A frailty index for predicting mortality, healthcare resources and costs of cardiac procedure patients
Bibliographic record
Abstract
Background: Canada’s aging population is growing steadily. Older age is associated with frailty, defined as the accumulation of age and illness-related deficits, which can be measured using a frailty index. Objectives: 1) To develop a non-weighted and weighted frailty index for cardiac procedure patients and to use these to predict mortality. 2) To develop a weighted frailty index for cardiac procedures to predict healthcare utilization and costs. For objectives 1 and 2, the created frailty indices were compared with an existing frailty index. 3) To explore healthcare provider and hospital administrator perspectives on the clinical usefulness and feasibility of implementing a frailty index for cardiac procedures. Methods: This was a retrospective cohort study involving cardiac procedure patients using healthcare administrative data followed by key informant interviews with providers and hospital administrators using reflexive thematic analysis. Results: 64,822 open-heart surgery patients and 2,024 transcatheter aortic valve implantation (TAVI) patients were included in the retrospective cohort studies. For predicting mortality, the multivariable regression model containing the weighted frailty index showed a small improvement in the open-heart surgery cohort versus the non-weighted index model (concordance-statistic=0.80 [95% Confidence Interval (CI): 0.78,0.81] versus 0.79 [95% CI: 0.77,0.80] respectively). In the TAVI cohort, the pre-existing frailty index model had the greatest prediction capability. For healthcare costs, the weighted frailty index model demonstrated the highest predictive abilities in both cohorts. For length of initial hospital stay, the weighted frailty index model had the highest prediction abilities in the open-heart surgery cohort and similar capabilities to the model containing the pre-existing frailty index in the TAVI cohort. Key informant interviews revealed four themes regarding a cardiac procedure-specific frailty index: (1) potential uses; (2) feasibility for prehabilitation; (3) logistics of implementation; (4) future implementation. Subthemes included surgical candidacy and electronic incorporation of the index into health information systems. Conclusion: Researchers may choose the pre-existing frailty index for predicting TAVI mortality or the weighted frailty index when predicting healthcare costs and resources. A cardiac procedure-specific frailty index could be useful to healthcare providers when determining surgical candidacy and optimizing preoperative care, especially if electronically integrated into health information 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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".