The relationship of COVID-19 severity with cardiovascular disease and its traditional risk factors: A systematic review and meta-analysis

Background: Whether cardiovascular disease (CVD) and its traditional risk factors predict severe coronavirus disease 2019 (COVID-19) is uncertain, in part, because of potential confounding by age and sex. Methods: We performed a systematic review of studies that explored pre-existing CVD and its traditional risk factors as risk factors of severe COVID-19 (defined as death, acute respiratory distress syndrome, mechanical ventilation, or intensive care unit admission). We searched PubMed and Embase for papers in English with original data (≥10 cases of severe COVID-19). Using random-effects models, we pooled relative risk (RR) estimates and conducted meta-regression analyses. Results: Of the 373 publications identified in our search, 15 papers met our inclusion criteria, with 51,845 COVID-19 patients including 9,066 severe cases. Older age was consistently associated with severe COVID-19 in all eight eligible studies, with RR >~5 in >60-65 vs. <50 years. Two studies showed no change in the RR of age after adjusting for covariate(s). In univariate analyses, factors significantly associated with severe COVID-19 were male sex (14 studies; pooled RR=1.70, [95%CI 1.52-1.89]), hypertension (10 studies; 2.74 [2.12-3.54]), diabetes (11 studies; 2.81 [2.01-3.93]), and CVD (9 studies; 3.58 [2.06-6.21]). RR for male sex was likely to be independent of age. Meta-regression analyses were suggestive of confounding by age for the other three factors. Only two studies reported multivariable analysis, with one showing non-significant association for CVD and the other demonstrating adjusted RR ~2 for hypertension and diabetes. No study explored renin-angiotensin system inhibitors as a risk factor for severe COVID-19. Conclusions: In addition to older age and male sex, hypertension, diabetes, and CVD were associated in univariate analyses with severe COVID-19. Although there is still uncertainty regarding the magnitude of potential confounding, these risk factors can be used to inform objective decisions on COVID-19 testing, clinical management, and workforce planning.