Zou Kang, Huang Shu, Ren Wensen, Xu Huan, Zhang Wei, Shi Xiaomin, Shi Lei, Zhong Xiaolin, Peng Yan, Lü Muhan, Tang Xiaowei
Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.
Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, People's Republic of China.
Int J Gen Med. 2023 Jun 17;16:2541-2553. doi: 10.2147/IJGM.S409812. eCollection 2023.
The aim of this study is to develop and validate a predictive model for the prediction of in-hospital mortality in patients with acute pancreatitis (AP) based on the intensive care database.
We analyzed the data of patients with AP in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and Electronic Intensive Care Unit Collaborative Research Database (eICU-CRD). Then, patients from MIMIC-IV were divided into a development group and a validation group according to the ratio of 8:2, and eICU-CRD was assigned as an external validation group. Univariate logistic regression and least absolute shrinkage and selection operator regression were used for screening the best predictors, and multivariate logistic regression was used to establish a dynamic nomogram. We evaluated the discrimination, calibration, and clinical efficacy of the nomogram, and compared the performance of the nomogram with Acute Physiology and Chronic Health Evaluation II (APACHE-II) score and Bedside Index of Severity in AP (BISAP) score.
A total of 1030 and 514 patients with AP in MIMIC-IV database and eICU-CRD were included in the study. After stepwise analysis, 8 out of a total of 37 variables were selected to construct the nomogram. The dynamic nomogram can be obtained by visiting https://model.sci-inn.com/KangZou/. The area under receiver operating characteristic curve (AUC) of the nomogram was 0.859, 0.871, and 0.847 in the development, internal, and external validation set respectively. The nomogram had a similar performance with APACHE-II (AUC = 0.841, p = 0.537) but performed better than BISAP (AUC = 0.690, p = 0.001) score in the validation group. Moreover, the calibration curve presented a satisfactory predictive accuracy, and the decision curve analysis suggested great clinical application value of the nomogram.
Based on the results of internal and external validation, the nomogram showed favorable discrimination, calibration, and clinical practicability in predicting the in-hospital mortality of patients with AP.
本研究旨在基于重症监护数据库开发并验证一种预测急性胰腺炎(AP)患者院内死亡率的预测模型。
我们分析了重症监护医学信息集市-IV(MIMIC-IV)数据库和电子重症监护病房协作研究数据库(eICU-CRD)中AP患者的数据。然后,将MIMIC-IV中的患者按照8:2的比例分为开发组和验证组,将eICU-CRD作为外部验证组。采用单因素逻辑回归和最小绝对收缩与选择算子回归筛选最佳预测因子,采用多因素逻辑回归建立动态列线图。我们评估了列线图的辨别力、校准度和临床疗效,并将列线图的性能与急性生理与慢性健康状况评分系统II(APACHE-II)评分和AP严重程度床边指数(BISAP)评分进行比较。
本研究纳入了MIMIC-IV数据库和eICU-CRD中分别为1030例和514例AP患者。经过逐步分析,从总共37个变量中选择了8个变量来构建列线图。可通过访问https://model.sci-inn.com/KangZou/获取动态列线图。列线图在开发集、内部验证集和外部验证集中的受试者操作特征曲线下面积(AUC)分别为0.859、0.871和0.847。在验证组中,列线图的表现与APACHE-II评分相似(AUC = 0.841,p = 0.537),但优于BISAP评分(AUC = 0.690,p = 0.001)。此外,校准曲线显示出令人满意的预测准确性,决策曲线分析表明列线图具有很大的临床应用价值。
基于内部和外部验证结果,列线图在预测AP患者院内死亡率方面显示出良好的辨别力、校准度和临床实用性。