Frailty in very old critically ill patients in Australia and New Zealand: a population‐based cohort study
Bibliographic record
Abstract
OBJECTIVE: To explore associations between frailty (Clinical Frailty Scale score of 5 or more) in very old patients in intensive care units (ICUs) and their clinical outcomes (mortality, discharge destination). DESIGN, SETTING AND PARTICIPANTS: Retrospective population cohort analysis of Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database data for all patients aged 80 years or more admitted to participating ICUs between 1 January 2017 and 31 December 2018. MAIN OUTCOME MEASURES: Primary outcome: in-hospital mortality; secondary outcomes: length of stay (hospital, ICU), re-admission to ICU during the same hospital admission, discharge destination (including new chronic care or nursing home admission). RESULTS: Frailty status data were available for 15 613 of 45 773 patients aged 80 years or more admitted to 178 ICUs (34%); 6203 of these patients (39.7%) were deemed frail. A smaller proportion of frail than non-frail patients were men (47% v 57%), the mean illness severity scores of frail patients were slightly higher than those of non-frail patients, and they were more frequently admitted from the emergency department (28% v 21%) or with sepsis (12% v 7%) or respiratory complications (16% v 12%). In-hospital mortality was higher for frail patients (17.6% v 8.2%; adjusted odds ratio [OR], 1.87 [95% CI, 1.65-2.11]). Median lengths of ICU and hospital stay were slightly longer for frail patients, and they were more frequently discharged to new nursing home or chronic care (4.9% v 2.8%; adjusted OR, 1.61 [95% CI, 1.34-1.95]). CONCLUSIONS: Many very old critically ill patients in Australia and New Zealand are frail, and frailty is associated with considerably poorer health outcomes. Routine screening of older ICU patients for frailty could improve outcome prediction and inform intensive care and community health care planning.
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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 | 0.002 |
| 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 | 0.001 |
| 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".