The Impact of Preoperative Frailty on the Clinical and Cost Outcomes of Adult Cardiac Surgery in Alberta, Canada: A Cohort Study
Bibliographic record
Abstract
Background There is limited information about the impact of frailty on public payer costs in cardiac surgery. This study aimed to determine quality-adjusted life-years (QALYs) and costs associated with preoperative frailty in patients referred for cardiac surgery. Methods We retrospectively compared costs of frailty in a cohort of 529 patients aged ≥ 50 years who were referred for nonemergent cardiac surgery in Alberta. Patients were screened preoperatively for frailty, defined as a score of 5 or greater on the Clinical Frailty Scale. The primary outcome measure was public payer costs attributable to frailty, calculated in a difference-in-difference (DID) model. Results The prevalence of frailty was 10% (n = 51; 95% confidence interval [CI], 7%-12%). Median (interquartile range) costs for frail patients were higher in the first year postsurgery ($200,709 [$146,177-$486,852] vs $147,730 [$100,674-$177,025]; P < 0.001) compared to nonfrail; the difference-in-difference attributable cost of frailty was $57,836 (95% CI, $–28,608-$144,280). At 1 year, frail patients had fewer QALYs realized compared to nonfrail patients (0.71 [0.57-0.77] vs 0.82 [0.75-0.86], P < 0.001), whereas QALYs gained were similar (0.02 [–0.02-0.05] vs 0.02 [0.00–0.04], P = 0.58, median difference 0.003 [95% CI, –0.01-0.02]) in frail and nonfrail patients. Conclusions Frailty screening identified a population with greater impairment in quality-of-life and greater healthcare costs. Costs attributable to frailty represent opportunity costs that should be considered in future cardiac surgical services planning in the context of our aging population and the growing prevalence of frailty.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".