Non-Sustained Ventricular Tachycardia as a Predictor of Sudden Cardiac Death in Patients with Left Ventricular Dysfunction: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Identifying patients at risk of sudden cardiac death (SCD) remains a challenge. AIM: To evaluate the performance of non-sustained ventricular tachycardia (NSVT) from 24 hour ambulatory electrocardiography as a predictor of SCD in patients with heart failure or non-ischaemic dilated cardiomyopathy with left ventricular systolic dysfunction (LVSD). METHODS AND RESULTS: Study search and selection were performed by independent reviewers using a validated strategy. Eleven prognostic studies with >100 patients with good quality data and multivariate analysis of predictors of SCD were included. Publication bias was evaluated by funnel plot with Kendall's tau b test. A summary ROC (sROC) curve was built to evaluate predictive performance of NSVT. There was threshold effect (Spearman's correlation between sensitivity and specificity=-0.818, p<0.01) which indicates that combining sensitivity and specificity was not appropriate. The area of 0.68+/-0.02 under the sROC curve indicates a statistically significant contribution of NSVT in the prediction of SCD. The true negative rate varied from 89 to 97%. Multivariate analysis and meta-regression suggested that the contribution of NSVT to risk stratification is independent of ejection fraction. CONCLUSIONS: Absence of NSVT indicated a low probability of SCD in patients with LVSD. A risk score including NSVT should be evaluated in prospective studies.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".