A Bayesian Comparison of Frailty Instruments in Noncardiac Surgery: A Cohort Study
Bibliographic record
Abstract
BACKGROUND: Frailty-a multidimensional syndrome related to age- and disease-related deficits-is a key risk factor for older surgical patients. However, it is unknown which frailty instrument most accurately predicts postoperative outcomes. Our objectives were to quantify the probability of association and relative predictive performance of 2 frailty instruments (ie, the risk analysis index-administrative [RAI-A] and 5-item modified frailty index [mFI-5]) with postoperative outcomes in National Surgical Quality Improvement Program (NSQIP) data. METHODS: Retrospective cohort study using Bayesian analysis of NSQIP hospitals. Adults having inpatient small or large bowel surgery 2010-2015 (derivation cohort) or intermediate to high risk mixed noncardiac surgery in 2016 (validation cohort) had preoperative frailty assigned using 2 unique approaches (RAI-A and mFI-5). Probabilities of association were calculated based on posterior distributions and relative predictive performance using posterior predictive distributions and Bayes factors for 30-day mortality (primary outcome) and serious complications (secondary outcome). RESULTS: Of 50,630 participants, 7630 (14.0%) died and 19,545 (38.6%) had a serious complication. Without adjustment, the RAI-A and mFI-5 had >99% probability being associated with mortality with a ≥2.0 odds ratio (ie, large effect size). After adjustment for NSQIP risk calculator variables, only the RAI-A had ≥95% probability of a nonzero association with mortality. Similar results arose when predicting postoperative complications. The RAI-A provided better predictive accuracy for mortality than the mFI-5 (minimum Bayes factor 3.25 × 1014), and only the RAI-A improved predictive accuracy beyond that of the NSQIP risk calculator (minimum Bayes factor = 4.27 × 1013). Results were consistent in leave-one-out cross-validation. CONCLUSIONS: Translation of frailty-related findings from research and quality improvement studies to clinical care and surgical planning will be aided by a consistent approach to measuring frailty with a multidimensional instrument like RAI-A, which appears to be superior to the mFI-5 when predicting outcomes for inpatient noncardiac surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".