Frailty and body composition predict adverse outcomes after emergency general surgery admission: a multicentre observational cohort study
Bibliographic record
Abstract
Introduction Emergency surgical admissions represent the most unwell patients admitted to any hospital. Frailty and body composition independently identify risk of adverse outcomes but are seldom combined to predict outcomes in emergency patients. We aim to determine the relationships between frailty, body composition analyses (BCA) and mortality in an undifferentiated emergency general surgical patient population. Method A prospective, multicentre observational cohort study of patients admitted with emergency surgical pathology was conducted in eight hospitals. BCA were performed at L3 vertebrae using computed tomography images to quantify sarcopenia and myosteatosis. Sex-specific BCA cut-off values were determined by our previous study. Reported Edmonton Frail Scale (REFS) values ≥8 identified frailty. The primary outcomes were all-cause 30-day and 1-year mortality. Multivariable logistic regression was utilised to explore predictive relationships between frailty, BCA, mortality and independent discharge. Results A total of 194 patients were included; 24% were frail, 25% were sarcopenic and 23% myosteatotic. Some 61% of patients underwent an emergency laparotomy. Frail patients were more likely to be sarcopenic (20.4% vs 40.4%; p = 0.011) and myosteatotic (27.2% vs 51.1%; p = 0.004). Thirty-day and 1-year mortality was 5.2% and 15.5%, respectively; 30-day mortality was two times higher in the frail group (4.1% vs 8.5%; p = 0.414), and three times higher at 1 year (10.2% vs 31.9%; p = 0.001). Age (odds ratio [OR] 1.06; p = 0.001), sarcopenia (OR 2.88; p = 0.047) and frailty (OR 4.13; p = 0.001) were associated with 1-year mortality. Only 55.3% of frail patients were discharged home independently compared with 88.4% non-frail patients (p < 0.001). One-year mortality was greater in those with frailty and/or BCA abnormalities than in those without (28.8% vs 9.6%; p = 0.003). Conclusion Frailty, sarcopenia and myosteatosis contribute significantly to adverse outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".