Measuring the Predictive Accuracy of Preoperative Clinical Frailty Instruments Applied to Electronic Health Data in Older Patients Having Emergency General Surgery
Bibliographic record
Abstract
OBJECTIVE: To compare predictive accuracy of frailty instruments operationalizable in electronic data for prognosticating outcomes among older adults undergoing emergency general surgery (EGS). BACKGROUND: Older patients undergoing EGS are at higher risk of perioperative morbidity and mortality. Preoperative frailty is a common and strong perioperative risk factor in this population. Despite this, existing barriers preclude routine preoperative frailty assessment. METHODS: We conducted a retrospective cohort study of adults above 65 undergoing EGS from 2012 to 2018 using Institute for Clinical Evaluative Sciences (ICES) provincial healthcare data in Ontario, Canada. We compared 4 frailty instruments: Frailty Index (FI), Hospital Frailty Risk Score (HFRS), Risk Analysis Index-Administrative (RAI), ACG Frailty-defining diagnoses indicator (ACG). We compared predictive accuracy beyond baseline risk models (age, sex, American Society of Anesthesiologists' score, procedural risk). Predictive performance was measured using discrimination, calibration, explained variance, net reclassification index and Brier score (binary outcomes); using explained variance, root mean squared error and mean absolute prediction error (continuous outcomes). Primary outcome was 30-day mortality. Secondary outcomes were 365-day mortality, nonhome discharge, days alive at home, length of stay, and 30-day and 365-day health systems cost. RESULTS: A total of 121,095 EGS patients met inclusion criteria. Of these, 11,422 (9.4%) experienced death 30 days postoperatively. Addition of FI, HFRS, and RAI to the baseline model led to improved discrimination, net reclassification index, and R2 ; RAI demonstrated the largest improvements. CONCLUSIONS: Adding 4 frailty instruments to typically assessed preoperative risk factors demonstrated strong predictive performance in accurately prognosticating perioperative outcomes. These findings can be considered in developing automated risk stratification systems among older EGS patients.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".