Arrhythmias and electrocardiographic findings in Coronavirus disease 2019: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) primarily causes lung infection, but recent studies have shown that cardiac involvement is associated with a worse prognosis. OBJECTIVES: We conducted a systematic review and meta-analysis to examine the prevalence of cardiac arrhythmias detected by the electrocardiogram and their relationships with adverse outcomes in patients with COVID-19. METHODS: PubMed and Google were searched for studies that reported on cardiac arrhythmias and/or examined the relationship between arrhythmias and adverse outcomes. RESULTS: Thirty studies with 12,713 participants were included in the systematic review, and 28 studies (n = 12,499) in the meta-analysis. The mean age was 61.3 ± 16.8 years; 39.3% were female. In 25 studies with 7578 patients, the overall prevalence of cardiac arrhythmias was 10.3% (95% confidence interval [CI]: 8.4%-12.3%). The most common arrhythmias documented during hospitalization were supraventricular arrhythmias (6.2%, 95% CI: 4.4%-8.1%) followed by ventricular arrhythmias (2.5%, 95% CI: 1.8%-3.1%). The incidence of cardiac arrhythmias was higher among critically ill patients (relative risk [RR]: 12.1, 95% CI: 8.5-17.3) and among non-survivors (RR: 3.8, 95%, CI: 1.7-8.7). Eight studies reported changes in the QT interval. The prevalence of QTc > 500 ms was 12.3% (95% CI: 6.9%-17.8%). ST-segment deviation was reported in eight studies, with a pooled estimate of 8.7% (95% CI: 7.3% to 10.0%). CONCLUSION: Our meta-analysis showed that QTc prolongation, ST-segment deviation, and various other cardiac arrhythmias were observed in patients hospitalized with COVID-19. The presence of cardiac arrhythmias was associated with a worse prognosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".