Selection and Receipt of Kidney Replacement in Critically Ill Older Patients with AKI
Bibliographic record
Abstract
Visual Abstract Export Background and objectives Older patients in the intensive care unit are at greater risk of AKI; however, use of kidney replacement therapy in this population is poorly characterized. We describe the triggers and outcomes associated with kidney replacement therapy in older patients with AKI in the intensive care unit. Design, setting, participants, & measurements Our study was a prospective cohort study in 16 Canadian hospitals from September 2013 to November 2015. Patients were ≥65 years old, were critically ill, and had severe AKI; exclusion criteria were urgent kidney replacement therapy for a toxin and ESKD. We recorded triggers for kidney replacement therapy (primary exposure), reasons for not receiving kidney replacement therapy, 90-day mortality (primary outcome), and kidney recovery. Results Of 499 patients, mean (SD) age was 75 (7) years old, Charlson comorbidity score was 3.0 (2.3), and median (interquartile range) Clinical Frailty Scale score was 4 (3–5). Most were receiving mechanical ventilation (64%; n=319) and vasoactive support (63%; n=314). Clinicians were willing to offer kidney replacement therapy to 361 (72%) patients, and 229 (46%) received kidney replacement therapy. Main triggers for kidney replacement therapy were oligoanuria, fluid overload, and acidemia, whereas main reasons for not receiving therapy were anticipated recovery (67%; n=181) and therapy not consistent with patient preferences for care (24%; n=66). Ninety-day mortality was similar in patients who did and did not receive kidney replacement therapy (50% versus 51%; adjusted hazard ratio, 0.78; 95% confidence interval, 0.58 to 1.06); however, decisions to offer kidney replacement therapy varied significantly by patient mix, acuity, and perceived benefit. There were no differences in health-related quality of life or rehospitalization among survivors. Conclusions Most older, critically ill patients with severe AKI were perceived as candidates for kidney replacement therapy, and approximately one half received therapy. Both willingness to offer kidney replacement therapy and reasons for not starting showed heterogeneity due to a range in patient-specific factors and clinician perceptions of benefit.
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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 | 0.001 | 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) | 0.002 | 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".