Data Sheet 1_Machine learning prediction models for mortality risk in sepsis-associated acute kidney injury: evaluating early versus late CRRT initiation.docx

BackgroundSepsis-associated acute kidney injury (S-AKI) has a significant impact on patient survival, with continuous renal replacement therapy (CRRT) being a crucial intervention. However, the optimal timing for CRRT initiation remains controversial.
MethodsUsing the MIMIC-IV database for model development and the eICU database for external validation, we analyzed patients with S-AKI to compare survival rates between early and late CRRT initiation groups. Propensity score matching was performed to address potential selection bias. Subgroup analyses stratified patients by disease severity using SOFA scores (low ≤10, medium 11–15, high >15) and creatinine levels (low ≤3 mg/dL, medium 3–5 mg/dL, high >5 mg/dL). Multiple machine learning models were developed and evaluated to predict patient prognosis, with Shapley Additive exPlanations (SHAP) analysis identifying key prognostic factors.
ResultsAfter propensity score matching, late CRRT initiation was associated with improved survival probability, but led to increased hospital and ICU stays. Subgroup analyses showed consistent trends favoring late CRRT across all SOFA categories, with the most pronounced effect in high SOFA scores (>15, p = 0.058). The GBM model demonstrated robust predictive performance (average C-index 0.694 in validation and test sets). SHAP analysis identified maximum lactate levels, age, and minimum SpO2 as the strongest predictors of mortality, while CRRT timing showed relatively lower impact on outcome prediction.
ConclusionWhile later initiation of CRRT in S-AKI patients was associated with improved survival, this benefit comes with increased healthcare resource utilization. The clinical parameters, rather than CRRT timing, are the primary determinants of patient outcomes, suggesting the need for a more personalized approach to CRRT initiation based on overall illness severity.

**背景** 脓毒症相关急性肾损伤（Sepsis-associated acute kidney injury, S-AKI）对患者生存预后具有显著影响，连续性肾脏替代治疗（continuous renal replacement therapy, CRRT）是其关键干预手段之一，但CRRT启动的最佳时机目前仍存在争议。
**方法** 本研究采用MIMIC-IV数据库开展模型开发，以eICU数据库进行外部验证，纳入脓毒症相关急性肾损伤患者，对比早期与晚期启动CRRT两组患者的生存率。为控制潜在的选择偏倚，本研究采用倾向得分匹配法进行校正。亚组分析依据序贯器官衰竭评分（SOFA）将患者按疾病严重程度分层（低危≤10、中危11~15、高危>15），同时按肌酐水平分层（低危≤3 mg/dL、中危3~5 mg/dL、高危>5 mg/dL）。本研究构建并评估了多种机器学习模型以预测患者预后，并通过夏普利可加性解释（Shapley Additive exPlanations, SHAP）分析明确关键预后影响因素。
**结果** 倾向得分匹配后，晚期启动CRRT与患者生存概率提升相关，但会延长住院及ICU停留时长。亚组分析显示，在所有SOFA分层中均呈现出晚期启动CRRT更具优势的一致趋势，其中在SOFA评分高危组（>15，P=0.058）中获益最为显著。梯度提升树（Gradient Boosting Machine, GBM）模型展现出稳健的预测性能（验证集与测试集的平均一致性指数C-index为0.694）。SHAP分析结果显示，最高乳酸水平、年龄及最低脉搏血氧饱和度（SpO2）是预测患者死亡的最强影响因素，而CRRT启动时机对预后预测的影响相对较弱。
**结论** 尽管脓毒症相关急性肾损伤患者晚期启动CRRT可提升生存概率，但该获益伴随着医疗资源消耗的增加。患者预后的主要决定因素为临床参数而非CRRT启动时机，这提示临床需基于患者整体疾病严重程度，制定更为个体化的CRRT启动策略。