"Explainable machine learning models for predicting 30-day readmission in pediatric pulmonary hypertension: A multicenter, retrospective study"

作者全名："Duan, Minjie; Shu, Tingting; Zhao, Binyi; Xiang, Tianyu; Wang, Jinkui; Huang, Haodong; Zhang, Yang; Xiao, Peilin; Zhou, Bei; Xie, Zulong; Liu, Xiaozhu"

作者地址："[Duan, Minjie; Zhang, Yang] Chongqing Med Univ, Coll Med Informat, Chongqing, Peoples R China; [Duan, Minjie; Huang, Haodong; Zhang, Yang] Chongqing Med Univ, Med Data Sci Acad, Chongqing, Peoples R China; [Shu, Tingting] Chongqing Med Univ, Affiliated Hosp 1, Dept Cardiol, Chongqing, Peoples R China; [Zhao, Binyi; Xiao, Peilin; Zhou, Bei; Xie, Zulong; Liu, Xiaozhu] Chongqing Med Univ, Affiliated Hosp 2, Dept Cardiol, Chongqing, Peoples R China; [Xiang, Tianyu] Chongqing Med Univ, Univ Town Hosp, Informat Ctr, Chongqing, Peoples R China; [Wang, Jinkui] Chongqing Med Univ, Childrens Hosp, Dept Urol, Chongqing, Peoples R China; [Huang, Haodong] Chongqing Hlth Ctr Women & Children, Personnel Dept, Chongqing, Peoples R China"

通信作者："Xie, ZL; Liu, XZ (通讯作者)，Chongqing Med Univ, Affiliated Hosp 2, Dept Cardiol, Chongqing, Peoples R China."

来源：FRONTIERS IN CARDIOVASCULAR MEDICINE

ESI学科分类：　

WOS号：WOS:000864612100001

JCR分区：Q2

影响因子：3.6

年份：2022

卷号：9

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：pediatric pulmonary hypertension; readmission; machine learning; prediction; risk factors

摘要："BackgroundShort-term readmission for pediatric pulmonary hypertension (PH) is associated with a substantial social and personal burden. However, tools to predict individualized readmission risk are lacking. This study aimed to develop machine learning models to predict 30-day unplanned readmission in children with PH. MethodsThis study collected data on pediatric inpatients with PH from the Chongqing Medical University Medical Data Platform from January 2012 to January 2019. Key clinical variables were selected by the least absolute shrinkage and the selection operator. Prediction models were selected from 15 machine learning algorithms with excellent performance, which was evaluated by area under the operating characteristic curve (AUC). The outcome of the predictive model was interpreted by SHapley Additive exPlanations (SHAP). ResultsA total of 5,913 pediatric patients with PH were included in the final cohort. The CatBoost model was selected as the predictive model with the greatest AUC for 0.81 (95% CI: 0.77-0.86), high accuracy for 0.74 (95% CI: 0.72-0.76), sensitivity 0.78 (95% CI: 0.69-0.87), and specificity 0.74 (95% CI: 0.72-0.76). Age, length of stay (LOS), congenital heart surgery, and nonmedical order discharge showed the greatest impact on 30-day readmission in pediatric PH, according to SHAP results. ConclusionsThis study developed a CatBoost model to predict the risk of unplanned 30-day readmission in pediatric patients with PH, which showed more significant performance compared with traditional logistic regression. We found that age, LOS, congenital heart surgery, and nonmedical order discharge were important factors for 30-day readmission in pediatric PH."

基金机构：Intelligent Medicine Research Project of Chongqing Medical University; Chongqing Postdoctoral Program; [ZHYX2019013]; [YJSZHYX202119]; [2010010006118105]

基金资助正文：Funding This study was supported by the Intelligent Medicine Research Project of Chongqing Medical University (Nos. ZHYX2019013 and YJSZHYX202119) and Chongqing Postdoctoral Program (No. 2010010006118105).