Association Between Acute Kidney Injury, Delirium, and Outcomes in Patients With Critical Illness: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: Acute kidney injury (AKI) and delirium are common complications of critical illness. However, relatively few studies have evaluated their relationship. We conducted a systematic synthesis and meta-analysis of existing evidence to clarify this association in critically ill patients. DATA SOURCES: A comprehensive search was conducted across MEDLINE, Embase, CINAHL, Scopus, Web of Science, and Cochrane Library for publications reporting both AKI and delirium in ICUs patients from January 2000 to January 2025. STUDY SELECTION: AKI was defined according to serum creatinine or urine output criteria based on the contemporary definitions used in the individual studies. The primary outcome was the proportion of critically ill patients with AKI who developed delirium. Secondary outcomes included mortality and health service utilization. DATA EXTRACTION: Pooled meta-analyses were summarized as effect sizes in proportions, risk ratios (RRs), odds ratios, or weighted mean differences (WMDs) using a random-effects model. The certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation approach. DATA SYNTHESIS: Eighteen observational studies comprising 158,694 patients were included. Overall study quality was moderate. The pooled proportion of delirium among patients with AKI was 32% (95% CI, 18-47%). Delirium was associated with higher mortality (RR, 2.36; 95% CI, 1.61-3.47; moderate certainty), greater renal replacement therapy use (RR, 3.12; 95% CI, 1.89-5.15; moderate certainty), longer ICU stays (WMD, 3.54 d; 95% CI, 1.20-5.87 d; moderate certainty), and longer hospital stays (WMD, 4.78 d; 95% CI, 3.48-6.09 d; moderate certainty) compared with patients with AKI not experiencing delirium. CONCLUSIONS: Delirium is common among critically ill patients with AKI and is associated with worse outcomes and greater health resource use.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.045 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".