Baseline Frailty as a Predictor of Survival After Critical Care
Bibliographic record
Abstract
BACKGROUND: The extent to which the degree of baseline frailty, as measured using standardized multidimensional health assessments before hospital admission, predicts survival among older adults after admission to an ICU remains unclear. RESEARCH QUESTION: Is baseline frailty an independent predictor of survival among older adults receiving care in an ICU? STUDY DESIGN AND METHODS: Retrospective cohort study of community-dwelling older adults (age, ≥ 65 years) receiving public home services who were admitted to any ICU in Ontario, Canada, between April 1, 2009, and March 31, 2015. All individuals underwent an interRAI Resident Assessment Instrument-Home Care (RAI-HC) assessment completed within 180 days of ICU admission. These assessments were linked to hospital discharge abstract records. Patients were categorized using frailty measures each calculated from the RAI-HC: a classification tree version of the Clinical Frailty Scale; the Frailty Index-Acute Care; and the Changes in Health, End-Stage Disease, Signs, and Symptoms Scale. One-year survival models were used to compare their performance. Patients were stratified based on the receipt of mechanical ventilation in the ICU. RESULTS: Of 24,499 individuals admitted to an ICU within 180 days of a RAI-HC assessment, 26.4% (n = 6,467) received mechanical ventilation. Overall, 43.0% (95% CI, 42.4%-43.6%) survived 365 days after ICU admission. In general, among the overall cohort and both mechanical ventilation subgroups, mortality hazards increased with the severity of baseline frailty. Models predicting survival 30, 90, and 365 days after admission to an ICU that adjusted for one of the frailty measures were more discriminant than reference models that adjusted only for age, sex, major clinical category, and area income quintile. INTERPRETATION: Severity of baseline frailty is independently associated with survival after ICU admission and should be considered when determining goals of care and treatment plans for people with critical illness.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".