Impact of peri‐operative frailty and operative stress on post‐discharge mortality, readmission and days at home in Medicare beneficiaries
Bibliographic record
Abstract
BACKGROUND: Understanding how patients' frailty and the physiological stress of surgical procedures affect postoperative outcomes may inform risk stratification of older patients undergoing surgery. The objective of the study was to examine the association of peri-operative frailty with mortality, 30-day readmission and days at home after non-cardiac surgical procedures of different physiological stress. METHODS: This retrospective study used Medicare claims data from a 7.125% random sample of Medicare fee-for-service beneficiaries from 2015 to 2019 who were aged ≥ 65 years and underwent non-cardiac surgical procedure listed in the Operative Stress Score categories. The exposure of the study was claims-based frailty index (robust, < 0.15; pre-frail, 0.15 to < 0.25; mildly frail, 0.25 to < 0.35; and moderate-to-severely frail, ≥ 0.35) with Operative Stress Score categories being 1, very low stress to 5, very high stress. The primary outcome was all-cause mortality at 30 days and 365 days after the surgical procedure. RESULTS: In total, 1,019,938 patients (mean (SD) age of 76.1 (7.3) years; 52.3% female; 16.8% frail) were included. The cumulative incidence of mortality generally increased with Operative Stress Score category, ranging from 5.0% (Operative Stress Score 2) to 24.9% (Operative Stress Score 4) at 365 days. Within each category, increasing frailty was associated with mortality at 30 days (hazard ratio comparing moderate-to-severe frailty vs. robust ranged from 1.59-3.91) and at 365 days (hazard ratio 1.30-4.04). The variation in postoperative outcomes by patients' frailty level was much greater than the variation by the operative stress category. CONCLUSIONS: These results emphasise routine frailty screening before major and minor non-cardiac procedures and the need for greater clinician awareness of postoperative outcomes beyond 30 days in shared decision-making with older adults with frailty.
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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) | 0.000 | 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".