Frailty status among older critically ill patients with severe acute kidney injury
Bibliographic record
Abstract
BACKGROUND: Frailty status among critically ill patients with acute kidney injury (AKI) is not well described despite its importance for prognostication and informed decision-making on life-sustaining therapies. In this study, we aim to describe the epidemiology of frailty in a cohort of older critically ill patients with severe AKI, the outcomes of patients with pre-existing frailty before AKI and the factors associated with a worsening frailty status among survivors. METHODS: This was a secondary analysis of a prospective multicentre observational study that enrolled older (age > 65 years) critically ill patients with AKI. The clinical frailty scale (CFS) score was captured at baseline, at 6 months and at 12 months among survivors. Frailty was defined as a CFS score of ≥ 5. Demographic, clinical and physiological variables associated with frailty as baseline were described. Multivariable Cox proportional hazard models were constructed to describe the association between frailty and 90-day mortality. Demographic and clinical factors associated with worsening frailty status at 6 months and 12 months were described using multivariable logistic regression analysis and multistate models. RESULTS: Among the 462 patients in our cohort, median (IQR) baseline CFS score was 4 (3-5), with 141 (31%) patients considered frail. Pre-existing frailty was associated with greater hazard of 90-day mortality (59% (n = 83) for frail vs. 31% (n = 100) for non-frail; adjusted hazards ratio [HR] 1.49; 95% CI 1.11-2.01, p = 0.008). At 6 months, 68 patients (28% of survivors) were frail. Of these, 57% (n = 39) were not classified as frail at baseline. Between 6 and 12 months of follow-up, 9 (4% of survivors) patients transitioned from a frail to a not frail status while 10 (4% of survivors) patients became frail and 11 (5% of survivors) patients died. In multivariable analysis, age was independently associated with worsening CFS score from baseline to 6 months (adjusted odds ratio [OR] 1.08; 95% CI 1.03-1.13, p = 0.003). CONCLUSIONS: Pre-existing frailty is an independent risk factor for mortality among older critically ill patients with severe AKI. A substantial proportion of survivors experience declining function and worsened frailty status within one year.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 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".