Combining Shock Reduction Strategies to Enhance ICD Therapy: A Role for Computer Modeling
Bibliographic record
Abstract
OBJECTIVES: To develop a computer model to test shock reduction strategies such as antitachycardia pacing and shock withholding for supraventricular rhythms, oversensing, and nonsustained ventricular tachycardia. BACKGROUND: While the implantable cardioverter defibrillator (ICD) can reduce mortality, inappropriate ICD shocks remain a limitation. Randomized trials provide evidence of efficacy, but they are not always practical. Computer models provide an alternative approach, and are particularly useful when evaluating multiple interventions. METHODS: A computer model was developed using clinical data and validated in a large ICD data set (EMPIRIC). After validation, the model was applied to 736 adjudicated clinical episodes from the ICD arm of Sudden Cardiac Death Heart Failure Trial (SCD-HeFT). RESULTS: The shock reduction strategies hypothetically reduced the number of VT/VF shocked episodes in SCD-HeFT by an estimated 59% (from 952 observed to 395 modeled shocks, probability of >0.999) at detection duration settings (18 of 24 intervals). The percentage of patients experiencing inappropriate shocks over 5 years was decreased by 15% (23.5-8.4%), and the number of shocks for non-VT/VF episodes was decreased from 423 to 77 (82% reduction). The percentage of patients receiving shocks for VT/VF was reduced from 30.7% (SCD-HeFT) to 26.1% with the addition of ATP. Extended detection (24 of 32 or 30 of 40 intervals) showed modest additional improvement compared to 18 of 24 intervals. CONCLUSION: Computer modeling is able to predict the results of a known clinical trial and demonstrate that shock reduction strategies have the potential to significantly reduce inappropriate and unnecessary ICD shocks versus the mandated programming used in SCD-HeFT.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".