Incidence and predictors of sudden cardiac death after heart transplantation: A systematic review and meta‐analysis
Bibliographic record
Abstract
PURPOSE: Sudden cardiac death (SCD) is an important post-transplant problem being responsible for ~10% of deaths. We conducted a systematic review and meta-analysis to evaluate incidence and predictors of post-heart transplant SCD and the use of implantable cardiac defibrillator (ICD). METHODS: Citations were identified in electronic databases and references of included studies. Observational studies on adults reporting on incidence and predictors of post-transplant SCD and ICD use were selected. We meta-analyzed SCD in person-years using random effects models. We qualitatively summarized predictors. RESULTS: This study includes 55 studies encompassing 47 901 recipients. The pooled incidence rate of SCD was 1.30 per 100 person-years (95% CI: 1.08-1.52). Cardiac allograft vasculopathy (CAV) was associated with higher SCD risk (2.40 per 100 patient-years, 95% CI: 1.46-3.34). Independent predictors of SCD identified by two moderate-quality studies were older donor age, younger recipient age, non-Caucasian race, reduced left ventricular ejection fraction, rejection, infection, and cancer. Authors rarely reported on ICD use. CONCLUSION: This meta-analysis found that post-transplant SCD risk in heart transplant recipients is higher than that in the general population. CAV was associated with increased SCD risk. Observational studies reporting on absolute risk of SCD are needed to better identify populations at a clinically significant increased risk.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".