Prospective Comparison of Preoperative Predictive Performance Between 3 Leading Frailty Instruments
Bibliographic record
Abstract
BACKGROUND: Guidelines recommend routine preoperative frailty assessment for older people. However, the degree to which frailty instruments improve predictive accuracy when added to traditional risk factors is poorly described. Our objective was to measure the accuracy gained in predicting outcomes important to older patients when adding the Clinical Frailty Scale (CFS), Fried Phenotype (FP), or Frailty Index (FI) to traditional risk factors. METHODS: This was an analysis of a multicenter prospective cohort of elective noncardiac surgery patients ≥65 years of age. Each frailty instrument was prospectively collected. The added predictive performance of each frailty instrument beyond the baseline model (age, sex, American Society of Anesthesiologists' score, procedural risk) was estimated using likelihood ratio test, discrimination, calibration, explained variance, and reclassification. Outcomes analyzed included death or new disability, prolonged length of stay (LoS, >75th percentile), and adverse discharge (death or non-home discharge). RESULTS: We included 645 participants (mean age, 74 [standard deviation, 6]); 72 (11.2%) participants died or experienced a new disability, 164 (25.4%) had prolonged LoS, and 60 (9.2%) had adverse discharge. Compared to the baseline model predicting death or new disability (area under the curve [AUC], 0.67; R, 0.08, good calibration), prolonged LoS (AUC, 0.73; R, 0.18, good calibration), and adverse discharge (AUC, 0.78; R, 0.16, poor calibration), the CFS improved fit per the likelihood ratio test (P < .02 for death or new disability, <.001 for LoS, <.001 for discharge), discrimination (AUC = 0.71 for death or new disability, 0.76 for LoS, 0.82 for discharge), calibration (good for death or new disability, LoS, and discharge), explained variance (R = 0.11 for death or new disability, 0.22 for LoS, 0.25 for discharge), and reclassification (appropriate directional reclassification) for all outcomes. The FP improved discrimination and R for all outcomes, but to a lesser degree than the CFS. The FI improved discrimination for death or new disability and R for all outcomes, but to a lesser degree than the CFS and the FP. These results were consistent in internal validation. CONCLUSIONS: Frailty instruments provide meaningful increases in accuracy when predicting postoperative outcomes for older people. Compared to the FP and FI, the CFS appears to improve all measures of predictive performance to the greatest extent and across outcomes. Combined with previous research demonstrating that the CFS is easy to use and requires less time than the FP, clinicians should consider its use in preoperative practice.
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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 | 0.000 |
| 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".