Beyond Acute COVID-19: A Review of Long-term Cardiovascular Outcomes
Bibliographic record
Abstract
Statistics Canada estimated that approximately 1.4 million Canadians suffer from long COVID. Although cardiovascular changes during acute SARS-CoV-2 infection are well documented, long-term cardiovascular sequelae are less understood. In this review, we sought to characterize adult cardiovascular outcomes in the months after acute COVID-19 illness. In our search we identified reports of outcomes including cardiac dysautonomia, myocarditis, ischemic injuries, and ventricular dysfunction. Even in patients without overt cardiac outcomes, subclinical changes have been observed. Cardiovascular sequelae after SARS-CoV-2 infection can stem from exacerbation of preexisting conditions, ongoing inflammation, or as a result of damage that occurred during acute infection. For example, myocardial fibrosis has been reported months after hospital admission for COVID-19 illness, and might be a consequence of myocarditis and myocardial injury during acute disease. In turn, myocardial fibrosis can contribute to further outcomes including dysrhythmias and heart failure. Severity of acute infection might be a risk factor for long-term cardiovascular consequences, however, cardiovascular changes have also been reported in young, healthy individuals who had asymptomatic or mild acute disease. Although evolving evidence suggests that previous SARS-CoV-2 infection might be a risk factor for cardiovascular disease, there is heterogeneity in existing evidence, and some studies are marred by measured and unmeasured confounders. Many investigations have also been limited by relatively short follow-up. Future studies should focus on longer term outcomes (beyond 1 year) and identifying the prevalence of outcomes in different populations on the basis of acute and long COVID disease severity.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".