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 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".