Lei Jiahao, Zhang Zhuojing, Li Yixuan, Wu Zhaoyu, Pu Hongji, Xu Zhijue, Yang Xinrui, Hu Jiateng, Liu Guang, Qiu Peng, Chen Tao, Lu Xinwu
Department of Vascular Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai People's Republic of China.
Department of Economics, University of Waterloo, Waterloo, Canada.
Digit Health. 2024 Aug 19;10:20552076241269450. doi: 10.1177/20552076241269450. eCollection 2024 Jan-Dec.
Aortic dissection (AD) is a severe emergency with high morbidity and mortality, necessitating strict monitoring and management. This retrospective study aimed to identify prognostic factors and establish predictive models for in-hospital mortality among AD patients in the intensive care unit (ICU).
We retrieved ICU admission records of AD patients from the Medical Information Mart for Intensive Care (MIMIC)-IV critical care data set and the eICU Collaborative Research Database. Functional data analysis was further applied to estimate continuous vital sign processes, and variables associated with in-hospital mortality were identified through univariate analyses. Subsequently, we employed multivariable logistic regression and machine learning techniques, including simple decision tree, random forest (RF), and eXtreme Gradient Boosting (XGBoost) to develop prognostic models for in-hospital mortality.
Given 643 ICU admissions from MIMIC-IV and 501 admissions from eICU, 29 and 28 prognostic factors were identified from each database through univariate analyses, respectively. For prognostic model construction, 507 MIMIC-IV admissions were divided into 406 (80%) for training and 101 (20%) for internal validation, and 87 eICU admissions were included as an external validation group. Of the four models tested, the RF consistently exhibited the best performance among different variable subsets, boasting area under the receiver operating characteristic curves of 0.870 and 0.850. The models highlighted the mean 24-h fluid intake as the most potent prognostic factor.
The current prognostic models effectively forecasted in-hospital mortality among AD patients, and they pinpointed noteworthy prognostic factors, including initial blood pressure upon ICU admission and mean 24-h fluid intake.
主动脉夹层（AD）是一种严重的急症，发病率和死亡率高，需要严格监测和管理。本回顾性研究旨在确定重症监护病房（ICU）中AD患者院内死亡的预后因素并建立预测模型。
我们从重症监护医学信息集市（MIMIC）-IV重症监护数据集和电子ICU协作研究数据库中检索了AD患者的ICU入院记录。进一步应用功能数据分析来估计连续生命体征过程，并通过单变量分析确定与院内死亡相关的变量。随后，我们采用多变量逻辑回归和机器学习技术，包括简单决策树、随机森林（RF）和极端梯度提升（XGBoost）来建立院内死亡的预后模型。
鉴于来自MIMIC-IV的643例ICU入院病例和来自电子ICU的501例入院病例，通过单变量分析分别从每个数据库中确定了29个和28个预后因素。对于预后模型构建，将507例MIMIC-IV入院病例分为406例（80%）用于训练和101例（20%）用于内部验证，并将87例电子ICU入院病例作为外部验证组。在测试的四个模型中，RF在不同变量子集中始终表现出最佳性能，受试者操作特征曲线下面积分别为0.870和0.850。模型突出显示平均24小时液体摄入量是最有力的预后因素。
当前的预后模型有效地预测了AD患者的院内死亡率，并确定了值得注意的预后因素，包括ICU入院时的初始血压和平均24小时液体摄入量。
