Cardiovascular diseases as risk factors of post-COVID syndrome: a systematic review
Bibliographic record
Abstract
Abstract Background A growing proportion of people experience incomplete recovery months after contracting coronavirus disease 2019 (COVID-19). These COVID-19 survivors develop a condition known as post-COVID syndrome (PCS), where COVID-19 symptoms persist for > 12 weeks after acute infection. Limited studies have investigated PCS risk factors that notably include pre-existing cardiovascular diseases (CVD), which should be examined considering the most recent PCS data. This review aims to identify CVD as a risk factor for PCS development in COVID-19 survivors. Methods Following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) checklist, systematic literature searches were performed in the PubMed, Scopus, and Web of Science databases from the earliest date available to June 2023. Data from observational studies in English that described the association between CVD and PCS in adults (≥ 18 years old) were included. A minimum of two authors independently performed the screening, study selection, data extraction, data synthesis, and quality assessment (Newcastle-Ottawa Scale). The protocol of this review was registered under PROSPERO (ID: CRD42023440834). Results In total, 594 studies were screened after duplicates and non-original articles had been removed. Of the 11 included studies, CVD including hypertension (six studies), heart failure (three studies), and others (two studies) were significantly associated with PCS development with different factors considered. The included studies were of moderate to high methodological quality. Conclusion Our review highlighted that COVID-19 survivors with pre-existing CVD have a significantly greater risk of developing PCS symptomology than survivors without pre-existing CVD. As heart failure, hypertension and other CVD are associated with a higher risk of developing PCS, comprehensive screening and thorough examinations are essential to minimise the impact of PCS and improve patients’ disease progression.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".