Yan Dongmei, Zhou Jing, Zhang Hongying, Peng Chaohua
Department of Critical Care Medicine, Guang'an People's Hospital, West China Guang'an Hospital, Sichuan University Guang'an 638000, Sichuan, China.
ICU, Affiliated Hospital of North Sichuan Medical College Nanchong 637000, Sichuan, China.
Am J Transl Res. 2025 Aug 15;17(8):6141-6149. doi: 10.62347/JOZT7082. eCollection 2025.
To identify independent risk factors for multiple organ failure (MOF) and construct a clinically applicable predictive nomogram.
We retrospectively analyzed 418 patients with acute kidney failure (AKF) and severe sepsis treated between January 2020 and September 2024. Demographic data, clinical features, and laboratory parameters were collected. Patients were randomly assigned to a training cohort (n=293) and a validation cohort (n=125). Independent risk factors for MOF were identified using logistic regression analysis, and a nomogram was subsequently developed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).
Five independent predictors of MOF were identified: abdominal infection, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, neutrophil count (NEU), lactate (Lac), and heparin-binding protein (HBP). The nomogram showed good discrimination, with an AUC of 0.756 (95% CI: 0.701-0.811) in the training cohort and 0.816 (95% CI: 0.743-0.889) in the validation cohort. Calibration curves demonstrated good agreement between predicted and observed outcomes, and DCA indicated a favorable net clinical benefit.
A nomogram incorporating abdominal infection, APACHE II score, NEU, Lac, and HBP effectively predicts the risk of MOF in AKF patients with severe sepsis. This model may aid in early risk stratification and clinical decision-making.
确定多器官功能衰竭（MOF）的独立危险因素，并构建一个临床适用的预测列线图。
我们回顾性分析了2020年1月至2024年9月期间治疗的418例急性肾衰竭（AKF）合并严重脓毒症患者。收集了人口统计学数据、临床特征和实验室参数。患者被随机分配到训练队列（n = 293）和验证队列（n = 125）。使用逻辑回归分析确定MOF的独立危险因素，随后开发列线图。使用受试者操作特征曲线（AUC）下的面积、校准曲线和决策曲线分析（DCA）评估模型性能。
确定了MOF的五个独立预测因素：腹部感染、急性生理与慢性健康状况评估II（APACHE II）评分、中性粒细胞计数（NEU）、乳酸（Lac）和肝素结合蛋白（HBP）。列线图显示出良好的区分度，训练队列中的AUC为0.756（95%CI：0.701 - 0.811），验证队列中的AUC为0.816（95%CI：0.743 - 0.889）。校准曲线显示预测结果与观察结果之间具有良好的一致性，DCA表明具有良好的净临床效益。
包含腹部感染、APACHE II评分、NEU、Lac和HBP的列线图可有效预测严重脓毒症AKF患者发生MOF的风险。该模型可能有助于早期风险分层和临床决策。
