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

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 . All included studies were non-randomized. The MINORS scores varied between 18 and 21, suggesting a low risk of bias overall ().

Characteristics of included studies

As shown in , 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 (). 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 (). The association between various clinical characteristics and the risk of mortality in COVID-19 patients were shown in . 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%) (). 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 ().

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

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 (). 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) (, ) 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) (). 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 (, ) 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) (). What’s more, LDH, D-Dimer, PCT and ferritin were also found to be elevated in non-survivors (, ) and the increased indicators mentioned above were also associated with increased risk of mortality ().

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

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 (.

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.

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.

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.

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)

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The results of the quality assessment for each individual study.

(XLSX)

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Clinical characteristics of survivor and non-survivor COVID-19 patients.

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

(XLSX)

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Laboratory abnormalities of survivor and non-survivor COVID-19 patients.

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

(XLSX)

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Prisma-2009-checklist.

(DOC)

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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.

27 Oct 2020

PONE-D-20-30258

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

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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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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

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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.

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

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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.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

17 Nov 2020

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

PONE-D-20-30258R1

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PLOS ONE

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19 Nov 2020

PONE-D-20-30258R1

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

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Table 1

Characteristics of all included studies.

Author	Year	Country	City	MINORS score	TotalS NS		AgeS 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

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.

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 heterogeneityI2 (%) 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

Abbreviation: WMD, weighted mean difference; LDH, lactate dehydrogenase.