CC:重新定义危重症患者急性肾损伤标准中的尿量阈值：一项推导和验证研究

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.目前对急性肾损伤（AKI）的定义包括血清肌酐（sCr）浓度的增加和尿量（UO）的减少。最近的研究表明，标准的UO阈值0.5 ml/kg/h可能不是最优的。本研究旨在开发并验证一种新的基于UO的AKI分类系统，以改善死亡率预测和患者分层。

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.数据来源于MIMIC-IV和eICU数据库。开发过程包括：(1)评估3、6、12和24小时期间UO作为连续变量；(2)为每个时间窗口确定3个最佳UO截断点（1、2、3期）；(3)比较敏感性和特异性以开发统一的分期系统；(4)评估平均每小时与持续减少UO的比较；(5)将新的UO-AKI系统与KDIGO UO-AKI系统进行比较；(6)将sCr标准与两个系统结合并进行比较；以及(7)使用独立队列验证新分类。在所有这些步骤中，结果为医院死亡率。另一个分析结果是90天死亡率。分析包括ROC曲线分析、净重新分类改进（NRI）、综合判别力改进（IDI）以及逻辑和Cox回归分析。

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.从MIMIC-IV数据库中，共有35,845名患者被纳入开发队列。在比较四个时间框架中12个不同最低UO阈值的敏感性和特异性后，选择了3个截断点组成提议的UO-AKI分类：6小时内1期（0.2-0.3 mL/kg/h）、2期（0.1-0.2 mL/kg/h）和3期（<0.1 mL/kg/h）。提议的分类在使用平均值时比使用持续方法时具有更好的判别力。调整后的比值比显示，随着UO-AKI阶段的进展，医院死亡率显著逐步增加。提议的分类无论是否结合sCr标准，在预测准确性方面都优于KDIGO标准—AUC-ROC 0.75 (0.74–0.76) 对比 0.69 (0.68–0.70)；NRI: 25.4% (95% CI: 23.3–27.6)；IDI: 4.0% (95% CI: 3.6–4.5)。使用eICU数据库进行的外部验证确认了新分类系统更优越的性能。

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.提议的UO-AKI分类在危重症患者中增强了死亡率预测和患者分层，提供了比当前KDIGO标准更准确、更实用的方法。