Song Binglin, Liu Ping, Liu Chun, Fu Kangrui, Zheng Xiangde, Liu Ying
Clinical Medical College of North Sichuan Medical College, Nanchong, 637000, China.
Southwest Medical University, LuZhou, 646000, China.
Sci Rep. 2025 Jul 1;15(1):21559. doi: 10.1038/s41598-025-07647-6.
To develop and validate a dynamic nomogram for predicting the need for continuous renal replacement therapy (CRRT) in septic patients in the intensive care unit (ICU). Data were extracted from the MIMIC-IV 3.0 database and divided into a training set and a validation set in a 7:3 ratio. Relevant risk factors were identified through LASSO regression, and a binary logistic regression model was subsequently developed. The CRRT risk nomogram was visualized using R language, with the DynNom package employed to create a dynamic nomogram. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), Harrell's C-index, and calibration curves. The clinical utility of the model was evaluated via decision curve analysis (DCA). A total of 7361 septic patients were included in this study, of which 525 required CRRT. The study identified several predictive factors for CRRT, including respiratory rate, oxygen saturation, international normalized ratio (INR), activated partial thromboplastin time (APTT), creatinine, lactate, pH, body weight, renal disease, and severe liver disease. The C-index was 0.871. The AUCs for the training and validation sets were 0.87 (95% CI: 0.8535-0.8883) and 0.86 (95% CI: 0.8282-0.8887), respectively. The calibration curves demonstrated good predictive consistency. DCA confirmed the model's significant clinical value. The dynamic nomogram is available for visualization at: https://zhong-hua-min-zu-wan-sui.shinyapps.io/CRRT_prediction_nomogram/ . We have developed a dynamic nomogram based on the MIMIC-IV database, incorporating 10 clinical features, to predict the probability of CRRT requirement in septic patients. Internal validation showed that this model exhibits robust predictive performance.
开发并验证一种动态列线图，用于预测重症监护病房（ICU）中脓毒症患者对持续肾脏替代疗法（CRRT）的需求。数据从MIMIC-IV 3.0数据库中提取，并按7:3的比例分为训练集和验证集。通过LASSO回归确定相关危险因素，随后建立二元逻辑回归模型。使用R语言将CRRT风险列线图可视化，采用DynNom软件包创建动态列线图。使用受试者操作特征曲线下面积（AUC）、Harrell's C指数和校准曲线评估模型性能。通过决策曲线分析（DCA）评估模型的临床实用性。本研究共纳入7361例脓毒症患者，其中525例需要CRRT。该研究确定了几个CRRT的预测因素，包括呼吸频率、血氧饱和度、国际标准化比值（INR）、活化部分凝血活酶时间（APTT）、肌酐、乳酸、pH值、体重、肾脏疾病和严重肝脏疾病。C指数为0.871。训练集和验证集的AUC分别为0.87（95%CI：0.8535 - 0.8883）和0.86（95%CI：0.8282 - 0.8887）。校准曲线显示出良好的预测一致性。DCA证实了该模型具有显著的临床价值。动态列线图可在以下网址查看：https://zhong-hua-min-zu-wan-sui.shinyapps.io/CRRT_prediction_nomogram/ 。我们基于MIMIC-IV数据库开发了一种动态列线图，纳入了10项临床特征，以预测脓毒症患者需要CRRT的概率。内部验证表明该模型具有强大的预测性能。
