Long-term cardiac symptoms following COVID-19: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background There is growing body of literature on the long-term cardiac symptoms following COVID-19. We conducted a systematic review and meta-analysis to synthesize and evaluate related evidence to inform clinical management and future studies. Methods We searched two preprint and seven peer-reviewed article databases from January 1, 2020 to January 8, 2022 for studies investigating cardiac symptoms that persisted for at least 4 weeks among individuals who survived COVID-19. A customized Newcastle–Ottawa scale was used to evaluate the quality of included studies. Random-effects meta-analyses were performed to estimate the proportion of symptoms with 95% confidence intervals (CI), and stratified analyses were conducted to quantify the proportion of symptoms by study characteristics and quality. Results A total of 101 studies describing 49 unique long-term cardiac symptoms met the inclusion criteria. Based on quality assessment, only 15.8% of the studies (n=16) were of high quality, and most studies scored poorly on sampling representativeness. The two most examined symptoms were chest pain and arrhythmia. Meta-analysis showed that the proportion of chest pain was 10.1% (95% CI: 6.4-15.5) and arrhythmia was 9.8% (95% CI: 5.4-17.2). Stratified analyses showed that studies with low-quality score, small sample size, unsystematic sampling method, and cross-sectional design were most likely to report high proportions of symptoms. For example, the proportion of chest pain was 21.3% (95% CI: 10.5-38.5), 9.3% (95% CI: 6.0-14.0), and 4.0% (95% CI: 1.3-12.0) in studies with low, medium, and high-quality scores, respectively. Similar patterns were observed for other cardiac symptoms including hypertension, cardiac abnormalities, myocardial injury, thromboembolism, stroke, heart failure, coronary disease, and myocarditis. Discussion There is a wide spectrum of long-term cardiac symptoms following COVID-19. Findings of existing studies are strongly related to study quality, size and design, underscoring the need for high-quality epidemiologic studies to characterize these symptoms and understand their etiology. Research in context Evidence before this study Accumulating evidence shows long-term cardiac symptoms following COVID-19. However, no previous reviews systematically evaluated and synthesized findings from studies on long-term cardiac symptoms. Added value of this study This is the first systematic review and meta-analysis that focused on studies of long-term cardiac symptoms of COVID-19. We included 101 studies and identified 49 cardiac symptoms that are indicative of cardiac abnormalities. We scored their quality based on epidemiologic principles and identified domains of study design that need improvements. We quantified proportions of multiple long-term cardiac symptoms including chest pain, arrhythmia, and others. We also observed systematic differences in reported proportions of these symptoms by selected study characteristics, including total quality assessment score, sample size, sampling representativeness, and study design. High-quality studies identified here can provide important guidelines for future studies of long-term symptoms following COVID-19. Implications of all the available evidence Multiple domains of study design, especially sampling representativeness, need to be improved in future studies on long-term cardiac symptoms following COVID-19. Notably, low-quality and smaller studies tend to report a larger proportion of symptoms, are more likely to be subject to greater sampling variation, and hence are less precise. These studies should be revisited with the emergence of large studies with rigriours study designs. This systematic review and meta-analysis highlight the scope of persistent cardiac symptoms among those who survived the acute phase of COVID-19, and the importance of synthesizing rigorous evidence to inform post-COVID surveillance and management plans.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".