Is Defibrillation Testing Still Necessary? A Decision Analysis and Markov Model
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of defibrillation threshold (DFT) testing of implanted cardioverter-defibrillators (ICDs) on survival. BACKGROUND: DFT testing is generally performed during implantation of ICDs to assess sensing and termination of ventricular fibrillation. It is common clinical practice to defibrillate ventricular fibrillation twice at an output at least 10 J below the maximum output of the device, providing a 10 J safety margin. However, there are few data regarding impact of DFT testing on outcomes. METHODS: Decision analysis and Monte Carlo simulation were used to assess expected outcomes of DFT testing. Survival of a hypothetical cohort of patients was assessed according to two strategies-routine DFT testing at time of ICD implant versus no DFT testing. Assumptions in the model were varied over a range of reasonable values to assess outcomes under a variety of scenarios. RESULTS: Five-year survival with DFT and no-DFT strategies were similar at 59.72% and 59.36%, respectively. The results were not sensitive to changing risk estimates for arrhythmia incidence and safety margin. Results of the Monte Carlo simulation were qualitatively similar to the base case scenario and consistent with a small and nonsignificant survival advantage with routine DFT testing. CONCLUSIONS: The impact of DFT testing on 5-year survival in ICD patients, if it exists, is small. Survival appears higher with DFT testing as long as annual risk of lethal arrhythmia or the risk of a narrow safety margin is at least 5%, although the incremental benefit is marginal and 95% confidence intervals cross zero. A prospective randomized study of DFT testing in modern devices is warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".