Impact of Long COVID-19 on Health Outcomes Among Adults With Preexisting Cardiovascular Disease and Hypertension: A Systematic Review
Bibliographic record
Abstract
Background: This review summarizes the impact of long COVID (LC) on the health of adults with preexisting cardiovascular disease (CVD) and hypertension. Methods: We searched Medline, Web of Science (Core Collection), and the Cumulative Index to Nursing and Allied Health Literature (CINAHL), without language restrictions, for articles published from December 1, 2019 through October 10, 2023, to ensure all relevant studies were captured. We included studies that enrolled adults (aged ≥18 years) diagnosed with CVD prior to COVID-19 infection whose infection was subsequently determined to be LC per the World Health Organization definition. We excluded studies with adults diagnosed with CVD concurrent with or subsequent to COVID-19 or with those who solely self-reported LC. We used a custom-built data extraction form to collect a range of study characteristics. Study quality was assessed using modified versions of the National Heart, Lung, and Blood Institute quality-assessment tools. Results: A total of 13,779 studies were identified; 53 were included in the final analysis. Of these, 27 were of good quality and 26 were of fair quality. Health outcomes consisted of the presence of prolonged symptoms of LC (n = 29), physiological health outcomes (n = 20), lifestyle behaviours (n = 19), psycho-social outcomes (n = 13), CVD complications (n = 5), and death and hospital readmission (n = 5). Thirty-four studies incorporated 2 or more outcomes, and 19 integrated only 1. Conclusions: Given the significant impact of LC among individuals with preexisting CVD, specially tailored clinical management is needed for members of this population. Additional studies on the impact of LC among those with CVD and other underlying conditions also would be beneficial.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".