Abstract 13261: Determining the Cost-Benefit Yields of Participating in a Cardiac Rehabilitation Program
Bibliographic record
Abstract
Background and Objective: Regular participation in cardiac rehabilitation (CR) has been consistently shown to improve survival prognosis for patients suffering from a recent cardiovascular episode. No study has quantified the thresholds of baseline risk/behavioral attrition beyond which the cost-benefits of CR become economically unattractive. We aimed to determine how the cost-benefits associated with CR vary across baseline risk and behavioral attrition patterns amongst an actual population of patients participating in an outpatient CR program. Methods: Data was obtained from 11,998 consecutive patient referrals to Toronto Rehab Cardiac Rehabilitation and Secondary Prevention Program (1995 to 2010). Baseline risk was defined as the probability of death/hospitalization at 2 years following program termination and behavioral attrition as the probability of program drop-out. We assumed a fixed cost of $1500 per program and a fixed program efficacy of 20% reduction in death/hospitalization. Multiple logistic regression models were used to predict the number of deaths/hospitalizations avoided per 1000 patients treated. Results: Regression models for baseline risk and baseline behavioral attrition produced C-statistics of 0.66 and 0.85, respectively. Increasing age, type 2 diabetes were found to be concordant predictors of CR cost-benefit and higher baseline functional capacity as a discordant predictor. The correlation between program drop out risk and baseline risk was poor (r=0.22, p<0.001). There was a 13% increased risk of predicted dropout for each quartile of program cost to yield (RR 0.87, 95% CI: 0.87, 0.89), while for each 10% baseline risk increase, RR was found to be 1.36 (95% CI: 1.34, 1.37). For both risk factors as predictors of improved cost-benefit yield ($1000 cost per adverse-event avoided) there was a 21% higher likelihood of the program being economically attractive for each 10% increase in behavioral attrition risk (RR 1.21, 95% CI: 1.207, 1.213). Conclusion: Baseline risk and behavioral attrition are important determinants of CR effectiveness and underscores the potential utility of risk stratification and patient baseline/behavioral attrition risk thresholds to estimate the programmatic benefit of CR programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.029 |
| 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) | 0.003 | 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".