Medical School of Chinese PLA, 28 Fuxing Road, Beijing, China; Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, 28 Fuxing Road, Beijing, China.
Beijing Xiaomi Mobile Software Co., Ltd., China.
Am J Emerg Med. 2021 Aug;46:38-44. doi: 10.1016/j.ajem.2021.03.006. Epub 2021 Mar 8.
Rhabdomyolysis (RM) is a complex set of clinical syndromes involving the rapid dissolution of skeletal muscles. The early detection of patients who need renal replacement therapy (RRT) is very important and may aid in delivering proper care and optimizing the use of limited resources.
Retrospective analyses of the following three databases were performed: the eICU Collaborative Research Database (eICU-CRD), the Medical Information Mart for Intensive Care III (MIMIC-III) database and electronic medical records from the First Medical Centre of the Chinese People's Liberation Army General Hospital (PLAGH). The data from the eICU-CRD and MIMIC-III datasets were merged to form the derivation cohort. The data collected from the Chinese PLAGH were used for external validation. The factors predictive of the need for RRT were selected using a LASSO regression analysis. A logistic regression was selected as the algorithm. The model was built in Python using the ML library scikit-learn. The accuracy of the model was measured by the area under the receiver operating characteristic curve (AUC). R software was used for the LASSO regression analysis, nomogram, concordance index, calibration, and decision and clinical impact curves.
In total, 1259 patients with RM (614 patients from eICU-CRD, 324 patients from the MIMIC-III database and 321 patients from the Chinese PLAGH) were eligible for this analysis. The rate of RRT was 15.0% (92/614) in the eICU-CRD database, 17.6% (57/324) in the MIMIC-III database and 5.6% in the Chinese PLAGH (18/321). After the LASSO regression selection, eight variables were included in the RRT prediction model. The AUC of the model in the training dataset was 0.818 (95% CI 0.78-0.87), the AUC in the test dataset was 0.794 (95% CI 0.72-0.86), and the AUC in the Chinese PLAGH dataset (external validation dataset) was 0.820 (95% CI 0.70-0.86).
We developed and validated a model for the early prediction of the RRT requirement among patients with RM based on 8 variables commonly measured during the first 24 h after admission. Predicting the need for RRT could help ensure appropriate treatment and facilitate the optimization of the use of medical resources.
横纹肌溶解症（RM）是一组涉及骨骼肌迅速溶解的复杂临床综合征。早期发现需要肾脏替代治疗（RRT）的患者非常重要，这可能有助于提供适当的护理并优化有限资源的利用。
对以下三个数据库进行回顾性分析：eICU 协作研究数据库（eICU-CRD）、医疗信息集市 III（MIMIC-III）数据库和中国人民解放军总医院第一医疗中心的电子病历。eICU-CRD 和 MIMIC-III 数据集的数据合并形成推导队列。从中国 PLAGH 收集的数据用于外部验证。使用 LASSO 回归分析选择预测 RRT 需要的因素。选择逻辑回归作为算法。使用 ML 库 scikit-learn 在 Python 中构建模型。使用接收者操作特征曲线（AUC）下的面积来衡量模型的准确性。使用 R 软件进行 LASSO 回归分析、列线图、一致性指数、校准和决策和临床影响曲线。
共有 1259 名 RM 患者（eICU-CRD 数据库 614 例、MIMIC-III 数据库 324 例、中国 PLAGH 数据库 321 例）符合本分析条件。eICU-CRD 数据库中 RRT 的发生率为 15.0%（92/614），MIMIC-III 数据库为 17.6%（57/324），中国 PLAGH 为 5.6%（18/321）。经过 LASSO 回归选择，有 8 个变量纳入 RRT 预测模型。该模型在训练数据集的 AUC 为 0.818（95%CI 0.78-0.87），在测试数据集的 AUC 为 0.794（95%CI 0.72-0.86），在中国 PLAGH 数据集（外部验证数据集）的 AUC 为 0.820（95%CI 0.70-0.86）。
我们基于入院后前 24 小时内通常测量的 8 个变量，开发并验证了一种预测 RM 患者 RRT 需求的模型。预测 RRT 的需求可以帮助确保适当的治疗并促进优化医疗资源的利用。
