dimer (μg/mL) 17 3108 4.97 (3.55, 6.39) <0.001 90 <0.001
Ferritin (ng/mL) 6 1500 770.05 (530.34, 1009.76) <0.001 86 <0.001
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Abbreviation: WMD, weighted mean difference; LDH, lactate dehydrogenase.

Fig 3. Meta-analysis results of the relationship between laboratory abnormalities and the increasing risk of mortality in COVID-19 patients.

Fig 3

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Abbreviation: OR, odds ratio; CI, confidence interval.

Publication bias

The funnel plots and Egger’s tests showed that there was no evidence of publication bias either in any clinical characteristic analysis or in any laboratory test analysis (S2 and S3 Figs).

Discussion

In this article, we summarized the incidence of some common symptoms of COVID-19 infections and found that dyspnea, chest tightness, hemoptysis, expectoration and fatigue were significantly associated with poor prognosis in COVID-19 patients. For laboratory tests, our study indicated significant increased pretreatment absolute leukocytes count and decreased pretreatment absolute lymphocytes count were observed in non-survivors and they were also associated with the increased risk of mortality in COVID-19 patients.

As an emerging infectious disease, the rapid global rise of COVID-19 pneumonia infections and deaths has attracted significant attention. To foresee the prognosis of COVID-19 infected individuals, it is essential to ascertain the risk factors for death fast and reliably. A large number of clinical studies have explored the clinical characteristics and laboratory examinations of severe and critical COVID-19 patients. Zheng et al. [36] reported that the fever, shortness of breath or dyspnea indicated the disease deterioration. Our results were consistent with the findings of Shi et al. that the presence of dyspnea was risk factors for death, rather than fever [37]. Another recent retrospective study of 179 patients with confirmed COVID-19 found that fatigue and expectoration were more frequently observed in non-survivors than survivors, which were associated with increased risk of mortality [9]. Hemoptysis was an uncommon symptom in COVID-19 patients [38]. In several studies, the incidence of hemoptysis was higher in survivors [9, 14], while many others reported that hemoptysis occurred more often in non-survivors [6, 7, 17], consistent with our observations. More researches on the role of hemoptysis in predicting the prognosis of COVID-19 patients was required.

For laboratory tests, in addition to pretreatment absolute leukocytes and lymphocytes count, increased LDH, PCT, and ferritin were also observed in non-survivors. Further analyses showed them were all associated with the mortality of patients.

Concerning lymphocyte, some studies found no significant correlation between lymphocyte counts and the severity of the disease [39, 40], whereas other research concluded that lymphopenia was a good predictor of disease progression [41, 42]. The present study is the first meta-analysis, which identified the correlation between lymphopenia and mortality in COVID-19 patients. Regardless of the baseline disease severity, lymphocyte was significantly lower on admission and maintained a lower level during hospitalization in non-survivors, while it increased after treatment in survivors [6–8, 43, 44]. The lymphopenia may result from destruction of lymphocytes (particularly T lymphocytes) and suppression of the proliferation of lymphocytes caused by virus invasion, and recovered lymphocyte could be a predictor of gradual recovery [45].

The present study had some limitations that should be acknowledged. First, all included studies were retrospective. Secondly, subgroup analyses were not performed due to the limited data we can draw from the enrolled studies. Additionally, due to the limitations of language, we included the studies written in English and Chinese only.

Conclusions

To sum up, we found that dyspnea, chest tightness, hemoptysis, expectoration and fatigue were predictors of increased risk of mortality. Besides, significantly increased pretreatment absolute leukocyte count, PCT, D-Dimer, LDH and ferritin, and decreased pretreatment absolute lymphocyte count were identified in non-survivors, which were all related to increased risk of mortality. Motoring these indicators during the hospitalization of patients plays a very important role in predicting the prognosis of patients. Collectively, our results are helpful in clinical practice, which should be verified by additional large-sample or multi-center studies.

Supporting information

S1 Fig

Forest plot of the laboratory abnormalities (A) leukocytes, (B) lymphocytes, (C) lactate dehydrogenase (LDH), (D) procalcitonin, (E) D-Dimer, (F) ferritin levels in survivors versus non-survivors.

(TIF)

Click here for additional data file. (2.8MB, tif)
S2 Fig

The publication bias of the clinical characteristics (A. dyspnea; B. chest tightness; C. hemoptysis; D. expectoration; E. fatigue; F. anorexia; G. dizziness; H. chest pain; I. fever; J. nausea; K. cough; L. emesis; M. headache; N. myalgia; O. diarrhea; P. pharyngalgia; Q. abdominal pain; R. shiver) between survivors and non-survivors.

(TIF)

Click here for additional data file. (1.2MB, tif)
S3 Fig

The publication bias of the laboratory abnormalities (A) increased leukocytes, (B) decreased leukocytes, (C) decreased lymphocytes, (D) increased lactate dehydrogenase (LDH), (E) increased procalcitonin (PCT), (F) increased D-Dimer, (G) increased ferritin between survivors and non-survivors.

(TIF)

Click here for additional data file. (830.2KB, tif)
S1 Table. The results of the quality assessment for each individual study.

(XLSX)

Click here for additional data file. (13.8KB, xlsx)
S2 Table. Clinical characteristics of survivor and non-survivor COVID-19 patients.

Abbreviation: CI, confidence interval; NA, not available.

(XLSX)

Click here for additional data file. (15.9KB, xlsx)
S3 Table. Laboratory abnormalities of survivor and non-survivor COVID-19 patients.

Abbreviation: CI, confidence interval; SD, standard deviation.

(XLSX)

Click here for additional data file. (17.4KB, xlsx)
S1 File. Prisma-2009-checklist.

(DOC)

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S2 File. Search strategy for meta-analysis of risk factors for predicting mortality of COVID-19 patients (PubMed via NLM).

(DOCX)

Click here for additional data file. (14.3KB, docx)

Abbreviations

COVID-19

coronavirus disease 2019

SARS-CoV-2

severe acute respiratory syndrome coronavirus 2

IQR

interquartile range

SARS

severe acute respiratory syndrome

CI

confidence interval

LDH

lactate dehydrogenase

PCT

procalcitonin

Data Availability

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

Funding Statement

This work was supported by National Nature Science Foundation of China [grant numbers 91859203 and 81871890] and Major Science and Technology Innovation Project of Chengdu City [grant number 2020-YF08-00080-GX].

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Decision Letter 0

Raffaele Serra

27 Oct 2020

PONE-D-20-30258

Risk factors for predicting mortality of COVID-19 patients : A systematic review and meta-analysis

PLOS ONE

Dear Dr. Li,

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.

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Academic Editor

PLOS ONE

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[This work was supported by National Nature Science Foundation of China [grant

227 numbers 91859203 and 81871890] and Major Science and Technology Innovation

228 Project of Chengdu City [grant number 2020-YF08-00080-GX]]

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.

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**********

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Reviewer #1: The authors aimed to perform a systematic meta-analysis to summarize the clinical characteristics and laboratory test before treatment among COVID-19 patients 65 and identify the possible risk factors for mortality. The article is timely and novel and it is overall well structured and written.

Nevertheless, I would improve the manuscript focusing also on cardiovascular disease that increases poor prognosis and related mortality. For this purpose read and cite the article by Ielapi N, et al. Cardiovascular disease as a biomarker for an increased risk of COVID-19 infection and related poor prognosis. Biomark Med. 2020 Jun;14(9):713-716.

**********

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PLoS One. 2020 Nov 30;15(11):e0243124. doi: 10.1371/journal.pone.0243124.r002

Author response to Decision Letter 0

6 Nov 2020

Dear Editor-in-Chief and reviewers:

Thank you for your letter and for the reviewers' comments concerning our manuscript entitled “Risk factors for predicting mortality of COVID-19 patients: A systematic review and meta-analysis”. All suggestions were very helpful for us to revise and improve our paper. We carefully studied these comments and made corrections that we hope meet with approval. The revised portions are marked with ‘Track changes’ in the manuscript.

Here are my responses to the Editor-in-Chief’ comments.

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: We have now re-formatted our paper carefully to meet PLOS ONE’s style requirements.

2. Please attach a Supplemental file of the results of the quality assessment for each individual study assessed, reporting the outcome for each individual criteria considered.

Response: The results of the quality assessment for each individual study were presented in S1 Table. We have added S1 table in the revised version of our manuscript. We change the original “eTable 1” to “S2 Table” and the original “eTable 2” to “S3 Table”. We are sorry for making some mistakes in calculating the MINORS scores. After re-calculating all scores of enrolled studies, the MINORS score of Chen T(2020) was changed from 18 to 21, the MINORS score of Goicoechea M (2020) was changed from 21 to 18, and the MINORS score of Zhou F (2020) was changed from 18 to 21.(Page 8-10, Table 1)

3. Please include the date(s) on which you accessed the databases or records to obtain the data used in your study.

Response: We conducted a systematic search in PubMed, Scopus, Web of Science and Embase to identify studies in patients with COVID-19 infection up to 4 June 2020. This was mentioned in “Materials and methods”-“Search strategy”. (Page 4, Line 64).

4. Please provide a citation for the MINORS score.

Response: The citation for the MINORS score was provided as reference [3]. (Page 5, Line 88)

Slim K, Nini E, Forestier D, Kwiatkowski F, Panis Y, Chipponi J. Methodological index for non-randomized studies (MINORS): development and validation of a new instrument. ANZ Journal of Surgery. 2003;73(9):712-6.

5.Thank you for stating the following in the Funding Section of your manuscript:

[This work was supported by National Nature Science Foundation of China [grant

227 numbers 91859203 and 81871890] and Major Science and Technology Innovation

228 Project of Chengdu City [grant number 2020-YF08-00080-GX]]

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:

[The author(s) received no specific funding for this work.]

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

Response: We have removed any funding-related text from the manuscript and add the information of funding in cover letter.

6. We suggest you thoroughly copyedit your manuscript for language usage, spelling, and grammar. If you do not know anyone who can help you do this, you may wish to consider employing a professional scientific editing service.

Response: The full manuscript has been reviewed and edited by a professional scientific English editor.

Here are my responses to the reviewers’ comments.

Reviewer #1: The authors aimed to perform a systematic meta-analysis to summarize the clinical characteristics and laboratory test before treatment among COVID-19 patients 65 and identify the possible risk factors for mortality. The article is timely and novel and it is overall well structured and written.

Nevertheless, I would improve the manuscript focusing also on cardiovascular disease that increases poor prognosis and related mortality. For this purpose read and cite the article by Ielapi N, et al. Cardiovascular disease as a biomarker for an increased risk of COVID-19 infection and related poor prognosis. Biomark Med. 2020 Jun;14(9):713-716.

Response: Thank you for reviewing our manuscript and your advices were helpful. According to your suggestion about the impact of cardiovascular disease on COVID-19 infection and prognosis. We did analysis to detect the relationship between hypertension and chronic cardiac disease and mortality of COVID-19. The results showed that hypertension (OR= 2.94, 95%CI [2.39, 3.62], P<0.001) and chronic cardiac disease (OR= 3.89, 95%CI [2.65, 5.72], P<0.001), were also associated with increased mortality of COVID-19. The detail information was provided in the following table.

　 No. of the studies No. of the patients OR, 95%CI P-value Heterogeneity

I2 P-value

Hypertension 28 8939 2.94 [2.39, 3.62] <0.001 54.90% <0.001

CCD 17 3806 3.89 [2.65, 5.72] <0.001 53.40% 0.005

We appreciate the editor/reviewers' earnest work and hope that the corrections will make the revised manuscript acceptable for publication. Once again, thank you very much for your comments and suggestions, and we look forward to hearing from you.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (20.6KB, docx)

Decision Letter 1

Raffaele Serra

17 Nov 2020

Risk factors for predicting  mortality of COVID-19 patients : A systematic review and meta-analysis

PONE-D-20-30258R1

Dear Dr. Li,

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,

Prof. Raffaele Serra, M.D., Ph.D

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

amended manuscript is acceptable.

Reviewers' comments:

Acceptance letter

Raffaele Serra

19 Nov 2020

PONE-D-20-30258R1

Risk factors for predicting mortality of COVID-19 patients: A systematic review and meta-analysis

Dear Dr. Li:

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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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof. Raffaele Serra

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 Fig

    Forest plot of the laboratory abnormalities (A) leukocytes, (B) lymphocytes, (C) lactate dehydrogenase (LDH), (D) procalcitonin, (E) D-Dimer, (F) ferritin levels in survivors versus non-survivors.

    (TIF)

    Click here for additional data file. (2.8MB, tif)
    S2 Fig

    The publication bias of the clinical characteristics (A. dyspnea; B. chest tightness; C. hemoptysis; D. expectoration; E. fatigue; F. anorexia; G. dizziness; H. chest pain; I. fever; J. nausea; K. cough; L. emesis; M. headache; N. myalgia; O. diarrhea; P. pharyngalgia; Q. abdominal pain; R. shiver) between survivors and non-survivors.

    (TIF)

    Click here for additional data file. (1.2MB, tif)
    S3 Fig

    The publication bias of the laboratory abnormalities (A) increased leukocytes, (B) decreased leukocytes, (C) decreased lymphocytes, (D) increased lactate dehydrogenase (LDH), (E) increased procalcitonin (PCT), (F) increased D-Dimer, (G) increased ferritin between survivors and non-survivors.

    (TIF)

    Click here for additional data file. (830.2KB, tif)
    S1 Table. The results of the quality assessment for each individual study.

    (XLSX)

    Click here for additional data file. (13.8KB, xlsx)
    S2 Table. Clinical characteristics of survivor and non-survivor COVID-19 patients.

    Abbreviation: CI, confidence interval; NA, not available.

    (XLSX)

    Click here for additional data file. (15.9KB, xlsx)
    S3 Table. Laboratory abnormalities of survivor and non-survivor COVID-19 patients.

    Abbreviation: CI, confidence interval; SD, standard deviation.

    (XLSX)

    Click here for additional data file. (17.4KB, xlsx)
    S1 File. Prisma-2009-checklist.

    (DOC)

    Click here for additional data file. (69KB, doc)
    S2 File. Search strategy for meta-analysis of risk factors for predicting mortality of COVID-19 patients (PubMed via NLM).

    (DOCX)

    Click here for additional data file. (14.3KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (20.6KB, docx)

    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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