Clinical characteristics of COVID-19 with cardiac injury: a systematic review and meta-analysis

Abstract

Objectives: Cardiac injury is associated with poor prognosis of 2019 novel coronavirus disease 2019 (COVID-19), but the risk factors for cardiac injury have not been fully studied. In this study, we carried out a systematic analysis of clinical characteristics in COVID-19 patients to determine potential risk factors for cardiac injury complicated COVID-19 virus infection.

Methods: We systematically searched relevant literature published in Pubmed, Embase, Europe PMC, CNKI and other databases. All statistical analyses were performed using STATA 16.0.

Results: We analysed 5726 confirmed cases from 17 studies. The results indicated that compared with non-cardiac-injured patients, patients with cardiac injury are older, with a greater proportion of male patients, with higher possibilities of existing comorbidities, with higher risks of clinical complications, need for mechanical ventilation, ICU transfer and mortality. Moreover, C-reactive protein, procalcitonin, D-dimer, NT-proBNP and blood creatinine in patients with cardiac injury are also higher while lymphocyte counts and platelet counts decreased. However, we fortuitously found that patients with cardiac injury did not present higher clinical specificity for chest distress (P = 0.304), chest pain (P = 0.334), palpitations (P = 0.793) and smoking (P = 0.234). Similarly, the risk of concomitant arrhythmia (P = 0.103) did not increase observably either.

Conclusion: Age, male gender and comorbidities are risk factors for cardiac injury complicated COVID-19 infection. Such patients are susceptible to complications and usually have abnormal results of laboratory tests, leading to poor outcomes. Contrary to common cardiac diseases, cardiac injury complicated COVID-19 infection did not significantly induce chest distress, chest pain, palpitations or arrhythmias. Our study indicates that early prevention should be applied to COVID-19 patients with cardiac injury to reduce adverse outcomes.

Keywords: COVID-19; Cardiac injury; coronavirus; meta analysis.

Fig. 1.

The proportion of articles classified by diagnostic criteria of cardiac injury.

Fig. 2.

Literature search and selection process.

Fig. 3.

Egger's test for death between patients with cardiac injury and non-cardiac injury.

Fig. 4.

Forest plot of age difference between patients with cardiac injury and non-cardiac injury (CI, confidence interval; SMD, standard mean difference; P = 0.000 I² = 88.9%).

Fig. 5.

Forest plot of differences in the number of males between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.003 I² = 69.4%).

Fig. 6.

Forest plots showing differences of hypertension between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.000 I² = 86.8%).

Fig. 7.

Forest plots showing differences of CHD between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.014 I² = 62.4%).

Fig. 8.

Forest plots showing differences of diabetes between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.157 I² = 35.5%).

Fig. 9.

Forest plots showing differences of COPD between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.189 I² = 34.8%).

Fig. 10.

Forest plots showing differences of complication between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.751 I² = 0%).

Fig. 11.

Forest plot of differences in ICU admissions between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.000 I² = 92.7%).

Fig. 12.

Forest plot of differences in the number of death between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.004 I² = 60.0%).

Fig. 13.

Forest plots showing differences of complication between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.751 I² = 0%).

Fig. 14.

Forest plots showing differences of PCT between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.000 I² = 81.3%).

Fig. 15.

Forest plots showing differences of NT-proBNP between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.000 I² = 90.6%).

Fig. 16.

Forest plots showing differences of D-dimer between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.021 I² = 89.3%).

Fig. 17.

Forest plots showing differences of creatinine between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.001 I² = 77.8%).

Fig. 18.

Forest plots showing differences of lymphocyte counts between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.076 I² = 52.7%).

Fig. 19.

Forest plots showing differences of platelet counts between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.911 I² = 0%).

Fig. 20.

Forest plots showing differences of Na⁺ between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.000 I² = 92.6%).

Fig. 21.

Forest plots showing differences of K⁺ between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.061 I² = 71.6%).

Fig. 22.

Forest plots showing immunoglobulin therapy between patients with cardiac injury and non-cardiac injury. (CI, confidence interval; RR, risk ratio; P = 0.000 I² = 91.9%).