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Predictors of COVID-19 severity: a systematic review and meta-analysis.

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Affiliations

  • 1. Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh, 23111, Indonesia.
      Authors
    • Mudatsir M 1
    • Fajar JK 1, 5
    • Harapan H 1
    (3 authors)
  • 2. Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, East Java, 60286, Indonesia.
      Authors
    • Wulandari L 2
    (1 author)
  • 3. Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Easy Java, 60286, Indonesia.
      Authors
    • Soegiarto G 3
    • Mahdi BA 3
    (2 authors)
  • 4. Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia.
      Authors
    • Ilmawan M 4
    • Purnamasari Y 4
    • Suhendra S 4
    • Alluza HHD 4
    • Rosida NA 4
    • Mayasari M 4
    • Mustofa M 4
    • Aditya R 4
    • Prastiwi F 4
    • Meku FX 4
    • Sitio M 4
    • Nugroho RA 4
    (12 authors)
  • 5. Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia.
      Authors
    • Fajar JK 1, 5
    • Jayanto GD 5
    • Gersom C 5
    (3 authors)
    • Show all (18)

ORCIDs linked to this article

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F1000research, 09 Sep 2020, 9:1107
https://doi.org/10.12688/f1000research.26186.2 PMID: 33163160 PMCID: PMC7607482

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Abstract 

Background: The unpredictability of the progression of coronavirus disease 2019 (COVID-19) may be attributed to the low precision of the tools used to predict the prognosis of this disease. Objective: To identify the predictors associated with poor clinical outcomes in patients with COVID-19. Methods: Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched as of April 5, 2020. The quality of the included papers was appraised using the Newcastle-Ottawa scale (NOS). Data of interest were collected and evaluated for their compatibility for the meta-analysis. Cumulative calculations to determine the correlation and effect estimates were performed using the Z test. Results: In total, 19 papers recording 1,934 mild and 1,644 severe cases of COVID-19 were included. Based on the initial evaluation, 62 potential risk factors were identified for the meta-analysis. Several comorbidities, including chronic respiratory disease, cardiovascular disease, diabetes mellitus, and hypertension were observed more frequent among patients with severe COVID-19 than with the mild ones. Compared to the mild form, severe COVID-19 was associated with symptoms such as dyspnea, anorexia, fatigue, increased respiratory rate, and high systolic blood pressure. Lower levels of lymphocytes and hemoglobin; elevated levels of leukocytes, aspartate aminotransferase, alanine aminotransferase, blood creatinine, blood urea nitrogen, high-sensitivity troponin, creatine kinase, high-sensitivity C-reactive protein, interleukin 6, D-dimer, ferritin, lactate dehydrogenase, and procalcitonin; and a high erythrocyte sedimentation rate were also associated with severe COVID-19. Conclusion: More than 30 risk factors are associated with a higher risk of severe COVID-19. These may serve as useful baseline parameters in the development of prediction tools for COVID-19 prognosis.

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Version 2. F1000Res. 2020; 9: 1107.
PMCID: PMC7607482
Other versions
PMID: 33163160

Predictors of COVID-19 severity: a systematic review and meta-analysis

Mudatsir Mudatsir, Conceptualization, Formal Analysis, Investigation, Methodology, Project Administration, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing,a,1 Jonny Karunia Fajar, Conceptualization, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing,1,2 Laksmi Wulandari, Formal Analysis, Investigation, Methodology, Project Administration, Supervision, Validation, Writing – Original Draft Preparation,b,3 Gatot Soegiarto, Conceptualization, Investigation, Methodology, Validation, Writing – Original Draft Preparation,4 Muhammad Ilmawan, Conceptualization, Formal Analysis, Investigation, Methodology, Project Administration, Software, Validation, Writing – Original Draft Preparation,5 Yeni Purnamasari, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation,5 Bagus Aulia Mahdi, Conceptualization, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Validation, Writing – Original Draft Preparation,4 Galih Dwi Jayanto, Conceptualization, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Writing – Original Draft Preparation,2 Suhendra Suhendra, Data Curation, Investigation, Methodology, Resources, Software, Visualization,5 Yennie Ayu Setianingsih, Conceptualization, Formal Analysis, Investigation, Methodology, Resources, Software, Validation,6 Romi Hamdani, Funding Acquisition, Investigation, Methodology, Resources, Software,7 Daniel Alexander Suseno, Data Curation, Investigation, Methodology, Resources, Software,8 Kartika Agustina, Data Curation, Investigation, Methodology, Resources, Software,9 Hamdan Yuwafi Naim, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software,10 Muchamad Muchlas, Data Curation, Investigation, Methodology, Project Administration, Resources, Software,11 Hamid Hunaif Dhofi Alluza, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software,5 Nikma Alfi Rosida, Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software,5 Mayasari Mayasari, Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software,5 Mustofa Mustofa, Data Curation, Formal Analysis, Investigation, Methodology, Resources,5 Adam Hartono, Data Curation, Formal Analysis, Investigation, Methodology, Software,12 Richi Aditya, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software,5 Firman Prastiwi, Data Curation, Investigation, Methodology, Resources, Software,5 Fransiskus Xaverius Meku, Data Curation, Investigation, Methodology, Project Administration, Resources, Software,5 Monika Sitio, Data Curation, Investigation, Methodology, Project Administration, Resources, Software,5 Abdullah Azmy, Data Curation, Investigation, Methodology, Resources, Software,7 Anita Surya Santoso, Data Curation, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software,13 Radhitio Adi Nugroho, Data Curation, Funding Acquisition, Investigation, Methodology, Resources, Software,5 Camoya Gersom, Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software,2 Ali A. Rabaan, Data Curation, Investigation, Methodology, Resources, Software,14 Sri Masyeni, Data Curation, Investigation, Methodology, Resources, Software,15 Firzan Nainu, Data Curation, Investigation, Methodology, Resources, Software,16 Abram L. Wagner, Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Writing – Review & Editing,17 Kuldeep Dhama, Supervision, Validation, Visualization, Writing – Review & Editing,18 and Harapan Harapan, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing1,19
Author information Article notes Copyright and License information Disclaimer

Associated Data

Data Availability Statement

Version Changes

Revised. Amendments from Version 1

In the revised version of our current article, we provided the revision of method, the limitation, and the clinical implication of our study.

Peer Review Summary

Review dateReviewer name(s)Version reviewedReview status
2021 Jan 19Morteza Arab-ZozaniVersion 2Approved
2020 Nov 2Annelies Wilder-SmithVersion 1Version 1Approved
2020 Sep 21Morteza Arab-ZozaniVersion 1Version 1Approved

Abstract

Background: The unpredictability of the progression of coronavirus disease 2019 (COVID-19) may be attributed to the low precision of the tools used to predict the prognosis of this disease.

Objective: To identify the predictors associated with poor clinical outcomes in patients with COVID-19.

Methods: Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched as of April 5, 2020. The quality of the included papers was appraised using the Newcastle-Ottawa scale (NOS). Data of interest were collected and evaluated for their compatibility for the meta-analysis. Cumulative calculations to determine the correlation and effect estimates were performed using the Z test.

Results: In total, 19 papers recording 1,934 mild and 1,644 severe cases of COVID-19 were included. Based on the initial evaluation, 62 potential risk factors were identified for the meta-analysis. Several comorbidities, including chronic respiratory disease, cardiovascular disease, diabetes mellitus, and hypertension were observed more frequent among patients with severe COVID-19 than with the mild ones. Compared to the mild form, severe COVID-19 was associated with symptoms such as dyspnea, anorexia, fatigue, increased respiratory rate, and high systolic blood pressure. Lower levels of lymphocytes and hemoglobin; elevated levels of leukocytes, aspartate aminotransferase, alanine aminotransferase, blood creatinine, blood urea nitrogen, high-sensitivity troponin, creatine kinase, high-sensitivity C-reactive protein, interleukin 6, D-dimer, ferritin, lactate dehydrogenase, and procalcitonin; and a high erythrocyte sedimentation rate were also associated with severe COVID-19.

Conclusion: More than 30 risk factors are associated with a higher risk of severe COVID-19. These may serve as useful baseline parameters in the development of prediction tools for COVID-19 prognosis.

Keywords: SARS-CoV-2, COVID-19, prognosis, severity, clinical outcome

Introduction

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global crisis across health, economic, and educational dimensions 1, 2. The disease has spread rapidly, can cause severe illness, and is characterized by a high mortality rate in certain groups. Mortality is particularly high in the absence of proven effective standard management measures 3. One of the problems with the management of this disease is the absence of standardized methods for diagnosis and the inability to estimate prognosis based on clinical features. Certain reports have shown that poor prognostic prediction has correlated with high mortality among patients with COVID-19 4, 5. Among patients with similar clinical characteristics and with similar treatment regiments, there may be a diversity in clinical outcomes 6. Therefore, the development and use of an accurate predictor for COVID-19 prognosis will be beneficial for the clinical management of patients with COVID-19, and will help reduce the mortality rate. Successful implementation of such a prediction mechanism could have a large public health impact. Better understanding of clinical progression could also improve public health messaging, particularly as many individuals may consider COVID-19 to not be severe.

Prognostic tools for the prediction of COVID-19 severity in patients have been in development since January 2020. At least nine studies proposed the use of prognostic tools for the prediction of COVID-19 severity 7– 15. However, a recent systematic review and critical appraisal study evaluated the accuracy of these tools using prediction model risk of bias assessment tool (PROBAST) and reported a high risk of bias 16. The establishment of a prediction model for the estimation of disease prognosis may help health workers segregate patients according to prediction status. However, the high risk of bias in these prediction tools might lead to inaccurate prediction of COVID-19 severity. A comprehensive study of the identification of risk factors that might play a significant role in determining the severity of patients with COVID-19 is necessary. We performed a systematic review and meta-analysis to assess the risk factors associated with poor clinical outcomes among patients with COVID-19. To the best of our knowledge, this is the first meta-analysis to assess the comprehensive risk factors that might affect the severity of COVID-19 in patients. The results of our study might serve as preliminary data for the compilation or improvement of the scoring system in the prediction of COVID-19 severity.

Methods

Study design

We performed a systematic review and meta-analysis to evaluate potential risk factors that might influence the severity of COVID-19. These risk factors include comorbidities, clinical manifestations, and laboratory findings. Accordingly, we searched the relevant studies from major scientific websites and databases to collect the data of interest, and determined the association and effect estimates by calculating the combined odds ratio (OR) and 95% confidence intervals (95% CI). The protocols for the systematic review and meta-analysis were similar to those used in previous studies 17– 23, as well as to those recommended by Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 24.

Eligibility criteria

Studies were included in this review if they met the following inclusion criteria: (1) assessed the clinical manifestations and laboratory findings of patients with mild to severe COVID-19; (2) provided adequate data for the calculation of OR and 95% CI (event per sample size or mean ± SD in both case and control groups), (3) written inEnglish. Review articles, articles with non-standard data presentation, and duplicate publications were excluded.

Search strategy and data extraction

Major scientific databases (PubMed, Embase, Cochrane, and Web of Science) were searched for articles as of April 5, 2020. Moreover, we also searched in google scholar for the additional database. A comprehensive initial search was performed to identify the potential predictors, and a final search was performed to identify the relevant papers that could be included in the meta-analysis. We used the keywords adapted from medical subject headings: ["COVID-19" or "Coronavirus disease-19" or "SARS-CoV-2"] and ["mild" or "severe" or "prognosis" or "clinical outcome"] and ["clinical manifestation" or "morbidity" or "laboratory findings"]. Only studies written in English were included. If a duplicate publication was found, the article with the larger sample size was included. We also searched for relevant studies from the reference lists in the articles. During data extraction, the following information of interest was extracted: (1) first author name; (2) publication year; (3) country of origin, (4) sample size of mild and severe cases, (5) clinical manifestations, (6) morbidities, and (7) laboratory findings. Data extraction was performed by two independent investigators (JKF and MI) using a pilot form. If the disagreement was found, we performed a discussion to resolve the disagreement.

Assessment of the methodological quality

Before inclusion in the meta-analysis, the methodological quality of the articles was assessed using the New Castle-Ottawa scale (NOS). NOS scores range from 0 to 9 and consider three items: selection of patients (4 points), comparability of the groups (2 points), and ascertainment of exposure (3 points). Each study was interpreted to be of low quality (for scores ≤ 4), moderate quality (for scores between 5–6), or high quality (for scores ≥ 7) 25. Articles with moderate to high quality were included in the analysis. The study assessment was conducted by two independent investigators (MI and YP) using a pilot form. The discrepancies between the findings of the two investigators were solved by consulting with another investigator (JKF).

Study measures

The outcome measure of the study was the severity of COVID-19 (mild vs. severe). The risk factors or predictors included three major groups: comorbidities, clinical manifestations, and laboratory parameters. Comorbid factors such as chronic kidney disease, chronic liver disease, chronic respiratory disease, cerebrovascular accident, cardiovascular disease, diabetes mellitus, hypertension, and malignancy were compatible with the analysis. For clinical manifestations, fever, cough, dry cough, expectoration, sore throat, dyspnea, diarrhea, myalgia, nasal congestion, anorexia, abdominal pain, fatigue, dizziness, headache, fever, heart rate, respiratory rate, systolic blood pressure, and diastolic blood pressure were included in this study. Among laboratory characteristics, the presence of leukocytosis, leukocytopenia, anemia, lymphocytopenia; the levels or the counts of white blood cell (WBC), hemoglobin, neutrophil, lymphocyte, monocyte, platelet, activated partial thromboplastin time (aPTT), partial thromboplastin time (PTT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin, albumin, serum creatinine, blood urea nitrogen (BUN), high-sensitivity (Hs)-troponin I, creatine kinase, high-sensitivity C-reactive protein (Hs-CRP), C-reactive protein (CRP) >8 mg/L, interleukin 6 (IL-6), glucose, D-dimer, serum ferritin, sodium, potassium, lactate dehydrogenase, and procalcitonin, CD4 and CD8; erythrocyte sedimentation rate (ESR); elevated IL-16; and elevated ESR were all included.

Statistical analysis

The significant risk factors that might govern the severity of COVID-19 were determined by the calculation of a pooled OR and 95% CI. The significance of the pooled ORs was determined using the Z test (p<0.05 was considered statistically significant). Prior to identification of the significant risk factors, data were evaluated for heterogeneity and potential publication bias. The heterogeneity among included studies was evaluated using the Q test. If heterogeneity existed (p<0.10), a random effect model was adopted; if not, a fixed effect model was adopted. Egger’s test and a funnel plot were used to assess the reporting or publication bias (p<0.05 was considered statistically significant). Furthermore, we performed a moderator analysis to identify the independent predictors of poor clinical outcomes among patients with COVID-19. The data were analyzed using Review Manager version 5.3 (Revman Cochrane, London, UK). To prevent analytical errors, statistical analysis was performed by two authors (JKF and MI). The cumulative calculation was presented in a forest plot.

Results

Eligible studies

Our searches yielded 6,209 potentially relevant studies, of which 6,170 studies were excluded after assessment of the titles and abstracts. Subsequently, further review of the complete texts was performed for 39 potential studies. In the full text review, we excluded 20 studies because they were reviews articles (n = 9), inadequacy of data for the calculation of OR and 95% CI (n = 7), and poor quality (n = 4). Eventually, 19 papers were included in our meta-analysis 26– 42 The paper selection process adopted in our study is summarized in Figure 1, and the characteristics of studies included in our analysis are outlined in Table 1.

Figure 1.

An external file that holds a picture, illustration, etc. Object name is f1000research-9-30622-g0000.jpg
A flowchart of paper selection in our study.

Table 1.

Baseline characteristics of studies included in our analysis.
Author & yearCountryCityHospitalSample sizeOutcome measureNOS
Severe Mild
Bai et al. 2020 26 ChinaWuhanJinyintan Hospital9136Died vs. cured7
Cai et al. 2020 27 ChinaShenzenThird people's Hospital58240Severe vs. non severe9
Chen et al. 2020 28 ChinaWuhanTongji hospital1110Severe vs. moderate9
Chen et al. 2020 29 ChinaMixedMulticenter50241Severe vs. mild-moderate9
Chen et al. 2020 30 ChinaWuhanZhongnan Hospital1411Viral clearance vs. without
viral clearance		9
Duan et al. 2020 31 ChinaWuhanWuhan Pulmonary Hospital4472Uncured vs. cured9
Gao et al. 2020 32 ChinaFuyangSecond People's Hospital1528Severe vs. mild7
Guan et al. 2020 33 ChinaGuangdongNational Health Commision
of China		926173Severe vs. non-severe7
Huang et al. 2020 34 ChinaWuhanJinyintan hospital1328ICU vs. non-ICU9
Jian-Ya et al. 2020 35 ChinaChongqingThree Gorges Hospital744Severe vs. non severe9
Liu et al. 2020 36 ChinaWuhanUnion Hospital6969Severe vs. non severe7
Shi et al. 2020 37 ChinaWuhanRenmin Hospital4853Died <3 d vs. >3 d9
Wang et al. 2020 38 ChinaMixedMulticenter50115CT imaging score >11 vs. <118
Wang et al. 2020 39 ChinaWuhanWuhan First People's
Hospital		22283Survivor vs. non-survivor8
Wang et al. 2020 43 ChinaWuhanZhongnan Hospital36102ICU vs. non-ICU9
Xu et al. 2020 40 ChinaMixedMulticenter2544Severe vs. mild8
Zhang et al. 2020 41 ChinaWuhanZhongnan Hospital55166Severe vs. non-severe9
Zhang et al. 2020 44 ChinaWuhanWuhan Seventh Hospital5682Severe vs. non-severe7
Zhou et al. 2020 42 ChinaWuhanWuhan Pulmonary Hospital54137Survivor vs. non-survivor8

Note: ICU, intensive care unit; CT, computed tomography; NOS, Newcastle Ottawa Scale.

Risk factors of severe COVID-19

We found that eight comorbidities, 19 clinical manifestations, and 35 laboratory parameters were available for the meta-analysis ( Table 2 and Table 3). Among the comorbid factors, chronic respiratory disease (OR: 2.48; 95% CI: 1.44, 4.27), cardiovascular disease (OR: 1.70; 95% CI: 1.05, 2.78), diabetes mellitus (OR: 2.10; 95% CI: 1.33, 3.34), and hypertension (OR: 2.33; 95% CI: 1.42, 3.81) were associated with a greater risk of severe COVID-19 ( Figure 2A–D).

Table 2.

Clinical characteristics of Covid-19 patients and the risk of severity.
Clinical characteristicsNSModelValuepEpHetpOR95%CI
Severe Mild
Comorbids
Chronic kidney disease6Random14 [3.94]15 [1.68]1.34300.02800.19102.560.63-10.45
Chronic liver disease6Fixed16 [4.82]26 [4.04]<0.00010.32200.32201.450.70-3.01
Chronic respiratory disease10Fixed31 [5.47]31 [1.66]0.70600.10200.00102.481.44-4.27
Cerebrovascular accident5Random20 [5.54]30 [2.09]0.91100.03800.18502.020.71-5.70
Cardiovascular disease13Random76 [10.45]94 [4.95]0.54000.05800.03101.701.05-2.78
Diabetes mellitus17Random156 [19.24]194 [8.40]0.7040<0.00010.00202.101.33-3.34
Hypertension15Random269 [35.54]369 [16.79]0.7680<0.00010.00102.331.42-3.81
Malignancy11Fixed29 [4.43]40 [2.23]0.61500.14300.53301.180.70-1.99
Symptoms
Fever16Random599 [79.34]1932 [80.84]0.9220<0.00010.17301.510.83-2.74
Cough12Random377 [64.33]1120 [ 54.05]0.9560<0.00010.18901.530.81-2.90
Dry cough4Fixed75 [44.38]178 [55.97]0.31300.18800.03600.660.44-0.97
Expectoration10Fixed136 [26.67]438 [29.05]<0.00010.83700.49701.090.85-1.39
Sore throat10Random59 [10.57]196 [10.96]0.78600.00400.63501.180.59-2.37
Dyspnea13Random286 [42.56]318 [16.51]0.6340<0.0001<0.00013.282.09-5.15
Diarrhea13Random65 [9.62]134 [6.68]0.51800.06900.80301.070.67-1.69
Myalgia11Fixed105 [17.89]283 [15.70]<0.00010.73300.51601.100.831-1.44
Nasal congestion4Fixed15 [5.02]53 [4.34]0.93500.10000.75901.120.55-2.29
Anorexia9Random103 [25.37]143 [15.10]0.69600.00400.04901.831.00-3.34
Abdominal pain5Fixed15 [6.07]6 [0.95]<0.00010.56500.00403.911.53-10.02
Fatigue13Random310 [46.48]694 [34.49]0.6790<0.00010.00402.001.25-3.20
Dizziness4Fixed13 [10.08]24 [5.02]0.65100.19500.01802.671.18-6.01
Headache11Fixed56 [10.45]197 [11.58]0.50700.11100.99501.000.71-1.41
Signs
Temperature >38°C5Random200 [57.97]738 [50.14]0.60900.00200.26601.440.76-2.73
Heart rate (x/min)4Fixed269 ± 35.5487.88 ± 13.30<0.00010.40700.00101.791.25-2.56
Respiratory rate (x/min)5Random22.6 ± 4.8020.36 ± 2.000.8080<0.00010.01002.851.28-6.33
SBP (mmHg)5Fixed132.57 ± 23.16123.88 ± 14.370.33400.1560<0.00011.841.31-2.60
DBP (mmHg)3Random76.50 ± 10.6175.59 ± 9.890.53500.02600.71901.140.56-2.32

Note, Value, data were presented in number [%] or mean ± SD; NS, number of studies; pE, p Egger; pHet, p heterogeneity; OR, odd ratio; CI, confidence interval; SBP, systolic blood pressure; DBP, diastolic blood pressure.

Table 3.

Laboratory findings and the risk of severity in Covid-19 patients.
Clinical
characteristics		NSModelValuepEpHetpOR95%CI
Severe Mild
Complete Blood Count
WBC (10^9/L)14Random7.32 ± 3.845.17 ± 2.041.4980<0.0001<0.00014.922.12-11.31
Leukocytosis 6Fixed62 [26.00]40[6.03]0.00000.5940<0.00015.383.36-8.62
Leukopenia6Fixed44 [18.00]206 [31.07]0.28900.24800.01600.590.41-0.87
Neutrophil count
(10^9/L)		12Random5.96 ± 3.623.84 ± 2.121.6380<0.00010.00105.452.04-14.54
Lymphocyte count
(10^9/L)		15Random0.74 ± 0.361.03 ± 0.440.6440<0.0001<0.00010.340.23-0.50
Lymphocytopenia6Random158 [59.00]40 [6.03]0.8270<0.0001<0.00013.191.14-7.07
Monocyte count
(10^9/L)		6Random0.38 ± 0.170.36 ± 0.150.58600.01000.51001.220.68-2.20
Hemoglobin (g/L)9Fixed129.11 ± 16.98132.02 ± 17.500.09000.40000.04600.760.58-1.00
Anaemia2Random18 [17.00]39 [10.32]0.76400.06600.47301.580.45-5.56
Platelet count (10^9/L)12Random172.58 ± 69.19183.21 ± 62.500.55500.00100.82000.820.55-1.23
Physiological function
AST (U/L)11Random56.20 ± 35.8328.67 ± 11.180.6930<0.0001<0.00014.912.96-8.12
ALT (U/L)12Random38.65 ± 22.9025.60 ± 14.710.8060<0.0001<0.00013.231.90-5.52
Total bilirubin (μmol/L)7Random15.80 ± 9.5013.46 ± 4.621.6600<0.00010.58001.460.41-5.21
Albumin (g/L)6Random32.39 ± 3.6435.53 ± 3.712.3900<0.00010.09500.190.03-1.34
aPTT (s)7Random31.23 ± 5.0233.13 ± 3.661.1900<0.00010.34200.580.19-1.79
PTT (s)11Random13.45 ± 1.8612.53 ± 1.310.7700<0.00010.24300.560.21-1.48
Serum creatinine
(μmol/L)		13Random82.04 ± 31.6970.25 ± 20.870.6670<0.00010.00102.141.37-3.33
BUN (mmol/L)10Random6.71 ± 2.704.74 ± 1.381.0220<0.0001<0.00016.153.05-12.37
Hs-Troponin I (pg/ml)6Random31.9 ± 61.553.55 ± 3.711.1290<0.0001<0.00019.253.51-24.37
Creatine kinase (U/L)10Random121.13 ± 115.6377.47 ± 56.260.48600.0030<0.00012.441.65-3.62
Inflammation markers
Hs-CRP (mg/L)10Random73.25 ± 49.9729.96 ± 24.401.5600<0.0001<0.000114.275.13-39.71
CRP >8 mg/L3Random147 [83.10]254 [52]1.15900.00500.00608.341.85-37.62
ESR (mm/h)4Random50.60 ± 27.2529.19 ± 26.520.42000.0710<0.00014.452.56-7.76
Elevated ESR2Fixed73 [68.00]214 [44.49]<0.00010.8060<0.00012.801.78-4.39
IL-6 (pg/ml)8Random30.45 ± 31.2911.06 ± 10.890.9120<0.0001<0.00016.683.20-13.94
Elevated IL-62Fixed44 [66]115 [46.56]<0.00010.71600.02001.981.12-3.52
CD4 count(10^9/L)3Random217.19 ± 118.56337.87 ± 149.931.59200.00100.27600.340.05-2.39
CD8 count (10^9/L)3Random178.80 ± 95.77224.17 ± 76.361.42600.00300.14200.260.04-1.57
Others
Glucose (mmol/L)3Random7.04 ± 1.836.45 ± 1.330.94800.00300.33401.800.55-5.90
D-dimer (pg/mL)15Random111.34 ± 145.1238.88 ± 28.930.6070<0.0001<0.00016.194.22 - 9.08
Serum Ferritin (μg/L)4Fixed1062.90 ± 868.19600.67 ± 758.610.43100.10700.03101.961.06-3.62
Sodium (mmol/L)3Random137.40 ± 3.1392.39 ± 1.773.2770<0.00010.284011.930.13-1109.37
Potassium (mmol/L)3Random4.12 ± 0.614.00 ± 0.540.96300.00100.74701.210.32-0.75
Lactate dehydrogenase
(U/L)		9Random381.85 ± 159.44283. 03 ± 89.400.6840<0.0001<0.00018.284.75-14.46
Procalcitonin (ng/mL)10Random0.40 ± 0.290.12 ± 0.070.9880<0.0001<0.00016.623.32-13.21

Note: Value, data were presented in number [%] or mean ± SD; NS, number of studies; pE, p Egger; pHet, p heterogeneity; OR, odd ratio; CI, confidence interval; CBC, complete blood count; WBC, white blood cells; AST, aspartate transaminase; ALT, alanine transaminase; aPTT, activated partial thromboplastin time; PTT, partial thromboplastin time; BUN, blood urea nitrogen; Hs-CRP, high sensitivity C reactive protein; ESR, erythrocyte sedimentation rate; IL, interleukin.

Figure 2.

An external file that holds a picture, illustration, etc. Object name is f1000research-9-30622-g0001.jpg
A forest plot of the association between comorbid factors and the risk of severe COVID-19.

A) Chronic respiratory disease; B) Cardiovascular diease; C) Diabetes mellitus; D) Hypertension.

Among the clinical manifestations, dyspnea (OR: 3.28; 95% CI: 2.09, 5.15), anorexia (OR: 1.83; 95% CI: 1.00, 3.34), fatigue (OR: 2.00; 95% CI: 1.25, 3.20), and dizziness (OR: 2.67; 95% CI: 1.18, 6.01) were associated with severe COVID-19 ( Figure 3A–D). In addition, increased respiratory rate (OR: 2.85; 95% CI: 1.28, 6.33) and increased systolic blood pressure (OR: 1.84; 95% CI: 1.31, 2.60) were also associated with severe COVID-19 ( Figure 4A and B). Compared to productive cough, dry cough was associated with a lower risk of severe COVID-19 (OR: 0.66; 95% CI: 0.44, 0.97).

Figure 3.

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A forest plot of the association between clinical manifestations and the risk of severe COVID-19.

A) Dyspnea; B) Anorexia; C) Fatique; D) Dizziness.

Figure 4.

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A forest plot of the association between clinical manifestation and the risk of severe COVID-19.

A) Respiratory rate; B) Systolic blood pressure.

Among laboratory characteristics, severe COVID-19 was associated with elevated WBC count (OR: 4.92; 95% CI: 2.12, 11.31), increased neutrophil count (OR: 5.45; 95% CI: 2.04, 14.54), lymphocytopenia (OR: 3.19; 95% CI: 1.14, 7.07), and decreased hemoglobin levels (OR: 0.76; 95%CI: 0.58, 1.00) ( Figure 5A–D). Elevated levels of AST, ALT, and serum creatinine increased the risk for severe manifestations of COVID-19 (ORs 4.91, 3.23, and 2.14, respectively; Figure 6A–C). Elevated levels of BUN (OR: 6.15; 95% CI: 3.05, 12.37), Hs-troponin I (OR: 9.25; 95% CI: 3.51, 24.37), creatine kinase (OR: 2.44; 95% CI: 1.65, 3.62), Hs-CRP (OR: 14.27; 95% CI: 5.13, 39.71), IL-6 (OR: 6.68; 95% CI: 3.20, 13.94), D-dimer (OR: 6.19; 95% CI: 4.22, 9.08), ferritin (OR: 1.96; 95% CI: 1.06, 3.62), lactate dehydrogenase (OR: 8.28; 95% CI: 4.75, 14.46), procalcitonin (OR: 6.62; 95% CI: 3.32, 13.21), ESR (OR: 4.45; 95% CI: 2.56, 7.76), and CRP >8 (OR: 8.34; 95% CI: 1.85, 37.62) were also associated with severe COVID-19 ( Figure 7– Figure 9). A low risk of severe COVID-19 was associated with low leukocyte levels (OR: 0.59; 95% CI: 0.41, 0.87) and elevated lymphocyte levels (OR: 0.34; 95% CI: 0.23, 0.50).

Figure 5.

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A forest of the association between complete blood count and the risk of severe COVID-19.

A) White blood cells; B) Neutrophil count; C) Lymphocytopenia; D) Hemoglobin.

Figure 6.

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A forest plot of the association between the risk of severe COVID-19 and the levels of AST ( A), ALT ( B), and serum creatinine ( C).

Figure 7.

An external file that holds a picture, illustration, etc. Object name is f1000research-9-30622-g0006.jpg

A forest plot of the association between the risk of severe COVID-19 and the levels of BUN ( A), Hs-troponin ( B), and creatine kinase ( C).

Figure 8.

An external file that holds a picture, illustration, etc. Object name is f1000research-9-30622-g0007.jpg

A forest plot of the association between the risk of severe COVID-19 and the levels of CRP ( A), Hs-CRP ( B), ESR ( C), and IL-6 ( D).

Figure 9.

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A forest plot of the association between the risk of severe COVID-19 and the levels of D-dimer ( A), serum ferritin ( B), lactate dehydrogenase ( C), and procalcitonin ( D).

Source of heterogeneity

Heterogeneity was detected in the data of chronic kidney disease, cerebrovascular disease, cardiovascular disease, diabetes mellitus, hypertension, and malignancy among the comorbid factors analyzed. Therefore, we used the random effect model to analyze the data. The fixed effect model was used to analyze the data on chronic liver disease and chronic respiratory disease, as there was no evidence of heterogeneity. For clinical manifestations, the data on fever, cough, sore throat, dyspnea, diarrhea, anorexia, fatigue, temperature >38°C, respiratory rate, and diastolic blood pressure were analyzed using the random effect model while the rest of clinical manifestation data were analyzed using the fixed effect model.

Among laboratory parameters, evidence of heterogeneity was found in count of WBC, neutrophil, monocyte, lymphocyte, platelet, CD4, and CD8; the presence of lymphocytopenia and anemia; the levels of AST, ALT, total bilirubin, albumin, aPTT, PTT, serum creatinine, BUN, Hs-Troponin I, creatine kinase, IL-6, Hs-CRP, glucose, D-dimer, sodium, potassium, lactate dehydrogenase, and procalcitonin; elevated CRP; and ESR. Accordingly, the data were analyzed using the random effect model. The data for the remaining parameters were analyzed using the fixed effect model.

Potential publication bias

We used Egger's test to assess the potential publication bias. Our cumulative calculation revealed that reporting or publication bias (p<0.05) existed with respect to chronic liver disease, expectoration, myalgia, abdominal pain, heart rate, leukocytosis, elevated ESR, and elevated IL-6 levels.

Discussion

Our data suggest that comorbidities, such as chronic respiratory disease, cardiovascular disease, diabetes, and hypertension, were associated with a higher risk of severe COVID-19, among which, hypertension was the strongest risk factor. These results are consistent with those of previous meta-analyses 43, 44 that indicated that chronic respiratory disease, cardiovascular disease, diabetes, and hypertension are significantly associated with higher COVID-19 mortality. Hypertension and diabetes are also associated with higher mortality among patients with dengue fever, West Nile virus infection, Zika virus infection, and yellow fever 45. To date, no study has reported details of the primary mechanism underlying the association between severe COVID-19 and comorbid factors. However, immune responses might be the most crucial factor underlying this association. Patients with comorbidities such as cardiovascular disease, chronic respiratory disease, hypertension, and diabetes were observed to have a lower immunity status than healthy individuals 46– 48. Since COVID-19 primarily affects the respiratory tract 49, patients with chronic respiratory diseases might be at a higher risk of contracting severe COVID-19. In addition, endothelial dysfunction might also play a pivotal role 50.

COVID-19 is a novel disease, and the immune response of this disease is not completely understood. Our data suggest that elevated leukocyte and neutrophil levels and reduced lymphocyte levels are associated with severe COVID-19. In other viral infections, such as influenza, elevated leukocyte and neutrophil levels serve as important predictors of disease severity 51. The role of leukocytes in the pathogenesis of COVID-19 is conflicting. In most cases, viral infections have been observed to cause leukopenia 52. Furthermore, a study also reported that leukopenia was observed at a significantly higher frequency among COVID-19 patients than among non-COVID-19 patients 53. However, in our present study, we did not compare COVID-19 and non-COVID-19 patients. The major factor that seemed to affect our findings was the occurrence of cytokine storm in patients. In COVID-19, there is an immune system overreaction, which results in a cytokine storm. In this condition, leukocytes might be over-activated, which might lead to the release of high levels of cytokines 54. Consistent with our data, a study has confirmed that cytokine storm is significantly associated with severe COVID-19 55. The theory underlying the role of neutrophils in COVID-19, as reported in our study, remains unclear. The speculations might be attributed to the involvement of neutrophil extracellular traps (NETs). While no study has assessed the precise role of NETs in COVID-19 pathogenesis, certain researchers speculate that SARS-CoV-2 might stimulate neutrophils to produce NETs, similar to several other viral pathogens 56. Furthermore, this might lead to neutrophil infiltration in pulmonary capillaries, organ damage, and the development of acute respiratory distress syndrome 57.

Low lymphocyte levels were observed in patients with severe COVID-19 compared with those with mild COVID-19. In the context of the immunological mechanism, our results might be contradictory. Lymphocyte subsets are known to play an important role in the action against bacterial, viral, fungal, and parasitic infections 58; therefore, the levels of circulating lymphocytes should increase. The immunological response in COVID-19 is unique and remains unclear. However, certain propositions might help describe our findings. First, coronaviruses infect human cells through ACE2 receptors 59. Since ACE2 receptors are also expressed by lymphocytes 60, the coronaviruses may enter lymphocytes and induce apoptosis. Second, the feedback mechanism between pro-inflammatory cytokines (such as IL-6) and lymphocytes might also explain our results. A study revealed that elevation in the levels of pro-inflammatory cytokines correlated with reduction in the levels of lymphocytes 61. Moreover, our findings also confirmed the significant elevation in the levels of IL-6. Third, ACE2 receptors are expressed by cells from various organs, including the thymus and spleen 62. As coronaviruses infect human cells through the ACE2 receptors, the spleen and thymus might also be damaged in patients with COVID-19, which would lead to lower levels of lymphocyte production. Fourth, lymphocyte proliferation requires a balanced metabolism, and metabolic disorders such as hyperlactic acidemia have been reported to disturb lymphocyte proliferation 63. Hyperlactic acidemia has been observed in patients with severe COVID-19 64.

The studies included in this systematic review also suggest that the levels of D-dimer were significantly higher in patients with severe COVID-19. Coagulation in patients with COVID-19 has been a major concern, and the lack of reliable data and meta-analyses prevents a holistic comparison. Certain infectious diseases that cause abnormal coagulation have been associated with poor clinical outcomes 65. The theory behind this mechanism is not understood clearly. It is widely known that ACE2 receptors are important for the infection of host cells by SARS-CoV-2, and ACE2 receptors are expressed in various cells in the human body, including endothelial cells 66. Consequently, a massive inflammatory reaction may occur in endothelial cells owing to SARS-CoV-2 infection 67, which may lead to increased coagulation, disseminated intravascular coagulation 68, and increased fibrin degradation 69. High fibrin degradation leads to elevated levels of fibrinogen and D-dimer 70, which might also explain the occurrence of venous thromboembolism in critical patients of COVID-19 71. In addition, a study with a short follow-up period also reported the existence of a dynamic correlation between the D-dimer levels and the severity of COVID-19 72. Furthermore, pulmonary embolism and deep vein thrombosis were also observed in patients with severe COVID-19 73, 74, which suggests that D-dimer might play a prominent role in governing the severity of COVID-19 patients.

We also observed that inflammatory markers, including elevated levels of CRP, ESR, and IL-6, were found both in patients with severe and mild COVID-19, with a significant increase detected in patients with severe COVID-19. Other variables associated with adverse outcomes, such as ferritin, lactate dehydrogenase, and procalcitonin levels, were found to be elevated predominantly in patients with severe COVID-19. Our findings were consistent with those of a previous meta-analysis 75, and indicated that high levels of CRP, lactate dehydrogenase, and ESR were associated with adverse outcomes in COVID-19. Another meta-analysis had also confirmed that elevated levels of IL-6 were observed in patients with COVID-19 who exhibited poor clinical outcomes 76. Therefore, the levels of CRP, ESR, IL-6, ferritin, procalcitonin, and lactate dehydrogenase can serve as potential markers for the evaluation of COVID-19 prognosis.

The high mortality rate and treatment failure in patients with COVID-19 can be attributed to the fact that COVID-19 affects multiple organs, including the lung, heart, kidney, and liver 77. Our data suggest that elevated levels of urea and creatinine, and not chronic kidney disease, were associated with severe COVID-19, which indicates that acute inflammation might be caused by SARS-CoV-2 infection. Previous meta-analyses have also reported findings consistent with our results 78, 79. Moreover, anatomical studies have reported significant renal inflammation in patients with severe COVID-19 75, 80, 81. There might be two mechanisms by which SARS-CoV-2 induces renal inflammation. First, SARS-CoV-2 might directly infect renal tubular epithelial cells and podocytes through ACE2 receptors, which facilitates the targeted infection of certain cells by the virus. Consequently, acute tubular necrosis, podocytopathy, microangiopathy, and collapsing glomerulopathy might occur owing to the massive inflammation in renal tubular epithelial cells and podocytes 82, 83. Second, the binding between SARS-CoV-2 and ACE2 receptors might activate angiotensin II and induce cytokine production, which may lead to hypercoagulopathy and microangiopathy, and eventually cause renal hypoxia 84, 85.

Conversely, with respect to liver function, we observed that the levels of liver enzymes were higher in patients with severe COVID-19. Previous studies in this context have elucidated that ACE2 receptors are highly expressed in bile duct cells; therefore, infection of these cells by coronaviruses might lead to abnormalities in the levels of liver enzymes 86. However, a recent anatomical study on liver biopsy specimens from patients with severe COVID-19 revealed that moderate microvascular steatosis and mild lobular and portal activities were observed 87. These data suggest that it cannot be determined clearly whether the elevated levels of liver enzymes in patients with severe COVID-19 are caused by direct infection or by drug-induced liver injury. Therefore, further studies are required to elucidate the precise mechanism underlying the elevation of liver enzymes levels in patients with severe COVID-19.

Meta-analyses on this topic have been performed previously 43, 44, 75, 76, 88– 91. However, compared to previous studies, our study has the following strengths. The previous studies only reported limited factors, such as clinical manifestations 43, 88, 90, 91, laboratory findings 76, 89, or a combination of only clinical manifestations and laboratory findings 75. In our study, we included all comorbidities, clinical manifestations, and laboratory characteristics. Additionally, compared to previous studies, this study has a larger sample size; the data on 1,934 patients with mild and 1,644 patients with severe COVID-19 treated across 19 hospitals were retrieved. However, this study also has certain limitations. Certain crucial factors that might play an important role in the pathogenesis of COVID-19, including secondary infection, treatment, and immunological status were not controlled for. Our current findings should be interpreted with caution because the majority of studies included were cross-sectional, and the samples corresponding to the data analyzed originated only in China. Moreover, in our study, we did not perform the sub-group analysis according to the transmission area. As already reported, the transmission of COVID-19 in China was also affected by the transmission area 92. Therefore, this limitation might also affect the final findings of our study. Longitudinal studies may reveal more long-term impacts of SARS-CoV-2 infection 93.

Conclusion

COVID-19 is an emergent infectious disease, and the major problem associated with it is the unknown pattern of disease development. We identified 34 factors that are associated with severe COVID-19. This might improve our understanding of COVID-19 progression and provide baseline data to compile or improve the prediction models for the estimation of COVID-19 prognosis. Moreover, our current findings may also contribute to guide the prioritization of high-risk target populations for vaccination.

Data availability

Underlying data

All data underlying the results are available as part of the article and no additional source data are required.

Reporting guidelines

Figshare: PRISMA checklist for ‘Predictors of COVID-19 severity: a systematic review and meta-analysis’, https://doi.org/10.6084/m9.figshare.12813683.v1 94

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Notes

[version 2; peer review: 2 approved]

Funding Statement

We thank to Lembaga Pengelola Dana Pendidikan (LPDP) Republik Indonesia for supporting this project.

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

References

1. Acikgoz O, Gunay A: The early impact of the Covid-19 pandemic on the global and Turkish economy. Turk J Med Sci. 2020;50(SI-1):520–526. 10.3906/sag-2004-6 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
2. Nicola M, Alsafi Z, Sohrabi C, et al. : The socio-economic implications of the coronavirus pandemic (COVID-19): A review. Int J Surg. 2020;78:185–193. 10.1016/j.ijsu.2020.04.018 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
3. Al-Tawfiq JA, Leonardi R, Fasoli G, et al. : Prevalence and fatality rates of COVID-19: What are the reasons for the wide variations worldwide? Travel Med Infect Dis. 2020;35:101711. 10.1016/j.tmaid.2020.101711 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
4. Shojaee S, Pourhoseingholi MA, Ashtari S, et al. : Predicting the mortality due to Covid-19 by the next month for Italy, Iran and South Korea; a simulation study. Gastroenterol Hepatol Bed Bench. 2020;13(2):177–179. [Europe PMC free article] [Abstract] [Google Scholar]
5. Wang L, Li J, Guo S, et al. : Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm. Sci Total Environ. 2020;727:138394. 10.1016/j.scitotenv.2020.138394 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
6. Siordia JA: Epidemiology and clinical features of COVID-19: A review of current literature. J Clin Virol. 2020;127:104357. 10.1016/j.jcv.2020.104357 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
7. Bai X, Fang C, Zhou Y, et al. : Predicting COVID-19 malignant progression with AI techniques. 2020. 10.1101/2020.03.20.20037325 [CrossRef] [Google Scholar]
8. Xie J, Hungerford D, Chen H, et al. : Development and external validation of a prognostic multivariable model on admission for hospitalized patients with COVID-19. Korean J Radiol. 2020;21(8):1007–1017. 10.3348/kjr.2020.0485 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
9. Caramelo F, Ferreira N, Oliveiros B: Estimation of risk factors for COVID-19 mortality-preliminary results. MedRxiv. 2020. 10.1101/2020.02.24.20027268v1 [CrossRef] [Google Scholar]
10. Qi X, Jiang Z, Yu Q, et al. : Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study. Ann Transl Med. 2020;8(14):859. 10.21037/atm-20-3026 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
11. Yan L, Zhang HT, Xiao Y, et al. : Prediction of criticality in patients with severe Covid-19 infection using three clinical features:a machine learning-based prognostic model with clinical data in Wuhan. MedRxiv. 2020. Reference Source [Google Scholar]
12. Yuan M, Yin W, Tao Z, et al. : Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China. PLoS One. 2020;15(3):e0230548. 10.1371/journal.pone.0230548 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
13. Lu J, Hu S, Fan R, et al. : ACP risk grade: a simple mortality index for patients with confirmed or suspected severe acute respiratory syndrome coronavirus 2 disease (COVID-19) during the early stage of outbreak in Wuhan, China. MedRxiv. 2020. 10.1101/2020.02.20.20025510 [CrossRef] [Google Scholar]
14. Gong J, Ou J, Qiu X, et al. : A tool to early predict severe 2019-novel coronavirus pneumonia (COVID-19): a multicenter study using the risk nomogram in Wuhan and Guangdong, China. Clin Infect Dis. 2020;71(15):833–840. 10.1093/cid/ciaa443 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
15. Shi Y, Yu X, Zhao H, et al. : Host susceptibility to severe COVID-19 and establishment of a host risk score: findings of 487 cases outside Wuhan. Crit Care. 2020;24(1):108. 10.1186/s13054-020-2833-7 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
16. Wynants L, Van Calster B, Collins GS, et al. : Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ. 2020;369:m1328. 10.1136/bmj.m1328 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
17. Fajar J, Mahdi B, Heriansyah T, et al. : Length of stay and major adverse cardiac events: Comparison between percutaneous coronary intervention and thrombolytic therapy in patients with ST-elevation myocardial infarction Ιmplications for cost effectiveness. Archives of Hellenic Medicine/Arheia Ellenikes Iatrikes. 2019;36(4). Reference Source [Google Scholar]
18. Fajar JK, Andalas M, Harapan H: Comparison of Apgar scores in breech presentations between vaginal and cesarean delivery. Tzu-Chi Medical Journal. 2017;29(1):24–29. 10.4103/tcmj.tcmj_5_17 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
19. Fajar JK, Harapan H: Socioeconomic and attitudinal variables associated with acceptance and willingness to pay towards dengue vaccine :a systematic review. Arch Clin Infect Dis. 2017;12(3):e13914 10.5812/archcid.13914 [CrossRef] [Google Scholar]
20. Fajar JK, Mahendra AI, Tamara F, et al. : The association between complete blood count and the risk of coronary heart disease. Turkiye Klinikleri J Med Sci. 2019;39(1):56–64. 10.5336/medsci.2018-61970 [CrossRef] [Google Scholar]
21. Fajar JK, Taufan T, Syarif M, et al. : Hip geometry and femoral neck fractures: A meta-analysis. J Orthop Translat. 2018;13:1–6. 10.1016/j.jot.2017.12.002 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
22. Prihatiningsih S, Fajar JK, Tamara F, et al. : Risk factors of tuberculosis infection among health care workers: a meta-analysis. Indian J Tuberc. 2020;67(1):121–129. 10.1016/j.ijtb.2019.10.003 [Abstract] [CrossRef] [Google Scholar]
23. Rohman MS, Purnamasari Y, Ilmawan M, et al. : Comparison of major bleeding in patients with acute coronary syndrome that underwent coronary artery bypass grafting treated with clopidogrel or ticagrelor: a systematic review and meta-analysis [version 1; peer review: 1 approved, 1 approved with reservations]. F1000Res. 2020;9(99):99 10.12688/f1000research.21925.1 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
24. McInnes MDF, Moher D, Thombs BD, et al. : Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies:The PRISMA-DTA Statement. JAMA. 2018;319(4):388–396. 10.1001/jama.2017.19163 [Abstract] [CrossRef] [Google Scholar]
25. Stang A: Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25(9):603–5. 10.1007/s10654-010-9491-z [Abstract] [CrossRef] [Google Scholar]
26. Bai T, Tu S, Wei Y, et al. : Clinical and laboratory factors predicting the prognosis of patients with COVID-19: an analysis of 127 patients in Wuhan, China. China (2/26/2020). 2020. 10.2139/ssrn.3546118 [CrossRef] [Google Scholar]
27. Cai Q, Huang D, Ou P, et al. : COVID-19 in a designated infectious diseases hospital outside Hubei Province, China. Allergy. 2020;75(7):1742–1752. 10.1111/all.14309 [Abstract] [CrossRef] [Google Scholar]
28. Chen G, Wu D, Guo W, et al. : Clinical and immunological features of severe and moderate coronavirus disease 2019. J Clin Invest. 2020;130(5):2620–2629. 10.1172/JCI137244 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
29. Chen X, Zheng F, Qing Y, et al. : Epidemiological and clinical features of 291 cases with coronavirus disease 2019 in areas adjacent to Hubei, China: a double-center observational study. MedRxiv. 2020. 10.1101/2020.03.03.20030353 [CrossRef] [Google Scholar]
30. Chen X, Ling J, Mo P, et al. : Restoration of leukomonocyte counts is associated with viral clearance in COVID-19 hospitalized patients. MedRxiv. 2020. 10.1101/2020.03.03.20030437 [CrossRef] [Google Scholar]
31. Duan Q, Guo G, Ren Y, et al. : Treatment Outcomes, Influence Factors of 116 Hospitalized COVID-19 Patients with Longer/Prolonged Treatment Course in Wuhan, China. Influence Factors. 2020;116. [Google Scholar]
32. Gao Y, Li T, Han M, et al. : Diagnostic utility of clinical laboratory data determinations for patients with the severe COVID-19. J Med Virol. 2020;92(7):791–796. 10.1002/jmv.25770 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
33. Guan WJ, Ni ZY, Hu Y, et al. : Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020;382(18):1708–1720. 10.1056/NEJMoa2002032 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
34. Huang C, Wang Y, Li X, et al. : Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497–506. 10.1016/S0140-6736(20)30183-5 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
35. Jian-ya G: Clinical characteristics of 51 patients discharged from hospital with COVID-19 in Chongqing, China. medRxiv. 2020. 10.1101/2020.02.20.20025536 [CrossRef] [Google Scholar]
36. Liu T, Zhang J, Yang Y, et al. : The potential role of IL-6 in monitoring coronavirus disease 2019. 2020. 10.1101/2020.03.01.20029769 [CrossRef] [Google Scholar]
37. Shi Q, Zhao K, Yu J, et al. : Clinical characteristics of 101 non-surviving hospitalized patients with COVID-19: A single center, retrospective study. medRxiv. 2020. Reference Source [Google Scholar]
38. Wang D, Hu B, Hu C, et al. : Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA. 2020;323(11):1061–1069. 10.1001/jama.2020.1585 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
39. Wang H, Luo S, Shen Y, et al. : Multiple enzyme release, inflammation storm and hypercoagulability are prominent indicators for disease progression in COVID-19: a multi-centered, correlation study with CT imaging score. 2020. 10.2139/ssrn.3544837 [CrossRef] [Google Scholar]
40. Xu Y, Li Y-r, Zeng Q, et al. : Clinical characteristics of SARS-CoV-2 pneumonia compared to controls in Chinese Han population. medRxiv. 2020. 10.1101/2020.03.08.20031658 [CrossRef] [Google Scholar]
41. Zhang JJ, Dong X, Cao YY, et al. : Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy. 2020;75(7):1730–1741. 10.1111/all.14238 [Abstract] [CrossRef] [Google Scholar]
42. Zhou F, Yu T, Du R, et al. : Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054–1062. 10.1016/S0140-6736(20)30566-3 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
43. Li J, He X, Zhang W, et al. : Meta-analysis investigating the relationship between clinical features, outcomes, and severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia. Am J Infect Control. 2020;S0196-6553(20)30369-2. 10.1016/j.ajic.2020.06.008 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
44. Yang J, Zheng Y, Gou X, et al. : Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. Int J Infect Dis. 2020;94:91–95. 10.1016/j.ijid.2020.03.017 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
45. Badawi A, Velummailum R, Ryoo SG, et al. : Prevalence of chronic comorbidities in dengue fever and West Nile virus: A systematic review and meta-analysis. PLoS One. 2018;13(7):e0200200. 10.1371/journal.pone.0200200 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
46. Vasdev S, Stuckless J, Richardson V: Role of the immune system in hypertension: modulation by dietary antioxidants. Int J Angiol. 2011;20(4):189–212. 10.1055/s-0031-1288941 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
47. Ferlita S, Yegiazaryan A, Noori N, et al. : Type 2 Diabetes Mellitus and Altered Immune System Leading to Susceptibility to Pathogens, Especially Mycobacterium tuberculosis. J Clin Med. 2019;8(12):2219. 10.3390/jcm8122219 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
48. Lazzerini PE, Hamilton RM, Boutjdir M: Editorial: Cardioimmunology: Inflammation and Immunity in Cardiovascular Disease. Front Cardiovasc Med. 2019;6:181. 10.3389/fcvm.2019.00181 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
49. Perlman S, Dandekar AA: Immunopathogenesis of coronavirus infections: implications for SARS. Nat Rev Immunol. 2005;5(12):917–27. 10.1038/nri1732 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
50. Green SJ: Covid-19 accelerates endothelial dysfunction and nitric oxide deficiency. Microbes Infect. 2020;22(4–5):149–150. 10.1016/j.micinf.2020.05.006 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
51. Russell CD, Parajuli A, Gale HJ, et al. : The utility of peripheral blood leucocyte ratios as biomarkers in infectious diseases: A systematic review and meta-analysis. J Infect. 2019;78(5):339–348. 10.1016/j.jinf.2019.02.006 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
52. Pascutti MF, Erkelens MN, Nolte MA: Impact of Viral Infections on Hematopoiesis: From Beneficial to Detrimental Effects on Bone Marrow Output. Front Immunol. 2016;7:364. 10.3389/fimmu.2016.00364 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
53. Li Y, Wang H, Wang F, et al. : Comparison of Hospitalized Patients with pneumonia caused by COVID-19 and influenza A in children under 5 years. Int J Infect Dis. 2020;98:80–83. 10.1016/j.ijid.2020.06.026 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
54. Coperchini F, Chiovato L, Croce L, et al. : The cytokine storm in COVID-19: An overview of the involvement of the chemokine/chemokine-receptor system. Cytokine Growth Factor Rev. 2020;53:25–32. 10.1016/j.cytogfr.2020.05.003 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
55. Quartuccio L, Sonaglia A, McGonagle D, et al. : Profiling COVID-19 pneumonia progressing into the cytokine storm syndrome: results from a single Italian Centre study on tocilizumab versus standard of care. J Clin Virol. 2020;129:104444. 10.1016/j.jcv.2020.104444 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
56. Papayannopoulos V: Neutrophil extracellular traps in immunity and disease. Nat Rev Immunol. 2018;18(2):134–147. 10.1038/nri.2017.105 [Abstract] [CrossRef] [Google Scholar]
57. Mikacenic C, Moore R, Dmyterko V, et al. : Neutrophil extracellular traps (NETs) are increased in the alveolar spaces of patients with ventilator-associated pneumonia. Crit Care. 2018;22(1):358. 10.1186/s13054-018-2290-8 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
58. Koyasu S, Moro K: Role of innate lymphocytes in infection and inflammation. Front Immunol. 2012;3:101. 10.3389/fimmu.2012.00101 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
59. Liu M, Wang T, Zhou Y, et al. : Potential Role of ACE2 in Coronavirus Disease 2019 (COVID-19) Prevention and Management. J Transl Int Med. 2020;8(1):9–19. 10.2478/jtim-2020-0003 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
60. To KF, Lo AW: Exploring the pathogenesis of severe acute respiratory syndrome (SARS): the tissue distribution of the coronavirus (SARS-CoV) and its putative receptor, angiotensin-converting enzyme 2 (ACE2). J Pathol. 2004;203(3):740–3. 10.1002/path.1597 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
61. Shachar I, Karin N: The dual roles of inflammatory cytokines and chemokines in the regulation of autoimmune diseases and their clinical implications. J Leukoc Biol. 2013;93(1):51–61. 10.1189/jlb.0612293 [Abstract] [CrossRef] [Google Scholar]
62. Hamming I, Timens W, Bulthuis M, et al. : Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis. J Pathol. 2004;203(2):631–7. 10.1002/path.1570 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
63. Fischer K, Hoffmann P, Voelkl S, et al. : Inhibitory effect of tumor cell-derived lactic acid on human T cells. Blood. 2007;109(9):3812–9. 10.1182/blood-2006-07-035972 [Abstract] [CrossRef] [Google Scholar]
64. Chhetri S, Khamis F, Pandak N, et al. : A fatal case of COVID-19 due to metabolic acidosis following dysregulate inflammatory response (cytokine storm). IDCases. 2020;21:e00829. 10.1016/j.idcr.2020.e00829 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
65. Levi M, Keller TT, van Gorp E, et al. : Infection and inflammation and the coagulation system. Cardiovasc Res. 2003;60(1):26–39. 10.1016/s0008-6363(02)00857-x [Abstract] [CrossRef] [Google Scholar]
66. Lovren F, Pan Y, Quan A, et al. : Angiotensin converting enzyme-2 confers endothelial protection and attenuates atherosclerosis. Am J Physiol Heart Circ Physiol. 2008;295(4):H1377–84. 10.1152/ajpheart.00331.2008 [Abstract] [CrossRef] [Google Scholar]
67. Varga Z, Flammer AJ, Steiger P, et al. : Endothelial cell infection and endotheliitis in COVID-19. Lancet. 2020;395(10234):1417–1418. 10.1016/S0140-6736(20)30937-5 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
68. Marietta M, Ageno W, Artoni A, et al. : COVID-19 and haemostasis: a position paper from Italian Society on Thrombosis and Haemostasis (SISET). Blood Transfus. 2020;18(3):167–169. 10.2450/2020.0083-20 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
69. Becker RC: COVID-19 update: Covid-19-associated coagulopathy. J Thromb Thrombolysis. 2020;50(1):54–67. 10.1007/s11239-020-02134-3 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
70. Matsuo T, Kobayashi H, Kario K, et al. : Fibrin D-dimer in thrombogenic disorders. Semin Thromb Hemost. 2000;26(1):101–7. 10.1055/s-2000-9811 [Abstract] [CrossRef] [Google Scholar]
71. Khan IH, Savarimuthu S, Leung MST, et al. : The need to manage the risk of thromboembolism in COVID-19 patients. J Vasc Surg. 2020;72(3):799–804. 10.1016/j.jvs.2020.05.015 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
72. Garcia-Olive I, Sintes H, Radua J, et al. : D-dimer in patients infected with COVID-19 and suspected pulmonary embolism. Respir Med. 2020;169:106023. 10.1016/j.rmed.2020.106023 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
73. Ullah W, Saeed R, Sarwar U, et al. : COVID-19 complicated by Acute Pulmonary Embolism and Right-Sided Heart Failure. JACC Case Rep. 2020;2(9):1379–1382. 10.1016/j.jaccas.2020.04.008 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
74. Nahum J, Morichau-Beauchant T, Daviaud F, et al. : Venous Thrombosis Among Critically Ill Patients With Coronavirus Disease 2019 (COVID-19). JAMA Netw Open. 2020;3(5):e2010478. 10.1001/jamanetworkopen.2020.10478 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
75. Rodriguez-Morales AJ, Cardona-Ospina JA, Gutierrez-Ocampo E, et al. : Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis. Travel Med Infect Dis. 2020;34:101623. 10.1016/j.tmaid.2020.101623 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
76. Aziz M, Fatima R, Assaly R: Elevated interleukin-6 and severe COVID-19: A meta-analysis. J Med Virol. 2020. 10.1002/jmv.25948 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
77. Zaim S, Chong JH, Sankaranarayanan V, et al. : COVID-19 and Multiorgan Response. Curr Probl Cardiol. 2020;45(8):100618. 10.1016/j.cpcardiol.2020.100618 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
78. Yang X, Jin Y, Li R, et al. : Prevalence and impact of acute renal impairment on COVID-19: a systematic review and meta-analysis. Crit Care. 2020;24(1):356. 10.1186/s13054-020-03065-4 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
79. Chen YT, Shao SC, Hsu CK, et al. : Incidence of acute kidney injury in COVID-19 infection: a systematic review and meta-analysis. Crit Care. 2020;24(1):346. 10.1186/s13054-020-03009-y [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
80. Rossi GM, Delsante M, Pilato FP, et al. : Kidney biopsy findings in a critically ill COVID-19 patient with dialysis-dependent acute kidney injury: a case against "SARS-CoV-2 nephropathy". Kidney Int Rep. 2020;5(7):1100–1105. 10.1016/j.ekir.2020.05.005 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
81. Harapan H, Itoh N, Yufika A, et al. : Coronavirus disease 2019 (COVID-19): A literature review. J Infect Public Health. 2020;13(5):667–673. 10.1016/j.jiph.2020.03.019 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
82. Batlle D, Soler MJ, Sparks MA, et al. : Acute kidney injury in COVID-19: emerging evidence of a distinct pathophysiology. J Am Soc Nephrol. 2020;31(7):1380–1383. 10.1681/ASN.2020040419 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
83. Nasr SH, Kopp JB: COVID-19-Associated Collapsing Glomerulopathy: An Emerging Entity. Kidney Int Rep. 2020;5(6):759–761. 10.1016/j.ekir.2020.04.030 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
84. Henry BM, Vikse J, Benoit S, et al. : Hyperinflammation and derangement of renin-angiotensin-aldosterone system in COVID-19: A novel hypothesis for clinically suspected hypercoagulopathy and microvascular immunothrombosis. Clin Chim Acta. 2020;507:167–173. 10.1016/j.cca.2020.04.027 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
85. Kai H, Kai M: Interactions of coronaviruses with ACE2, angiotensin II, and RAS inhibitors-lessons from available evidence and insights into COVID-19. Hypertens Res. 2020;43(7):648–654. 10.1038/s41440-020-0455-8 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
86. Chai X, Hu L, Zhang Y, et al. : Specific ACE2 expression in cholangiocytes may cause liver damage after 2019-nCoV infection. bioRxiv. 2020. 10.1101/2020.02.03.931766 [CrossRef] [Google Scholar]
87. Xu Z, Shi L, Wang Y, et al. : Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med. 2020;8(4):420–422. 10.1016/S2213-2600(20)30076-X [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
88. Park JH, Jang W, Kim SW, et al. : The Clinical Manifestations and Chest Computed Tomography Findings of Coronavirus Disease 2019 (COVID-19) Patients in China: A Proportion Meta-Analysis. Clin Exp Otorhinolaryngol. 2020;13(2):95–105. 10.21053/ceo.2020.00570 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
89. Zhang ZL, Hou YL, Li DT, et al. : Laboratory findings of COVID-19: a systematic review and meta-analysis. Scand J Clin Lab Invest. 2020;1–7. 10.1080/00365513.2020.1768587 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
90. Jain V, Yuan JM: Predictive symptoms and comorbidities for severe COVID-19 and intensive care unit admission: a systematic review and meta-analysis. Int J Public Health. 2020;1–14. 10.1007/s00038-020-01390-7 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
91. Li LQ, Huang T, Wang YQ, et al. : COVID-19 patients' clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol. 2020;92(6):577–583. 10.1002/jmv.25757 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
92. Chen D, Hu C, Su F, et al. : Exposure to SARS-CoV-2 in a high transmission setting increases the risk of severe COVID-19 compared with exposure to a low transmission setting? J Travel Med. 2020;27(5):taaa094. 10.1093/jtm/taaa094 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
93. Tenforde MW, Kim SS, Lindsell CJ, et al. : Symptom Duration and Risk Factors for Delayed Return to Usual Health Among Outpatients with COVID-19 in a Multistate Health Care Systems Network - United States, March-June 2020. MMWR Morb Mortal Wkly Rep. 2020;69(30):993–998. 10.15585/mmwr.mm6930e1 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
94. Mudatsir M, Fajar J: PRISMA CHECKLIST FOR "Predictors of COVID-19 severity: a systematic review and meta-analysis". figshare.Media.2020. 10.6084/m9.figshare.12813683.v1 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

Approved

Morteza Arab-Zozani, Referee1
1Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand, Iran
Competing interests: No competing interests were disclosed.
Review date: 2021 Jan 19. Status: Approved. 10.5256/f1000research.30622.r76646
Copyright : © 2021 Arab-Zozani M
This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The authors responded to my comments clearly and the manuscript is accepted for publication in your journal.

Are the rationale for, and objectives of, the Systematic Review clearly stated?

Yes

Is the statistical analysis and its interpretation appropriate?

Yes

Are sufficient details of the methods and analysis provided to allow replication by others?

Partly

Are the conclusions drawn adequately supported by the results presented in the review?

Yes

Reviewer Expertise:

NA

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Approved

Annelies Wilder-Smith, Referee1
1Heidelberg Institute of Global Health, University of Heidelberg, Heidelberg, Germany
Competing interests: No competing interests were disclosed.
Review date: 2020 Nov 2. Status: Approved. 10.5256/f1000research.28897.r72568
Copyright : © 2020 Wilder-Smith A
This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The strength of this paper is the meta-analysis in terms of effect estimates. The weakness is the focus of data from China, while we should learn more from global data including the comparison between HIC and LMIC.

In China, severity was also found to correlate with the force of infection, eg those in high transmission areas had more severe disease outcomes than those from lower transmission areas in China, see: Exposure to SARS-CoV-2 in a hightransmission setting increases the risk of severe COVID-19 compared with exposure to a low transmission setting?

Chen D, Hu C, Su F, Song Q, Wang Z. J Travel Med. 2020 Aug 20;27(5):taaa094. 10.1093/jtm/taaa094. 1

The authors highlight the need for a scoring system for the prediction of severity. There is another reason why it is important to identify risk factors for severe disease: to guide prioritization of high risk target populations for vaccination

Are the rationale for, and objectives of, the Systematic Review clearly stated?

Yes

Is the statistical analysis and its interpretation appropriate?

Yes

Are sufficient details of the methods and analysis provided to allow replication by others?

Yes

Are the conclusions drawn adequately supported by the results presented in the review?

Yes

Reviewer Expertise:

COVID-19, Zika and dengue

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

References

1. : Exposure to SARS-CoV-2 in a high transmission setting increases the risk of severe COVID-19 compared with exposure to a low transmission setting?. Journal of Travel Medicine.2020;27(5) : 10.1093/jtm/taaa094 10.1093/jtm/taaa094 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

Approved

Morteza Arab-Zozani, Referee1
1Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand, Iran
Competing interests: No competing interests were disclosed.
Review date: 2020 Sep 21. Status: Approved. 10.5256/f1000research.28897.r71054
Copyright : © 2020 Arab-Zozani M
This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

In this meta-analysis, you investigated the predictors of COVID-19 severity through the literature. You considered a topic of interest and provided a well-written manuscript. However, there are some things that will improve your reporting.

  • Abstract, method section, please insert detail about critical/quality appraisal of the included studies.

  • Abstract, method section, line 1, please remove " and extracted" from the text. It maybe causes a misunderstanding between this step and the data extraction step. 

  • Method section, please remove line five. "the protocols for the ...". Mentioning the PRISMA is enough.

  • Method section, eligibility criteria, (2) please mention the type of data for adequate data. what is adequate data?

  • Method section, search strategy, why is Scopus not searched? You may have missed some articles that are only indexed in Scopus.

  • Method section, search strategy, this sentence not related to this section. If you limit the search to EN publication then you need to change the verb. If not this sentence related to inclusion criteria. 

  • Method section, search strategy, based on PRISMA, add at least one search strategy for one database as a supplement.

  • Method section, data extraction, please added the country of origin for each study. The predictors may be different from one setting to another setting. 

  • Method section, data extraction, please add details about how resolved disagreement between reviewers.

  •  Method section, how did you handle the publication bias? 

  • Result section, there is some problem in figure 1. Please fill it considering other related studies. The number for  "record screened" is incorrect.

  • Result section, table 1, all studies are from China. If all studies are from China it is better to change the title. these are a predictor of severity in China. In my opinion, this is a limitation of your study.

Cheers

Are the rationale for, and objectives of, the Systematic Review clearly stated?

Yes

Is the statistical analysis and its interpretation appropriate?

Yes

Are sufficient details of the methods and analysis provided to allow replication by others?

Partly

Are the conclusions drawn adequately supported by the results presented in the review?

Yes

Reviewer Expertise:

Systematic review and meta-analysis in health and medical intervention

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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