Economic implications of adding a novel algorithm to optimize cardiac resynchronization therapy: rationale and design of economic analysis for the AdaptResponse trial
Bibliographic record
Abstract
AIMS: Although cardiac resynchronization therapy (CRT) has proven beneficial in several randomized trials, a subset of patients have limited clinical improvement. The AdaptivCRT algorithm provides automated selection between synchronized left ventricular or biventricular pacing with optimization of atrioventricular delays. The rationale and design of the economic analysis of the AdaptResponse clinical trial are described. RATIONALE: The costs associated with HF hospitalization are substantial and are compounded by a high rate of readmission. HF hospitalization payments range from $1,001 for Greece to $12,235 for US private insurance. When examining the breakdown of HF-related costs, it is clear that approximately 55% of the hospitalization costs are directly attributable to length of stay. Notably, the mean costs of a CRT patient in need of a HF-related hospitalization are currently estimated to be an average of $10,679. METHODS: The economic analysis of the AdaptResponse trial has two main objectives. The hospital provider objective seeks to test the hypothesis that AdaptivCRT reduces the incidence of all-cause re-admissions after a heart failure admission within 30 days of the index event. A negative binomial regression model will be used to estimate and compare the number of readmissions after an index HF hospitalization. The payer economic objective will assess cost-effectiveness of CRT devices with the AdaptivCRT algorithm relative to traditional CRT programming. This analysis will be conducted from a U.S. payer perspective. A decision analytic model comprised of a 6-month decision tree and a Markov model for long term extrapolation will be used to evaluate lifetime costs and benefits. CONCLUSION: AdaptivCRT may offer improvements over traditional device programming in patient outcomes. How the data from AdaptResponse will be used to demonstrate if these clinical benefits translate into substantial economic gains is herein described.
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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 | 0.112 | 0.161 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".