Development and assessment of scoring model for ICU stay and mortality prediction after emergency admissions in ischemic heart disease: a retrospective study of MIMIC-IV databases

作者全名："Shu, Tingting; Huang, Jian; Deng, Jiewen; Chen, Huaqiao; Zhang, Yang; Duan, Minjie; Wang, Yanqing; Hu, Xiaofei; Liu, Xiaozhu"

作者地址："[Shu, Tingting] Army Med Univ, Mil Med Univ 3, Chongqing 400038, Peoples R China; [Huang, Jian] Guangxi Univ Chinese Med, Grad Sch, Nanning, Peoples R China; [Deng, Jiewen] Chongqing Med Univ, Affiliated Hosp 2, Dept Neurosurg, Chongqing 400010, Peoples R China; [Chen, Huaqiao] Chongqing Med Univ, Affiliated Hosp 1, Dept Cardiol, Chongqing, Peoples R China; [Zhang, Yang; Duan, Minjie] Chongqing Med Univ, Coll Med Informat, Chongqing, Peoples R China; [Zhang, Yang; Duan, Minjie] Chongqing Med Univ, Med Data Sci Acad, Chongqing, Peoples R China; [Wang, Yanqing] Chongqing Med Univ, Coll Clin Med 1, Chongqing, Peoples R China; [Hu, Xiaofei] Army Med Univ, Mil Med Univ 3, Southwest Hosp, Dept Radiol, 30 Gaotan Yanzheng St, Chongqing 400038, Peoples R China; [Liu, Xiaozhu] Chongqing Med Univ, Affiliated Hosp 2, Dept Cardiol, 288 Tiantian Ave, Chongqing 400010, Peoples R China"

通信作者："Hu, XF (通讯作者)，Army Med Univ, Mil Med Univ 3, Southwest Hosp, Dept Radiol, 30 Gaotan Yanzheng St, Chongqing 400038, Peoples R China.; Liu, XZ (通讯作者)，Chongqing Med Univ, Affiliated Hosp 2, Dept Cardiol, 288 Tiantian Ave, Chongqing 400010, Peoples R China."

来源：INTERNAL AND EMERGENCY MEDICINE

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000919132400001

JCR分区：Q1

影响因子：3.2

年份：2023

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期号：　

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结束页：　

文献类型：Article; Early Access

关键词：Ischemic heart disease; Emergency department; Scoring model; MIMIC-IV; Machine learning

摘要："Ischemic heart disease (IHD) is the leading cause of death and emergency department (ED) admission. We aimed to develop more accurate and straightforward scoring models to optimize the triaging of IHD patients in ED. This was a retrospective study based on the MIMIC-IV database. Scoring models were established by AutoScore formwork based on machine learning algorithm. The predictive power was measured by the area under the curve in the receiver operating characteristic analysis, with the prediction of intensive care unit (ICU) stay, 3d-death, 7d-death, and 30d-death after emergency admission. A total of 8381 IHD patients were included (median patient age, 71 years, 95% CI 62-81; 3035 [36%] female), in which 5867 episodes were randomly assigned to the training set, 838 to validation set, and 1676 to testing set. In total cohort, there were 2551 (30%) patients transferred into ICU; the mortality rates were 1% at 3 days, 3% at 7 days, and 7% at 30 days. In the testing cohort, the areas under the curve of scoring models for shorter and longer term outcomes prediction were 0.7551 (95% CI 0.7297-0.7805) for ICU stay, 0.7856 (95% CI 0.7166-0.8545) for 3d-death, 0.7371 (95% CI 0.6665-0.8077) for 7d-death, and 0.7407 (95% CI 0.6972-0.7842) for 30d-death. This newly accurate and parsimonious scoring models present good discriminative performance for predicting the possibility of transferring to ICU, 3d-death, 7d-death, and 30d-death in IHD patients visiting ED."

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