Accelerated biological aging and postoperative acute kidney injury in surgical patients: a retrospective multicenter cohort analysis of 94 006 cases
Bibliographic record
Abstract
BACKGROUND: While aging is closely associated with increased risk of acute kidney injury (AKI), chronological age often fails to capture the heterogeneity in biological decline among individuals. In contrast, biological age emerges as a more accurate indicator of the aging process. However, the association between accelerated biological aging and postoperative AKI remains unexplored. Therefore, this study aimed to assess the association between accelerated biological aging and postoperative AKI. METHODS: We conducted a retrospective cohort study from 2015 to 2023 at three academic medical centers in China, including inpatients who underwent surgery under general anesthesia. Biological age was measured using Phenotypic Age (PhenoAge) approach. Biological aging was calculated by Phenotypic Age acceleration (PhenoAgeAccel). The primary endpoint was AKI within 7 days after surgery. Secondary endpoints included AKI stage 2 or 3 (AKI 2 +) and length of stay (LOS). RESULTS: Among 94,006 patients (median age 62 (51-71) years, 44.0% female), 37.7% were biologically older. The incidence of AKI was 1.62 per 100 person-days. After adjustment, accelerated biological aging was significantly associated with increased risk of AKI (adjusted hazard ratio (aHR) 1.50, 95% confidence interval (CI) 1.42-1.60), AKI 2 + (aHR 2.27, 95% CI 2.06-2.50), and prolonged LOS (adjusted coefficient 1.30, 95% CI 1.06-1.55). Dose-response relationship analyses revealed a monotonic non-linear positive association between PhenoAgeAccel and the risk of AKI. DISCUSSION: Accelerated biological aging may serve as an independent risk factor for postoperative AKI, AKI 2 +, and prolonged LOS, highlighting its potential as a target for preoperative risk stratification and intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".