Cardiovascular Pathophysiology, Epidemiology, and Treatment Considerations of Coronavirus Disease 2019 (COVID-19): A Review
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is rapidly evolving, with important cardiovascular considerations. The presence of underlying cardiovascular risk factors and established cardiovascular disease (CVD) may affect the severity and clinical management of patients with COVID-19. We conducted a review of the literature to summarize the cardiovascular pathophysiology, risk factors, clinical presentations, and treatment considerations of COVID-19 patients with underlying CVD. Angiotensin-converting enzyme 2 (ACE2) has been identified as a functional receptor for the SARS-CoV-2 virus, and it is associated with the cardiovascular system. Hypertension, diabetes, and CVD are the most common comorbidities in COVID-19 patients, and these factors have been associated with the progression and severity of COVID-19. However, elderly populations, who develop more-severe COVID-19 complications, are naturally exposed to these comorbidities, underscoring the possible confounding of age. Observational data support international cardiovascular societies' recommendations to not discontinue ACE inhibitor/angiotensin-receptor blocker therapy in patients with guideline indications for fear of the increased risk of SARS-CoV-2 infection, severe disease, or death. In addition to the cardiotoxicity of experimental antivirals and potential interactions of experimental therapies with cardiovascular drugs, several strategies for cardiovascular protection have been recommended in COVID-19 patients with underlying CVD. Troponin elevation is associated with increased risk of in-hospital mortality and adverse outcomes in patients with COVID-19. Cardiovascular care teams should have a high index of suspicion for fulminant myocarditis-like presentations being SARS-CoV-2 positive, and remain vigilant for cardiovascular complications in COVID-19 patients.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
| grok | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| opus | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".