Cardiovascular complications and outcomes among athletes with COVID-19 disease: a systematic review
Bibliographic record
Abstract
BACKGROUND: Current evidence still emerging regarding the risk of cardiovascular (CV) sequel associated with coronavirus disease 2019 (COVID-19) infection, and considerable replicated studies are needed to ensure safe return-to-play. Therefore, we aimed in this systematic review to measure the prevalence of CV complications suffered by COVID-19 athletic patients, explore the outcomes, optimal approaches to diagnoses, and safe return-to-play considerations. METHODS: A systematic search on post COVID-19 infection quantitative studies among athletes was conducted following MeSH terms in Medline, Cochrane Library, Ovid, Embase and Scopus (through 15 January 2022). We included peer-reviewed studies reported athletes' CV complications and the outcomes post COVID-19 infection. Editorials, letters, commentaries, and clinical guidelines, as well as duplicate studies were excluded. Studies involving non-athletic patients were also excluded. Quality assessment was performed using Newcastle-Ottawa Scale. RESULTS: We included 15 eligible articles with a total of 6229 athletes, of whom 1023 were elite or professional athletes. The prevalence of myocarditis ranged between 0.4% and 15.4%, pericarditis 0.06% and 2.2%, and pericardial effusion between 0.27% and 58%. Five studies reported elevated troponin levels (0.9-6.9%). CONCLUSIONS: This study provides a low prevalence of CV complications secondary to COVID-19 infection in short-term follow-up. Early recognition and continuous assessment of cardiac abnormality in competitive athletes are imperative to prevent cardiac complications. Establishing a stepwise evaluation approach is critical with an emphasis on imaging techniques for proper diagnosis and risk assessment for a safe return to play.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".