Redefining urine output thresholds for acute kidney injury criteria in critically Ill patients: a derivation and validation study

Abstract

Introduction: The current definition of acute kidney injury (AKI) includes increased serum creatinine (sCr) concentration and decreased urinary output (UO). Recent studies suggest that the standard UO threshold of 0.5 ml/kg/h may be suboptimal. This study aimed to develop and validate a novel UO-based AKI classification system that improves mortality prediction and patient stratification.

Methods: Data were obtained from the MIMIC-IV and eICU databases. The development process included (1) evaluating UO as a continuous variable over 3-, 6-, 12-, and 24-h periods; (2) identifying 3 optimal UO cutoff points for each time window (stages 1, 2, and 3); (3) comparing sensitivity and specificity to develop a unified staging system; (4) assessing average versus persistent reduced UO hourly; (5) comparing the new UO-AKI system to the KDIGO UO-AKI system; (6) integrating sCr criteria with both systems and comparing them; and (7) validating the new classification with an independent cohort. In all these steps, the outcome was hospital mortality. Another analyzed outcome was 90-day mortality. The analyses included ROC curve analysis, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and logistic and Cox regression analyses.

Results: From the MIMIC-IV database, 35,845 patients were included in the development cohort. After comparing the sensitivity and specificity of 12 different lowest UO thresholds across four time frames, 3 cutoff points were selected to compose the proposed UO-AKI classification: stage 1 (0.2-0.3 mL/kg/h), stage 2 (0.1-0.2 mL/kg/h), and stage 3 (< 0.1 mL/kg/h) over 6 h. The proposed classification had better discrimination when the average was used than when the persistent method was used. The adjusted odds ratio demonstrated a significant stepwise increase in hospital mortality with advancing UO-AKI stage. The proposed classification combined or not with the sCr criterion outperformed the KDIGO criteria in terms of predictive accuracy-AUC-ROC 0.75 (0.74-0.76) vs. 0.69 (0.68-0.70); NRI: 25.4% (95% CI: 23.3-27.6); and IDI: 4.0% (95% CI: 3.6-4.5). External validation with the eICU database confirmed the superior performance of the new classification system.

Conclusion: The proposed UO-AKI classification enhances mortality prediction and patient stratification in critically ill patients, offering a more accurate and practical approach than the current KDIGO criteria.

Fig. 1

Participant flow charts for the MIMIC- IV and eICU cohorts. Abbreviations: ICU: intensive care unit; LOS: length of stay; KRT: kidney replacement therapyve; sCr: serum creatinine; eICU:eICU Collaborative Research Database; MIMIC-IV: Medical Information Mart for Intensive Care IV

Fig. 2

Adjusted odds ratio for hospital mortality per acute kidney injury (AKI) severity stage according to proposed and Kidney Disease: Improving Global Outcomes (KDIGO) urinary output (UO) or UO/serum creatinine (sCr) criteria. The association between AKI severity and in-hospital mortality was explored with a multivariate logistic regression model. The variables included in the model were age, sex, baseline serum creatinine (sCr) level, Charlson Comorbidity Index, type of ICU admission, worst nonrenal SOFA score, and the need for KRT

Fig. 3

Area under the curve-receiver operating characteristic (AUC-ROC) curve for the a Proposed and KDIGO UO-AKI criteria and b Proposed and KDIGO UO/sCr-AKI criteria. The AUC-ROC curve was used to predict hospital mortality in the developing (MIMIC-IV database) cohort

Fig. 4

Area under the curve-receiver operating characteristic (AUC-ROC) curve for the proposed and KDIGO UO/sCr-AKI criteria. The AUC-ROC curve was used to predict in-hospital mortality in the validation (eICU database) cohort