Development of a Frailty Index from Routine Hospital Data in Perioperative and Critical Care
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Frailty is common in surgical and intensive care unit (ICU) populations, yet it is not routinely measured. Frailty indices are able to quantify this condition across a range of health deficits. We aimed to develop a frailty index (FI) from routinely collected hospital data in a surgical and ICU population. DESIGN: Prospective observational single-center cohort study. SETTING: Tertiary referral metropolitan Australian hospital. PARTICIPANTS: A total of 336 individuals aged 65 and older undergoing surgery or aged 50 and older admitted to the ICU. MEASUREMENTS: Routine admission health data were used to derive an FI comprising 36 health deficits. We examined the FI correlation with existing frailty tools (Clinical Frailty Scale [CFS] and Edmonton Frail Scale [EFS]) and assessed its predictive ability for negative outcomes including 30-day mortality. RESULTS: Median FI was .17 (interquartile range [IQR]) = .10-.24) for ICU patients and .17 (IQR = .11-.25) for surgical patients; maximum FI was .58, and 25% (95% confidence interval [CI] = 10.4-29.6) of patients overall were diagnosed with frailty (FI score ≥.25). Correlation was strong between the FI and the EFS: ρ = .76 (95% CI = .70-.83) for ICU patients and .71 (95% CI = .64-.78) for surgical patients, and the CFS was .77 (95% CI = .70-.84) for ICU patients and .72 (95% CI = .65-.79) for surgical patients. The FI had good discriminative ability for prediction of 30-day mortality in ICU patients (multivariate odds ratio for each increase in FI of .1 = 2.04 [95% CI = 1.19-3.48]), comparable with the performance of the Acute Physiology and Chronic Health Evaluation III score (ICU patients) and the Portsmouth Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity score (surgical patients). CONCLUSION: It is feasible to construct an FI from hospital admission data in a cohort of critically ill and surgical 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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".