The clinical and economic impact of extended battery longevity of a substernal extravascular implantable cardioverter defibrillator
Bibliographic record
Abstract
INTRODUCTION: The extravascular implantable cardioverter defibrillator (EV ICD) has extended projected battery longevity compared to the subcutaneous implantable cardioverter defibrillator (S-ICD). This study used modeling to characterize the need for generator changes, long-term complications, and overall costs for both the EV ICD and S-ICD in healthcare systems of various countries. METHODS: Battery longevity data were modeled using a Markov model from averages reported in device labeling for the S-ICD and with engineering estimates based on real life usage from EV ICD Pivotal Study patient data to introduce variability. Clinical demographic data were derived from published literature. The primary outcomes were defined as the number of generator replacement surgeries, complications, and total healthcare system costs due to battery depletion over the expected lifetime of patients receiving EV ICD or S-ICD therapy. RESULTS: Average modeled battery longevity was determined to be 7.3 years for the S-ICD versus 11.8 years for the EV ICD. The probability of a complication after a replacement procedure was 1.4%, with an operative mortality rate of 0.02%. The use of EV ICD was associated with 1.4-1.6 fewer replacements on average over an expected patient lifetime as compared to S-ICD and a 24.3%-26.0% reduction in cost. A one-way sensitivity analysis of the model for the US healthcare system found that use of an EV ICD resulted in a reduction in replacement surgeries of greater than 1 (1.1-1.6) along with five-figure cost savings in all scenarios ($18 602-$40 948). CONCLUSION: The longer projected battery life of the EV ICD has the potential to meaningfully reduce long-term morbidity and healthcare resources related to generator changes from the perspective of multiple diverse healthcare systems.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".