Cardiac Manifestations in Patients with COVID-19: A Scoping Review
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19), commonly affects the lungs, but the involvement of other organs, particularly the heart, is highly prevalent as has been reported in several studies. The overall aim of this review was to provide an in-depth description of the available literature related to the cardiac system and COVID-19 infection. It focuses on type and the frequency of cardiac manifestations, clinical parameters and cardiac biomarkers that support the prognosis of COVID-19 patients, and the cardiac adverse events and outcomes related to pharmacotherapy. Methods: A scoping review was conducted searching Embase, PubMed, Epistomonikos, Medrxiv, BioRxiv databases, up to November 2020, for systematic reviews relevant to cardiac manifestations in adult COVID-19 patients. Relevant articles were screened and extracted to summarize key outcomes and findings. Results: A total of 63 systematic reviews met the inclusion criteria. The overall frequency of acute cardiac injury ranged from 15% to 33% in the reporting studies. The main cardiac complications were arrhythmias (3.1% to 6.9% in non-severe patients, 33.0% to 48.0% in severe disease), acute coronary syndromes (6% to 33% in severe disease), and myocarditis. Most studies found no association with the use of Renin-angiotensin-aldosterone system inhibitors (RAASI) with COVID-19 outcomes such as susceptibility to infection, hospitalization, severity, and mortality. Conclusion: This study provided an overview of the several cardiac complications associated with Covid-19. Cardiac injury, arrhythmias, myocarditis, cardiac failure, and acute coronary syndrome, are prevalent and clinically significant and associated with COVID-19 disease severity and mortality. Other studies are needed to clearly identify what is the part of viral heart infection and what is the part of cardiac injury secondary to acute respiratory failure and inflammation. In the therapeutic field, these systematic reviews gave heterogenous results. This underlines the importance of randomized trials to determine the right therapeutic approach.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.012 |
| 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".