Prevalence of arrhythmia-induced cardiomyopathy: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Arrhythmia-induced cardiomyopathy (AiCM) is a reversible cause of left ventricular systolic dysfunction (LVSD) that arises from sustained cardiac arrhythmias. Despite its clinical significance, AiCM has traditionally been considered a rare entity, and its prevalence remains unknown. OBJECTIVE: This study aimed to estimate the prevalence of AiCM across various patient populations, assess the impact of baseline characteristics on AiCM development, and critically evaluate the diagnostic criteria used in clinical studies. METHODS: A systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, with searches performed in PubMed, MEDLINE, and Embase. Studies were selected based on predefined criteria, and quality was evaluated using the Newcastle-Ottawa scale and the Risk of Bias in Non-randomized Studies of Interventions version 2. Meta-regression was used to analyze the factors influencing AiCM prevalence. RESULTS: From 1149 abstracts, 26 studies involving 7331 patients (mean age 58 years; 26% women; mean left ventricular ejection fraction 46%) were included. The overall prevalence of AiCM was 8%, with a significantly higher prevalence (59%) observed in cohorts of patients with LVSD. AiCM prevalence varied across studies depending on the type of arrhythmia and the diagnostic criteria applied. Multivariable meta-regression identified significant associations between AiCM prevalence and mean left ventricular ejection fraction (odds ratio 0.94; 95% confidence interval 0.90-0.98; P = .002) and patient age (odds ratio 1.15; 95% confidence interval 1.02-1.30; P = .03). Patients with AiCM had consistently higher baseline heart rates than control groups (standardized mean difference random effects model 0.35 [0.09-0.61]; P = .01). CONCLUSION: AiCM is the most frequent etiology of LVSD in patients with arrhythmias. These findings highlight the need for standardized diagnostic criteria to ensure appropriate management.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.043 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".