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. 2020 Jul 6;92(11):2473–2488. doi: 10.1002/jmv.26166

Association of cardiac biomarkers and comorbidities with increased mortality, severity, and cardiac injury in COVID‐19 patients: A meta‐regression and decision tree analysis

Eman A Toraih 1,2, Rami M Elshazli 3, Mohammad H Hussein 1, Abdelaziz Elgaml 4,5, Mohamed Amin 6, Mohammed El‐Mowafy 4, Mohamed El‐Mesery 6, Assem Ellythy 1, Juan Duchesne 7, Mary T Killackey 1, Keith C Ferdinand 8, Emad Kandil 9, Manal S Fawzy 10,11,✉
PMCID: PMC7307124  PMID: 32530509

Abstract

Background

Coronavirus disease‐2019 (COVID‐19) has a deleterious effect on several systems, including the cardiovascular system. We aim to systematically explore the association of COVID‐19 severity and mortality rate with the history of cardiovascular diseases and/or other comorbidities and cardiac injury laboratory markers.

Methods

The standardized mean difference (SMD) or odds ratio (OR) and 95% confidence intervals (CIs) were applied to estimate pooled results from the 56 studies. The prognostic performance of cardiac markers for predicting adverse outcomes and to select the best cutoff threshold was estimated by receiver operating characteristic curve analysis. Decision tree analysis by combining cardiac markers with demographic and clinical features was applied to predict mortality and severity in patients with COVID‐19.

Results

A meta‐analysis of 17 794 patients showed patients with high cardiac troponin I (OR = 5.22, 95% CI = 3.73‐7.31, P < .001) and aspartate aminotransferase (AST) levels (OR = 3.64, 95% CI = 2.84‐4.66, P < .001) were more likely to develop adverse outcomes. High troponin I more than 13.75 ng/L combined with either advanced age more than 60 years or elevated AST level more than 27.72 U/L was the best model to predict poor outcomes.

Conclusions

COVID‐19 severity and mortality are complicated by myocardial injury. Assessment of cardiac injury biomarkers may improve the identification of those patients at the highest risk and potentially lead to improved therapeutic approaches.

Keywords: cardiac injury, cardiac markers, COVID‐19, meta‐analysis, outcome, SARS‐CoV‐2

Highlights

  • COVID‐19 severity and mortality are complicated by myocardial injury.

  • Patients with high cardiac troponin I and AST levels were more likely to develop adverse outcomes.

  • High troponin I combined with either advanced age or elevated AST level was the best model to predict poor outcomes.

  • Assessment of cardiac injury biomarkers may improve identification of COVID‐19 patients at the highest risk.

Abbreviations

AKI

acute kidney injury

ARDS

acute respiratory distress syndrome

AST

aspartate aminotransferase

AUC

area under the curve

CI

confidence intervals

CK

creatine kinase

CKD

chronic kidney disease

COVID‐19

coronavirus disease‐2019

cTnI

cardiac troponin I

ICU

intensive care unit

LDH

lactate dehydrogenase

MERS

Middle East respiratory syndrome

NT‐proBNP

N‐terminal‐pro hormone B‐type natriuretic peptide

OR

odds ratio

PRISMA

Preferred Reporting Items for Systematic Reviews and Meta‐Analyses

ROC

receiver operating characteristic

RT‐PCR

reverse transcription‐polymerase chain reaction

SARS‐CoV‐2

severe acute respiratory syndrome coronavirus 2

SMD

standardized mean difference

1. INTRODUCTION

The first incidence of coronavirus disease‐2019 (COVID‐19) was in December 2019 in Wuhan city, China which was attributed to viral infection with a newly originating Zoonotic virus. This virus is known as the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). 1 , 2 Indeed, infection with coronavirus was detected before in China in 2002 to 2003 and was also later detected in Saudi Arabia and was given the name of Middle East respiratory syndrome (MERS‐CoV). 3 , 4 Although SARS‐CoV‐2 infection is considered the most serious infection worldwide, most of the infected individuals suffer from mild or moderate symptoms that begin in the first week after infection. The most common mild symptoms include fever, fatigue, and cough. However, infected patients may suffer from serious complications that vary in their degrees between different individuals such as dyspnea, severe pneumonia, and organ dysfunction. 1 Based on the previous facts, the diagnosis of COVID‐19 cannot be based on specific symptom detection and the only specific detection test depends on identification of the viral genome utilizing reverse transcription‐polymerase chain reaction (RT‐PCR) method. 1

Although China is the country of origin for COVID‐19, it has been spread everywhere all over the world. That is why several prospective and retrospective studies have been directed to characterize COVID‐19 and its complications among infected patients. Cardiovascular diseases are classified as one of the main reasons for mortality and morbidity among patients with COVID‐19. 5 , 6 , 7 Moreover, the presence of cardiovascular diseases is linked to poor prognosis among infected patients. 8 , 9 Moreover, it was also detected that SARS‐CoV‐2 infection is associated with aggravation in inflammation that can trigger cardiac arrhythmia, myocarditis, and inflammation in the vascular system that can induce heart destruction. 8

Based on the fact that COVID‐19 is a recently detected disease, there is no wonder that no sufficient clinical data that characterize the correlation between the severity and complication of COVID‐19 and cardiovascular or cerebrovascular diseases. Moreover, data available provide wide variations in results and do not determine the risk factors for COVID‐19. Thus, the current meta‐analysis aimed to gather a broad range of current studies to characterize the association between the history of cardiovascular diseases and their specific biological markers levels, and the severity of COVID‐19 and its rate of mortality.

2. METHODS

2.1. Search strategy

This systematic review and meta‐analysis were reported following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. We selected relevant studies published up to 8 May 2020, by searching Web of Science, PubMed, Scopus, and Science Direct search engines. We applied no language restrictions. Searches initially used the following strings: “Novel coronavirus 2019,” “2019 nCoV,” “COVID‐19,” “Wuhan coronavirus,” “Wuhan pneumonia,” or “SARS‐CoV‐2.” The results of these searches were combined with sets created with “Cardiac biomarkers,” “chronic heart disease,” “cardiovascular disease,” intensive care unit: “ICU,” “cardiac injury,” and “mortality.” Bibliographies of allocated articles were reviewed for possible data sources.

2.2. Selection criteria

We performed a systematic review of studies that explored pre‐existing cardiovascular diseases as risk factors of severe COVID‐19, cardiac injury, ICU admission, or mortality. Inclusion criteria for eligibility were as follows: (a) types of studies: a retrospective, prospective, observational, descriptive or case‐control studies reporting cardiac biomarkers (including cardiac troponin I (cTnI), creatine kinase (CK), CK‐MB, aspartate aminotransferase (AST), lactate dehydrogenase (LDH), myoglobin, or N‐terminal‐pro hormone B‐type natriuretic peptide (NT‐proBNP) in patients with COVID‐19; (b) subjects: diagnosed patients with COVID‐19; (c) exposure/intervention: enclosing at least one outcome data for severe (defined as acute respiratory distress syndrome, mechanical ventilation, and ICU admission) vs nonsevere, ICU admission vs floor admission, develop cardiac injury (defined as cTnI elevation above 99th percentile) vs not, or survived vs expired cohorts; and (d) outcome indicator: the mean and standard deviation for each laboratory test or event and total sample size for demographics, comorbidities, and complications. The following exclusion criteria were considered: (a) pre‐print, case reports, reviews, editorial materials, conference abstracts, and summaries of discussions, (b) insufficient reported data information; or (c) in vitro or in vivo studies.

2.3. Data abstraction

Two investigators separately conducted literature screening, followed by data abstraction in a predesigned excel sheet by four investigators (RE, AE, MNA, and MEM). Any disagreement was resolved by another investigator (ET). Study characteristics (author name, publication date, journal name, ethnicity, study design, and sample size) and the patients' demographics (age and gender) were collected.

2.4. Statistical analysis

Data analysis was performed using RevMan version 5.3 and comprehensive meta‐analysis software version 3.0. 10 The standardized mean difference (SMD) or odds ratio (OR) and 95% confidence intervals (CIs) were applied to estimate pooled results from studies. Two levels of analysis were conducted; (a) four pairwise comparison for severity, myocardial injury, ICU admission, and mortality, then (b) all studies related to severity, ICU admission, cardiac injury, and mortality were pooled together to compare between patients with poor vs good prognosis. The results of the included studies were performed with random‐effect models. 11 Heterogeneity was evaluated using Cochran's Q statistic and quantified by using Higgin's I 2 statistics. If there was statistical heterogeneity among the results, further sensitivity analysis and meta‐regression were performed to determine the source of heterogeneity. Receiver operating characteristic (ROC) curve analysis was performed to assess the prognostic performance of cardiac biomarkers and area under the curve (AUC) was calculated. Next, the risk assessment decision tree was employed to identify laboratory and clinical predictor factors for poor prognosis. Accuracy, precision, and recall of model performance were evaluated. R Studio was employed using the following packages: tidyverse, magrittr, rpart, caret, and pROC. Finally, publication bias was assessed using a funnel plot and quantified using Egger's linear regression test. Asymmetry of the collected studies’ distribution by visual inspection or P‐value < .1 indicated obvious publication bias. 12

3. RESULTS

3.1. Study selection and characteristics

Using the key terms, a total of 4021 articles were retrieved using the search strategy. After screening by the abstract and title of 1541 studies, 160 articles were selected for full‐text assessment. Of these, 104 were excluded due to lack of enough data, and 56 were included for qualitative analysis. Pairwise comparison meta‐analysis was conducted; 29 articles to compare between the severe and nonsevere presentation of COVID‐19 disease, seven records to compare between cohorts who developed cardiac injury and those who are not, six records to compare between patients who were admitted to the ICU and those admitted to the general hospital ward and 16 studies to compare between survivors and expired patients (Figure 1A). The study included a total of 56 studies (52 retrospective and 4 prospective studies) published from 24 January 2020 to 7 May 2020. 1 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 These included 17 794 COVID‐19 patients from China (13 cities) and overseas (Figure 1B,C). The main characteristics of eligible studies are demonstrated in Table 1.

Figure 1.

Figure 1

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Selected studies. A, The workflow of the selection process. PRISMA guidelines were followed. B, The total sample size for each geographic location. Mixed: analysis included data from 169 hospitals located in 11 countries in Asia, Europe, and North America. C, Map of the source of patients with COVID‐19 in the eligible studies. COVID‐19, coronavirus disease‐2019; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta‐Analyses

Table 1.

Characteristics of the included studies

First author Sample size Age Gender
(1) Severity Year Publication date Journal name Continent Country Ethnicity Study design Severe Mild Severe, M (SD) Mild, M (SD) Severe, (M/F) Mild, (M/F) Reference no.
Aggarwal S 2020 29‐Apr Diagnosis (Berl) Des Moines USA American Retrospective 8 8 58.3 (28.6) 68.2 (40.0) 5/3 7/1 13
Chen C 2020 6‐Mar Zhonghua Xin Xue Guan Bing Za Zhi Wuhan China Asian Retrospective 24 126 NA NA 18/6 66/60 14
Chen G 2020 27‐Mar J Clin Invest Wuhan China Asian Retrospective 11 10 61.2 (7.04) 50.3 (9.8) 10/1 7/3 15
Deng Q 2020 8‐Apr Int J cardiol Wuhan China Asian Retrospective 67 45 67.3 (14.8) 54 (20.7) 38/29 19/26 16
Fang X 2020 11‐Apr J Infect Anhui China Asian Retrospective 7 46 54.3 (15.4) 39.9 (15.5) 5/2 22/24 17
Gao L 2020 15‐Apr Respir Res Wuhan China Asian Retrospective 30 24 67.4 (14.4) 51.6 (13.9) 16/14 8/16 18
He R 2020 12‐Apr J Clin Virol Wuhan China Asian Retrospective 69 135 62.3 (16.3) 42.3 (16.3) 37/32 42/93 19
Hong Y 2020 8‐Apr Ann Transl Med Zhejiang China Asian Retrospective 25 50 44.1 (11.3) 47.5 (14.2) 11/14 30/20 20
Lo I 2020 15‐Mar Int J Biol Sci Macau China Asian Retrospective 4 6 61 (5.0) 37 (19.0) 1/3 2/4 21
Mo P 2020 16‐Mar Clin Infect Dis Wuhan China Asian Retrospective 85 70 60.7 (14.1) 45.7 (15.6) 55/30 31/39 22
Pereira M 2020 24‐Apr Am J Transplant New York USA American Retrospective 27 63 65.7 (13.3) 52.3 (18.5) 16/11 37/26 23
Shi Y 2020 18‐Mar Crit Care Zhejiang China Asian Retrospective 49 438 56 (17.0) 45 (19.0) 36/13 223/215 24
Wan S 2020 21‐Mar J Med Virol Chongqing China Asian Retrospective 40 95 60.3 (15.6) 42 (11.8) 21/19 52/43 25
Wei Y 2020 17‐Apr J Infect Anhui China Asian Retrospective 30 137 49 (12.6) 40.8 (15.5) 20/10 75/62 26
Zhang G 2020 9‐Apr J Clin Virol Wuhan China Asian Retrospective 55 166 62.7 (16.3) 50.4 (20.9) 35/20 73/93 27
Zhang J 2020 19‐Feb Allergy Wuhan China Asian Retrospective 58 82 58.7 (45.9) 51.8 (38.5) 33/25 38/44 28
Zhao X 2020 29‐Apr BMC Infect Dis Hubei China Asian Retrospective 30 61 NA NA 14/16 35/26 29
Zhu Z 2020 22‐Apr Int J Infect Dis Zhejiang China Asian Retrospective 16 104 57.5 (11.7) 49.9 (15.5) 9/7 73/38 30
Feng Y 2020 10‐Apr Am J Respir Crit Care Med Wuhan China Asian Retrospective 54 352 57.7 (14.1) 50.3 (19.3) 33/21 190/162 31
Han Y 2020 27‐Mar MedRxiv Wuhan China Asian Retrospective 24 23 61 (41.5) 62.2 (29.6) 17/7 9/14 32
Ma K 2020 23‐Mar MedRxiv Chongqing China Asian Retrospective 20 64 60.3 (19.3) 46.8 (11.6) 12/8 36/28 33
Zhao W 2020 30‐Mar MedRxiv Beijing China Asian Retrospective 20 57 69 (15.0) 45 (17.0) 11/9 23/34 34
Zheng F 2020 24‐Mar Eur Rev Med Pharmacol Sci Hunan China Asian Retrospective 30 131 56.5 (14.4) 40.7 (14.8) 14/16 66/65 35
Chen X 2020 17‐Apr Clin Infect Dis Wuhan China Asian Retrospective 10 21 63.9 (15.2) 52.8 (14.2) 9/1 13/8 36
Han H 2020 31‐Mar J Med Virol Wuhan China Asian Retrospective 60 198 58.9 (14.4) 58.9 (10.8) 21/39 71/127 37
Yang Y 2020 29‐Apr J Allergy Clin Immunol Shenzhen China Asian Retrospective 25 14 58.3 (26.7) 50.5 (41.5) 14/11 7/7 38
Li X 2020 12‐Apr J Allergy Clin Immunol Wuhan China Asian Retrospective 269 279 63.7 (13.3) 55.3 (16.3) 153/116 126/153 39
Zheng C 2020 27‐Mar Int J Infect Dis Wuhan China Asian Retrospective 21 34 NA NA NA NA 40
Wu J 2020 27‐Mar J Intern Med Multicenter China Asian Retrospective 83 197 63 (10.2) 37.5 (17.1) 45/38 106/91 41
(2) Cardiac injury With Without With, M (SD) Without, M (SD) With, (M/F) Without, (M/F)
Guo T 2020 27‐Mar JAMA Cardiol Wuhan China Asian Retrospective 52 135 71.4 (9.4) 53.5 (13.2) 34/18 57/78 42
Li M 2020 18‐Apr Nutrition, Metabolism & Cardiovascular Diseases Wuhan China Asian Retrospective 42 41 60 (13.3) 33 (5.2) 18/24 16/25 43
Shi S 2020 25‐Mar JAMA Cardiol Wuhan China Asian Retrospective 82 334 67.7 (45.2) 57 (51.1) 44/38 161/173 44
Liu Y 2020 16‐Mar MedRxiv Guangzhou China Asian Retrospective 15 276 64 (12.6) 47 (20.7) 11/4 122/154 45
Wei J 2020 30‐Apr Heart Sichuan China Asian Prospective 16 85 69.5 (14.4) 45 (16.3) 7/9 47/38 46
He X 2020 30‐Apr Zhonghua Xin Xue Guan Bing Za Zhi Shanghai China Asian Retrospective 24 30 69.2 (8.5) 66.1 (12.8) 17/7 17/13 47
Peng Y 2020 2‐Mar Zhonghua Xin Xue Guan Bing Za Zhi Wuhan China Asian Retrospective 16 96 58.2 (6.7) 61.5 (9.2) 9/7 44/52 48
(3) Admission ICU Floor ICU, M (SD) Floor, M (SD) ICU, (M/F) Floor, (M/F)
Goyal P 2020 18‐Apr N Engl J Med New York USA American Retrospective 130 263 63.3 (16.2) 61.2 (20.7) 92/38 146/117 49
Chu Y 2020 28‐April J Infect Zhejiang China Asian Retrospective 7 26 67 (17.7) 64.7 (16.6) 6/1 16/10 50
Du R 2020 7‐Apr Ann Am Thorac Soc Wuhan China Asian Retrospective 51 58 68.4 (9.7) 72.7 (11.6) 36/15 38/20 51
Huang C 2020 24‐Jan The Lancet Wuhan China Asian Prospective 13 28 50.3 (14.8) 49.2 (12.2) 11/2 19/9 1
Lei S 2020 4‐Apr EClinicalMedicine Wuhan China Asian Retrospective 15 19 57.7 (22.2) 44.7 (21.5) 5/10 9/10 52
Wang D 2020 7‐Feb JAMA Wuhan China Asian Retrospective 36 102 67 (15.6) 50 (20.7) 22/14 53/49 53
(4) Mortality Died Alive Died, M (SD) Alive, M (SD) Died, (M/F) Alive, (M/F)
Chen T 2020 16‐Mar BMJ Wuhan China Asian Retrospective 113 161 69 (11.1) 51.3 (21.5) 83/30 88/73 54
Du R 2020 7‐May Eur Respir J Wuhan China Asian Prospective 21 158 70.2 (7.7) 56 (13.5) 10/11 87/71 55
Mehra M 2020 1‐May N Engl J Med Mixed Mixed Mixed Retrospective 515 8395 55.8 (15.1) 48.7 (16.6) 336/179 5003/3392 56
Siciliano R 2020 10‐Mar Int J Infect Dis São Paulo Brazil Latin American Prospective 26 71 NA NA 19/7 42/29 57
Tomlins J 2020 27‐Apr J Infect Bristol UK Caucasian Retrospective 20 75 78 (9.6) 70.7 (19.3) 12/8 48/27 58
Wang L 2020 30‐Mar J Infect Wuhan China Asian Retrospective 65 274 76.3 (9.6) 68.7 (7.4) 39/26 127/147 59
Zhou F 2020 9‐Mar Lancet Wuhan China Asian Retrospective 54 137 69.3 (9.6) 51.7 (9.6) 38/16 81/56 60
Zhou W 2020 21‐Feb Signal Transduction Targeted Therapy Wuhan China Asian Retrospective 7 8 68 (3.3) 56.3 (10.0) 4/3 6/2 61
Deng Y 2020 20‐Mar Chin Med J (Engl) Wuhan China Asian Retrospective 109 116 68.3 (8.9) 43.3 (17.8) 73/36 51/65 62
Fu L 2020 16‐Mar MedRxiv Wuhan China Asian Retrospective 34 166 NA NA 16/18 NA 63
Li K 2020 27‐Mar MedRxiv Wuhan China Asian Retrospective 15 87 68 (14.1) 55 (16.3) 48/39 11/4 64
Luo X 2020 23‐Mar MedRxiv Wuhan China Asian Retrospective 100 303 72 (11.1) 49.3 (18.5) 57/43 136/167 65
Wang Y 2020 8‐Apr Am J Respir Crit Care Med Wuhan China Asian Retrospective 133 211 69.7 (11.1) 57.7 (16.3) 74/59 105/106 66
Zhang F 2020 24‐Mar MedRxiv Wuhan China Asian Retrospective 17 31 78.6 (8.3) 66.2 (13.7) 12/5 21/10 67
He X 2020 30‐Apr Zhonghua Xin Xue Guan Bing Za Zhi Shanghai China Asian Retrospective 26 28 69.7 (10.4) 64.8 (11.7) 16/10 18/10 47
Wang L 2020 14‐Apr Zhonghua Yan Ke Za Zhi Wuhan China Asian Retrospective 33 169 74.3 (14.1) 59 (13.3) 23/10 65/104 68
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3.2. Pooled analysis of demographic characteristics

The demographic characteristics of patients with COVID19 are shown in Table 2. The median age of 17 364 COVID‐19 patients across 53 studies ranged from 32 to 74 years in patients with a good prognosis and 47 to 77 years in patients with poor outcomes. Pooled estimates revealed significantly higher age in critical/expired cases (SMD = 1.0, 95% CI = 0.72‐1.31, P < .001) than the noncritical group. The results from 54 articles with a total sample size of 17 702 patients showed that the proportion of males was significantly higher in critical cases (OR = 1.50, 95% CI = 1.36‐1.69, P < .001). Evidence of heterogeneity and publication bias were observed for age data (I 2 = 97.1%, P < .001, Egger's P = .041), but not for gender (I 2 = 26.5%, P = .041, Egger's P = .58).

Table 2.

Predictors for poor outcomes in patients with COVID‐19

Characteristics Number studies Sample size Test of association Effect size Heterogeneity Publication bias
Total Poor prognosis Good prognosis Statistical method Effect measure Analysis model Estimate 95% CI P‐value I 2 P‐value P (Egger's test)
Demographic data
Age 53 17 364 2942 14 422 IV SMD Random 1.01 0.72‐1.31 <.001 97.11% <.001 .041
Sex (male) 54 17 702 3022 14 680 MH OR Random 1.50 1.34‐1.69 <.001 26.56% .041 .58
Cardiac biomarkers
Troponin I 32 4953 1321 3632 IV SMD Random 0.96 0.71‐1.22 <.001 91.9% <.001 .46
Creatine kinase 30 4528 1262 3266 IV SMD Random 0.68 0.47‐0.90 <.001 89.32% <.001 .55
CK‐MB 27 3816 994 2822 IV SMD Random 0.80 0.59‐1.01 <.001 86.63% <.001 .12
AST 38 5557 1483 4074 IV SMD Random 0.71 0.57‐0.84 <.001 74.70% <.001 .25
LDH 30 3992 1145 2847 IV SMD Random 1.12 0.86‐1.38 <.001 90.67% <.001 .57
Myoglobin 10 2232 536 1696 IV SMD Random 1.16 0.80‐1.51 <.001 90.06% <.001 .98
NT‐proBNP 20 3240 719 2521 IV SMD Random 1.15 0.83‐1.48 <.001 91.52% <.001 .80
Presentation
Chest pain/tightness 18 3325 974 2351 MH OR Random 1.93 1.14‐3.28 .014 70.23% <.001 .818
Comorbidities
Hypertension 50 16 974 2782 14 192 MH OR Random 2.22 1.75‐2.81 <.001 77.83% <.001 .027
Diabetes 51 17 120 2826 14 294 MH OR Random 1.88 1.59‐2.24 <.001 32.08% .020 .96
CHD 40 15 864 2508 13 356 MH OR Random 3.42 2.65‐4.42 <.001 49.86% .011 .031
COPD 35 14 658 2148 12 510 MH OR Random 3.08 2.36‐4.03 <.001 10.12% .30 .42
CVD 21 3791 970 2821 MH OR Random 4.49 2.72‐7.40 <.001 60.8% <.001 .85
CKD 26 5212 1450 3762 MH OR Random 2.75 1.77‐4.28 <.001 32.4% .06 .046
Cancer 31 5563 1567 3996 MH OR Random 1.97 1.41‐2.76 <.001 8.35% .33 .73
Complications
ARDS 14 2963 877 2086 MH OR Random 34.8 13.6‐89.2 <.001 87.6% <.001 .12
Pneumonia 10 1211 348 863 MH OR Random 3.66 2.04‐6.57 <.001 0.0% .52 .72
AKI 13 2979 844 2135 MH OR Random 15.7 8.24‐30.2 <.001 57.88% <.001 .83
Liver injury 11 2050 558 1492 MH OR Random 2.93 1.01‐8.46 .049 86.55% <.001 .030
Arrhythmia 10 10 421 847 9574 MH OR Random 3.40 1.67‐6.94 <.001 66.98% <.001 .35
Heart failure 9 10 391 781 9610 MH OR Random 4.15 2.41‐7.15 <.001 56.8% .020 .23
Coagulopathy 4 996 221 775 MH OR Random 5.86 2.83‐12.13 <.001 50.96% .010 .71
Shock 12 1915 628 1287 MH OR Random 36.9 11.05‐123.5 <.001 70.16% <.001 .73
Sepsis 2 465 167 298 MH OR Random 220.0 30.38‐1593.71 <.001 0.0% .69 NA
Treatment
Antiviral 16 3620 1150 2470 MH OR Random 0.985 0.67‐1.45 .94 42.84% .036 .77
Antibiotics 11 2924 920 2004 MH OR Random 3.36 1.66‐6.77 .001 71.46% <.001 .73
Glucocorticoids 23 3961 1289 2672 MH OR Random 3.52 2.51‐4.93 <.001 67.97% <.001 .83
Immunoglobulin 12 2300 738 1562 MH OR Random 3.41 1.90‐6.14 <.001 84.66% <.001 .16
Lopinavir/ritonavir 3 299 122 177 MH OR Random 0.620 0.097‐3.97 .61 87.33% <.001 .72
Oseltamivir 2 494 130 364 MH OR Random 0.974 0.61‐1.56 .91 5.46% .30 NA
Interferon 4 842 302 540 MH OR Random 0.794 0.285‐2.21 .65 79.84% .002 .43
Hydroxychloroquine 2 106 35 71 MH OR Random 6.67 2.00‐22.22 .002 0.0% .35 NA
Azithromycin 2 106 35 71 MH OR Random 5.49 1.13‐26.66 .03 38.49% .20 NA
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Abbreviations: AKI, acute kidney injury; ARDS, acute respiratory distress syndrome; AST, aspartate aminotransferase; CHD, chronic heart disease; CI, confidence interval; CKD, chronic kidney disease; CK‐MB, creatine kinase myocardial band; COPD, chronic obstructive pulmonary disease; COVID‐2019, coronavirus disease‐2019; I2, the ratio of true heterogeneity to total observed variation; IV, inverse variance; LDH, lactate dehydrogenase; MH, Mantel‐Haenszel; NT‐proBNP, N‐terminal‐pro hormone B‐type natriuretic peptide; OR, odds ratio; SMD, standardized mean difference. Bold values indicate significance at P < 0.05.

3.3. Pooled analysis of cardiac biomarkers

The laboratory examination of the included studies is demonstrated in Table 2. Meta‐analysis showed higher levels of cardiac biomarkers in critical/expired patients; high‐sensitivity cTnI (SMD = 0.96, 95% CI = 0.71‐1.22, P < .001), creatine kinase (SMD = 0.68, 95% CI = 0.47‐0.90, P < .001), CK‐MB (SMD = 0.80, 95% CI = 0.59‐1.01, P < .001), AST (SMD = 0.71, 95% CI = 0.57‐0.84, P < .001), LDH (SMD = 1.12, 95% CI = 0.86‐1.38, P < .001), myoglobin (SMD = 1.16, 95% CI = 0.80‐1.51, P < .001), and NT‐proBNP (SMD = 1.15, 95% CI = 0.83‐1.48, P < .001). A considerable heterogeneity was observed across studies for all laboratory parameters; cTnI (I 2 = 91.9%, P < .001), creatine kinase (I 2 = 89.3%, P < .001), CK‐MB (I 2 = 86.6%, P < .001), AST (I 2 = 74.7%, P < .001), LDH (I 2 = 90.6%, P < .001), myoglobin (I 2 = 90.1%, P < .001), and NT‐proBNP (I 2 = 91.5%, P < .001). Subgroup analysis by ethnicity and sample size did not resolve heterogeneity. No evidence of publication bias was found for all laboratory tests.

3.4. Pooled analysis of comorbidities

We then compared the difference of the prevalence of the comorbidities in patients with poor outcomes compared with those with good outcomes. The presence of prior cerebrovascular diseases (OR = 4.49, 95% CI = 2.72‐7.40, P < .001) or chronic heart diseases (OR = 3.42, 95% CI = 2.65‐4.42, P < .001) had the highest risk for poor prognosis, followed by chronic obstructive pulmonary disease (COPD) (OR = 0.08, 95% CI = 2.36‐4.03, P < .001). For all other reported comorbid conditions, their proportion was also statistically higher in critical/expired group; chronic kidney disease (CKD) (OR = 2.75, 95% CI = 1.77‐4.28, P < .001), hypertension (OR = 2.22, 95% CI = 1.75‐2.81, P < .001), diabetes mellitus (OR = 1.88, 95% CI = 1.59‐2.24, P < .001), and malignant neoplasm (OR = 1.97, 95% CI = 1.41‐2.76, P < .001). Apart of articles for hypertension (I 2 = 77.8%, P < .001) and cerebrovascular diseases (I 2 = 60.8%, P < .001), homogeneity was observed across studies. Pairwise comparison yielded evidence of publication bias for hypertension (Egger's P‐value = .027), chronic heart disease (Egger's P‐value = .031), and CKD (Egger's P‐value = .046) (Table 2).

3.5. Pooled analysis of secondary complications

Summarizing analysis revealed a 93% increased risk of poor prognosis in cohorts who experienced chest pain or tightness (OR = 1.93, 95% CI = 1.14‐3.28, P = .014). In addition, meta‐analysis showed that patients with COVID‐19 who developed complications were more likely to have adverse outcomes with higher risk of mortality (Table 2). The highest risk was for those with ARDS (OR = 34.8, 95% CI = 13.6‐89.2, P < .001), shock (OR = 31.4, 95% CI = 6.26‐157, P < .001), and acute kidney injury (OR = 15.7, 95% CI = 8.24‐30.2, P < .001), followed by coagulopathy (OR = 5.86, 95% CI = 2.83‐12.13, P < .001), heart failure (OR = 4.15, 95% CI = 2.41‐7.15, P < .001), pneumonia (OR = 3.66, 95% CI = 2.04‐6.57, P < .001), arrhythmia (OR = 3.40, 95% CI = 1.67‐6.94, P < .001), and liver injury (OR = 2.93, 95% CI = 1.01‐8.46, P = .049). Obvious heterogeneity was observed across studies. Apart of liver injury articles (P = .030), the Egger's test provides no evidence of publication bias.

3.6. Pooled analysis of COVID‐19‐related medications

Furthermore, as depicted in Table 2 patients who received antibiotics (OR = 3.36, 95% CI = 1.66‐6.77, P = .001), glucocorticoids (OR = 3.52, 95% CI = 2.51‐4.93, P < .001), immunoglobulins (OR = 3.41, 95% CI = 1.90‐6.14, P < .001), and hydroxychloroquine (OR = 6.67, 95% CI = 2.0‐22.2, P = .002) had higher risk for poor prognosis. However, noteworthy, there was significant heterogeneity between studies (I 2 = 67.9%‐84.6%), and only two studies had reported hydroxychloroquine.

3.7. Pairwise comparisons for severity, cardiac injury, ICU admission, and mortality

Table S1 summarizes pooled estimates for seven cardiac biomarkers, eight comorbidities, and nine secondary complications in patients with COVID‐19 with severe presentation compared with nonsevere cohorts, who developed secondary cardiac injury versus not, ICU admitted patients vs general ward patients and survived vs expired. The Forest plot for the pooled analyses is presented in Figures S1‐S11. Funnel plots for assessment of publication bias are depicted in Figure S12. Meta‐regression to assess the impact of study characteristics as sample size, the city of the study, and timing of publications as moderators for the study effect size of each pairwise comparison is demonstrated in Table S2.

3.8. Meta‐regression analysis

To assess the impact of study characteristics as sample size, the city of the study, and timing of publications as moderators for the study effect size, meta‐regression was performed. Results of studies comparing critical/expired patients with noncritical cases suggested confounding of AST (coefficient = 0.31, 95% CI = 0.03‐0.59, P = .028) and pneumonia (coefficient = 1.39, 95% CI = 0.04‐2.74, P = .040) by publication date, and hypertension (coefficient = 0.76, 95% CI = 0.17‐1.35, P = .010) and chronic heart disease (coefficient = 0.75, 95% CI = 0.28‐1.22, P = .002) by ethnicity (Table 3).

Table 3.

Meta‐regression analysis for overall analysis

Parameter Feature Categories Number of studies Coefficient Lower bound Upper bound P‐value
(1) Demographic data
Age Country of origin China vs others 48/5 0.74 −0.59 2.08 .28
Sample size >50 vs ≤50 42/11 0.57 −0.39 1.54 .25
Publication date Jan‐Mar vs Apr‐May 27/26 0.64 −0.15 1.42 .11
Male gender Country of origin China vs others 48/6 0.07 −0.20 0.34 .60
Sample size >50 vs ≤50 43/43 0.02 −0.51 0.56 .94
Publication date Jan‐Mar vs Apr‐May 28/26 0.20 −0.01 0.41 .07
(2) Presentation
Chest pain or tightness Sample size >50 vs ≤50 16/2 −0.83 −2.87 1.21 .42
Publication date Jan‐Mar vs Apr‐May 10/8 0.12 −0.92 1.18 .81
(3) Cardiac biomarkers
Troponin I Country of origin China vs others 28/4 0.34 −0.72 1.40 .53
Sample size >50 vs ≤50 27/5 0.28 −0.67 1.24 .56
Publication date Jan‐Mar vs Apr‐May 18/14 0.12 −0.57 0.82 .73
Creatine kinase Country of origin China vs others 25/5 0.16 −0.52 0.83 .65
Sample size >50 vs ≤50 24/6 0.3 −0.35 0.95 .37
Publication date Jan‐Mar vs Apr‐May 18/12 0.36 −0.15 0.87 .17
CK‐MB Country of origin China vs others 23/4 0.06 −0.62 0.74 .86
Sample size >50 vs ≤50 23/4 0.63 −0.1 1.36 .09
Publication date Jan‐Mar vs Apr‐May 13/14 0.48 −0.001 0.96 .05
AST Country of origin China vs others 36/2 −0.03 −0.74 0.68 .94
Sample size >50 vs ≤50 28/10 0.23 −0.13 0.59 .22
Publication date Jan‐Mar vs Apr‐May 22/16 0.31 0.03 0.59 .028
LDH Country of origin China vs others 29/1 −0.1 −1.91 1.71 .91
Sample size >50 vs ≤50 22/8 0.27 −0.4 0.93 .43
Publication date Jan‐Mar vs Apr‐May 17/13 0.39 −0.15 0.92 .16
NT‐proBNP Country of origin China vs others 19/1 0.3 −1.14 1.74 .68
Sample size >50 vs ≤50 19/1 0.5 −0.98 1.99 .51
Publication date Jan‐Mar vs Apr‐May 10/10 0.57 −0.07 1.21 .08
(4) Comorbidities
Hypertension Country of origin China vs others 44/6 0.76 0.17 1.35 .010
Sample size >50 vs ≤50 41/9 0.43 −0.26 1.12 .22
Publication date Jan‐Mar vs Apr‐May 27/23 0.24 −0.17 0.64 .25
Diabetes Country of origin China vs others 45/6 0.3 0.04 0.57 .14
Sample size >50 vs ≤50 42/9 0.51 −0.15 1.18 .34
Publication date Jan‐Mar vs Apr‐May 26/25 0.16 −0.1 0.42 .13
CHD Country of origin China vs others 37/3 0.75 0.28 1.22 .002
Sample size >50 vs ≤50 34/6 0.63 −0.24 1.49 .15
Publication date Jan‐Mar vs Apr‐May 25/15 0.2 −0.2 0.6 .33
COPD Country of origin China vs others 30/5 0.61 −0.09 1.32 .09
Sample size >50 vs ≤50 31/4 −0.28 −1.96 1.40 .74
Publication date Jan‐Mar vs Apr‐May 15/20 0.19 −0.46 0.83 .57
CVD Country of origin China vs others 19/2 1.08 −0.87 3.03 .28
Sample size >50 vs ≤50 18/3 0.42 −1.16 2.00 .60
Publication date Jan‐Mar vs Apr‐May 11/10 0.45 −0.48 1.38 .35
CKD Country of origin China vs others 23/3 0.62 −0.32 1.56 .20
Sample size >50 vs ≤50 22/4 −0.06 −1.47 1.34 .93
Publication date Jan‐Mar vs Apr‐May 13/13 −0.20 −0.62 1.01 .63
Cancer Country of origin China vs others 28/3 0.33 −0.88 1.53 .59
Sample size >50 vs ≤50 26/5 −0.48 −1.61 0.66 .41
Publication date Jan‐Mar vs Apr‐May 15/16 0.43 −0.25 1.10 .21
(5) Complications
ARDS Country of origin China vs others 13/1 −3.82 −11.04 3.41 .30
Sample size >50 vs ≤50 12/2 3.95 −1.36 9.26 .15
Publication date Jan‐Mar vs Apr‐May 9/5 0.41 −1.90 2.71 .73
Pneumonia Country of origin China vs others 9/1 −3.26 −7.81 1.28 .16
Sample size >50 vs ≤50 8/2 0.73 −2.77 4.21 .68
Publication date Jan‐Mar vs Apr‐May 6/4 1.39 0.04 2.74 .040
AKI Country of origin China vs others 12/1 −0.71 −4.44 3.02 .71
Sample size >50 vs ≤50 12/1 0.23 −1.21 1.67 .75
Liver injury Country of origin China vs others 10/1 −0.89 −4.82 3.04 .66
Sample size >50 vs ≤50 10/1 −0.68 −2.79 1.44 .53
Arrhythmia Country of origin China vs others 7/3 0.82 −1.02 2.66 .38
Sample size >50 vs ≤50 8/2 0.83 −1.36 3.01 .46
Publication date Jan‐Mar vs Apr‐May 4/6 0.17 −1.65 2.00 .85
Heart failure Country of origin China vs others 6/3 0.76 0.08 1.44 .030
Publication date Jan‐Mar vs Apr‐May 6/3 −0.03 −0.72 0.66 .93
Shock Sample size >50 vs ≤50 8/4 1.97 −0.10 4.05 .06
Publication date Jan‐Mar vs Apr‐May 8/4 −1.25 −3.25 0.75 .22
(6) Treatment
Antiviral Sample size >50 vs ≤50 15/4 −0.27 −2.35 1.80 .79
Publication date Jan‐Mar vs Apr‐May 7/12 0.24 −1.25 1.73 .75
Antibiotics Sample size >50 vs ≤50 11/4 1.14 −0.99 3.28 .29
Publication date Jan‐Mar vs Apr‐May 10/5 0.59 −0.80 1.99 .40
Glucocorticoids Sample size >50 vs ≤50 17/6 0.29 −0.68 1.27 .55
Publication date Jan‐Mar vs Apr‐May 12/11 0.06 −0.63 0.76 .85
Immunoglobulin Sample size >50 vs ≤50 10/2 0.25 −1.49 2.01 .77
Publication date Jan‐Mar vs Apr‐May 8/4 0.69 −0.50 1.90 .25
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Note: Variables with number of studies ≥10 were included.

Abbreviations: AKI, acute kidney injury; ARDS, acute respiratory distress syndrome; AST, aspartate aminotransferase; CHD, chronic heart disease; CKD, chronic kidney disease; CK‐MB, creatine kinase‐MB; COPD, chronic obstructive pulmonary disease; CVD, cardiovascular disease; LDH, lactate dehydrogenase; NT‐proBNP, N‐terminal‐pro hormone B‐type natriuretic peptide.

3.9. Decision tree classifier model

Receiver operating characteristics (ROC) curves were first employed to analyze the prognostic performance of cardiac markers for predicting adverse outcomes and to select the best cutoff threshold with high sensitivity and specificity. The highest area under the curves (AUC) were for myoglobin (AUC = 0.91 ± 0.07, P = .002) and high‐sensitive cTnI (AUC = 0.89 ± 0.04, P < .001) at the cutoff values of 72 ng/mL and 13.75 ng/L, respectively, followed by NT‐proBNP (AUC = 0.86 ± 0.06, P < .001) and AST (AUC = 0.84 ± 0.04, P < .001). Combining cardiac markers with demographic and clinical features, decision tree analysis was used to predict mortality and severity in patients with COVID‐19. Age, cTnI, and AST levels were able to classify patients into high and low‐risk patients (Figure 2A,B). High troponin I over 13.75 ng/L combined with either advanced age over 60 years or elevated AST level over 27.72 U/L were the best model to predict poor outcomes (classification accuracy = 81.03%, precision = 74.1%, recall = 86.0%, and diagnostic odds ratio = 20.8). After conversion of SMD to OR, meta‐analysis showed that patients with high cTnI (OR = 5.22, 95% CI = 3.73‐7.31, P < .001) and AST levels (OR = 3.64, 95% CI = 2.84‐4.66, P < .001) were more likely to develop adverse outcomes for COVID‐19 disease.

Figure 2.

Figure 2

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A, Decision tree model analysis for clinical and cardiac biomarkers. Based on several inputs (clinical parameters and biomarkers), a model was created by a multilevel split. Each interior node corresponds to one of the input variables, each leaf represents a value of the target variable given the values of the input variables represented by the path from the root to the leaf. B, Receiver operating characteristics for cardiac biomarkers. C, Forest plot of high‐sensitivity cardiac troponin I in critical/expired patients compared to noncritical cases. Each horizontal bar represents a study, with lines extending from the symbols representing 95% confidence intervals. The size of the data marker indicates relative weight. Pooled estimates are represented by the black diamond. D, Forest plot for AST in critical/expired patients compared with noncritical cases. AST, aspartate aminotransferase; AUC, area under the curve; CK‐MB, creatine kinase myocardial band; LDH, lactate dehydrogenase; NT‐proBNP, N‐terminal‐pro hormone B‐type natriuretic peptide; LL, lower limit; SE, standard error; UL, upper limit

4. DISCUSSION

Our meta‐analysis has several important aspects. We include a robust sample size with broad, global geographic reach. Utilizing a two‐arms meta‐analysis for 56 articles and 17 794 COVID‐19 subjects, our findings reveal the association of COVID‐19 mortality with high levels of cardiac biomarkers. We amplify previous smaller meta‐analyses and the single site or regional studies. Furthermore, as of 8 May 2020, we enclosed a larger number of studies and patients, and involved more cardiac biomarkers, demographics, and clinical data than prior studies, demonstrating multiple predictors of cardiac injury, poor prognosis, severity, ICU admission, and mortality. In addition, for prognostic risk assessment, we employed decision tree model analysis for both serum biomarkers and the clinical data and performed ROC curves analyses. Although our analysis included 169 hospitals located in 11 countries in Asia and Europe, it is largely retrospective.

Meta‐regression analyses indicated the pooled results were independent to study characteristics and decision tree analysis revealed that cTnI, AST, and potentially other serum biomarkers could be predictors of risk. One significant limitation, inherent in the use of meta‐analyses to guide further clinical practice is the heterogeneity across studies, including differences in study methods.

COVID‐19 pulmonary and cardiac complications are difficult to disaggregate. Before the SARS‐CoV‐2 pandemic, acute viral infections were associated with acute coronary syndromes. 69 Despite limited elevated cTnl findings in less severe cases, significantly higher cTnI unmasks the subset of patients with poorer outcomes as earlier seen in 341 patients from China. 70

Similarly, in 112 patients with COVID‐19 in China, elevated troponin was linked to severity and mortality despite normal levels of troponin at admission. 16 Another prior systematic literature, from 1 December 2019 to 27 March 2020, in 4189 patients with COVID‐19 from 28 studies, higher mean troponin, with a similar trend for CK‐MB, myoglobin, and NT‐proBNP were associated with higher mortality (summary risk ratio 3.85, 2.13‐6.96; P < .001). 71

A recent retrospective single‐center cohort study of patients between 28 January 2020 and 16 March 2020, from the Central Hospital of Wuhan, also reported 176 patients (116 survivors, 60 nonsurvivors) with elevated cTnI and increased odds of mortality by the regression models. 72

Moreover, a larger cohort enrolled 671 patients with severe COVID‐19 from 1 January to 23 February 2020. As a predictor of in‐hospital mortality, the area under the receiver operating characteristic curve of initial cTnI was 0.92 (95% CI, 0.87‐0.96; sensitivity, 0.86; specificity, 0.86; P < .001). Overall, multiple abnormal laboratory values on admission were higher in nonsurvivors, including CK‐MB, myoglobin, cTnI, and NT‐proBNP (all P < .001). 73

The exact pathway by which elevated biomarkers leads to death with COVID‐19 with systemic inflammatory activity may include myocarditis, thrombosis, and additionally unstable coronary atherosclerotic plaque rupture. Hence, beyond the predominant pulmonary complications, severity, and mortality sources include viral myocarditis, cytokine‐driven myocardial damage, microangiopathy, and acute coronary syndromes. 74 Therefore, biomarkers may identify a heightened inflammatory response, including endothelial dysfunction and microvascular damage.

There are several limitations to our analysis and review. The actual cause of mortality may be obscured by unmeasured or unknown confounders, underestimated by analysis of multivariable regression. Understanding CVD‐associated mortality must integrate biomarker data with cardiac imaging and physiologic and structural abnormalities. In addition, the percentage of patients with sepsis has been underreported in our report and cardiac injury may correlate with the prevalence of shock with severe COVID‐19. 75 Another limitation of these data is the lack of a determination of timing and estimated glomerular filtration rate as factors. Although cardiac biomarkers may reflect myocardial injury, inflammation, and remodeling, interpretation of biomarkers in chronic kidney disease (CKD) can be complicated by decreased urinary clearance and/or overall CKD‐associated chronic inflammation. The prognostic power of future biomarker analyses for COVID‐19 mortality should be trended over time and account for the degree of renal dysfunction. 76 Finally, in consideration of the immense COVID‐19 global mortality, over 360 000 deaths, 77 with over 100 000 deaths in the US alone 78 at the time of manuscript submission, despite our relatively large sample size, our data will require ongoing supplementation, to overcome inherent statistical bias and confirming our results.

In conclusion, COVID‐19 severity and mortality are compounded by vascular and myocardial injury. Elevated cardiac injury biomarkers may improve the identification of those patients at the highest risk and potentially lead to improved therapeutic approaches.

CONFLICT OF INTERESTS

All the authors declare that there are no conflict of interests.

AUTHOR CONTRIBUTIONS

EAT and RME: study design; RME, AE, MNA, ME‐M, and ME‐M: study identification and data extraction; EAT, RME, and MHH: statistical analysis; EAT, RME, MHH, AE, and MSF: data interpretation; EAT, RME, MHH, AE, MNA, M E‐M, M E‐M, KCF, and MSF: original draft preparation. All authors revised and approved the final version of the manuscript.

Supporting information

Supporting information

Click here for additional data file. (8.3MB, doc)

Toraih EA, Elshazli RM, Hussein MH, et al. Association of cardiac biomarkers and comorbidities with increased mortality, severity, and cardiac injury in COVID‐19 patients: A meta‐regression and decision tree analysis. J Med Virol. 2020;92:2473–2488. 10.1002/jmv.26166

Contributor Information

Rami M. Elshazli, Email: Relshazly@horus.edu.eg.

Mohammad H. Hussein, Email: mhussein1@tulane.edu.

Abdelaziz Elgaml, Email: Elgamel3a@mans.edu.eg.

Mohamed Amin, Email: Dr.mohamednasr@hotmail.com.

Mohammed El‐Mowafy, Email: Seven@mans.edu.eg.

Mohamed El‐Mesery, Email: Elmesery@hotmail.com.

Juan Duchesne, Email: jduchesn@tulane.edu.

Mary T. Killackey, Email: mkillack@tulane.edu.

Keith C. Ferdinand, Email: kferdina@tulane.edu.

Emad Kandil, Email: ekandil@tulane.edu.

Manal S. Fawzy, Email: manal_mohamed@med.suez.edu.eg.

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