Derivation and Validation of a Generalizable Preoperative Frailty Index Using Population-based Health Administrative Data
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a preoperative frailty index (pFI) for use in population-based health administrative (HA) data. SUMMARY BACKGROUND DATA: Frailty is a robust predictor of adverse postoperative outcomes. Population-level frailty measures used in surgical studies have significant methodological limitations. Frailty indices (FIs) are a well-defined approach to measuring frailty with well-described methods for development and evaluation. An appropriate preoperative FI in HA data has not been derived or evaluated. METHODS: Retrospective cohort study using linked HA data in Canada. We identified people >65 years (2002-2015) who had major elective or emergency surgery. Standardized methods were used to construct a 30-variable pFI. Unadjusted and multilevel, multivariable adjusted models were used to measure the association of the pFI with 1-year mortality and institutional discharge. Elective patients were the derivation cohort, emergency patients were the validation cohort. Prespecified sensitivity analyses were performed. RESULTS: We identified 415,704 elective, and 95,581 emergency patients. The elective 1-year mortality rate was 4.7%. Thirty percent of population-level deaths occurred in people with frailty. Every 0.1-unit increase in the pFI was associated with a 2.20-fold increase in the adjusted odds of mortality (95% CI 2.15-2.26; c-statistic 0.81), and a 1.70-fold increase in institutional discharge (95% CI 1.59-1.80; c-statistic 0.71). pFI performance was similar in emergency patients, and was robust to changes in index composition. CONCLUSIONS: A preoperative FI derived from HA data is a robust method to measure frailty in elective and emergency patients. Generalizable FIs should be considered a standard approach to population-level study of surgical 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 | 0.033 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".