History of COVID-19 as a Risk Factor for Cardiac Arrhythmias: A Case-Control Study
Bibliographic record
Abstract
Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was responsible for the coronavirus disease 2019 (COVID-19) pandemic and generated high morbidity and mortality rates worldwide, as well as several sequelae that persist and need to be evaluated. The aim of this study was to evaluate the association between a history of COVID-19 infection and the occurrence of cardiac arrhythmias in outpatients from a private clinic in Arequipa. Methods: We conducted a retrospective, analytical, unmatched case-control study in a private cardiology clinic in Arequipa, Peru. A total of 252 adult patients who underwent 24-h Holter monitoring between October and December 2023 were included. Cases were defined as patients with documented cardiac arrhythmias; controls had no arrhythmic findings. The main exposure was a confirmed history of COVID-19. Age, sex, and additional Holter findings were also analyzed. Logistic regression was used to estimate crude and adjusted odds ratios (ORs) with 95% confidence intervals (CIs), adjusting for age and sex. Results: Of the total sample, 68 patients were classified as cases and 184 as controls. A history of COVID-19 was more frequent among cases (70.6%) than among controls (50.5%) (P = 0.004). In unadjusted analysis, patients with prior COVID-19 had more than twice the odds of presenting arrhythmias (OR: 2.35; 95% CI: 1.29 - 4.26; P = 0.005). After adjusting for age and sex, the association remained statistically significant (OR: 2.12; 95% CI: 1.10 - 4.11; P = 0.025). Conclusion: A prior history of COVID-19 was significantly associated with increased odds of cardiac arrhythmias. These findings highlight the importance of structured cardiac evaluation in patients with prior SARS-CoV-2 infection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".