FP762REGULAR SCREENING VERSUS NO FURTHER SCREENING FOR ASYMPTOMATIC CORONARY ARTERY DISEASE IN WAITLISTED KIDNEY TRANSPLANT CANDIDATES: A MODELLED COST-EFFECTIVENESS ANALYSIS
Bibliographic record
Abstract
INTRODUCTION: Clinical practice guidelines recommend regular screening for asymptomatic coronary artery disease (CAD) in waitlisted kidney transplant candidates due to the high prevalence of cardiovascular disease in patients with end-stage kidney disease. However, the efficacy and cost-effectiveness of this practice have never been evaluated. A Canadian-Australasian randomised controlled trial of screening kidney transplant candidates for coronary artery disease (CARSK), ACTRN126160007364488 is being conducted to answer this question. METHODS: We constructed a pre-trial Markov microsimulation model to estimate the costs and health outcomes associated with regular screening compared with no further screening after waitlist entry. We also examined influential variables within the model to inform data collection during the definitive trial. We performed a literature review to obtain clinical, utility (preference-based quality-of-life) and cost inputs for our model. RESULTS: For a cohort of patients aged between 18 and 69, the incremental cost-effectiveness ratio (ICER) of no further screening was $11,122 gained per quality-adjusted life year (QALY) when compared with regular screening over a lifetime horizon. No further screening increased survival by an additional 0.49 life-years or 0.35 QALYs over regular screening. Influential variables included the prevalence of CAD and the cost of transplantation in the first year. Probabilistic sensitivity analysis combining individual variability and parameter uncertainty showed that 99% of the iterations were cost-effective below the commonly quoted willingness-to-pay threshold of $50,000 per QALY gained. CONCLUSIONS: Thus, no further screening for CAD after transplant waitlisting is likely to be cost-effective and may increase survival. Uncertainty around the ICER will be further reduced from detailed information on healthcare resource utilisation collected within the CARSK trial, especially costs incurred during the first year of transplantation.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".