Development and validation of a machine learning-based readmission risk prediction model for non-ST elevation myocardial infarction patients after percutaneous coronary intervention

• Published in: Scientific reports Vol. 14; no. 1; pp. 13393 - 13
• Main Authors: Liu, Yanxu, Du, Linqin, Li, Lan, Xiong, Lijuan, Luo, Hao, Kwaku, Eugene, Mei, Xue, wen, Cong, Cui, Yang Yang, Zhou, Yang, Zeng, Lang, Li, Shikang, Wang, Kun, Zheng, Jiankang, Liu, Zonglian, Hu, Houxiang, Yue, Rongchuan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11.06.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/1807; 692/4019; 692/499; 692/699; 692/699/75; 692/700; 692/700/1538; 692/700/459; Accuracy; Aged; Angioplasty; Female; Heart attacks; High density lipoprotein; Humanities and Social Sciences; Humans; Learning algorithms; Low density lipoprotein; Machine Learning; Male; Middle Aged; multidisciplinary; Myocardial infarction; Non-ST Elevated Myocardial Infarction - surgery; Non-ST elevation myocardial infarction; Patient Readmission - statistics & numerical data; Patients; PCI; Percutaneous Coronary Intervention - adverse effects; Percutaneous Coronary Intervention - methods; Performance evaluation; Prediction model; Prediction models; Readmissions; Risk Assessment - methods; Risk Factors; Risk groups; ROC Curve; Science; Science (multidisciplinary); Non-ST elevation myocardial infarction; PCI; Prediction model; Machine learning; Readmissions
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-64048-x

To investigate the factors that influence readmissions in patients with acute non-ST elevation myocardial infarction (NSTEMI) after percutaneous coronary intervention (PCI) by using multiple machine learning (ML) methods to establish a predictive model. In this study, 1576 NSTEMI patients who were hospitalized at the Affiliated Hospital of North Sichuan Medical College were selected as the research subjects. They were divided into two groups: the readmitted group and the non-readmitted group. The division was based on whether the patients experienced complications or another incident of myocardial infarction within one year after undergoing PCI. Common variables selected by univariate and multivariate logistic regression, LASSO regression, and random forest were used as independent influencing factors for NSTEMI patients’ readmissions after PCI. Six different ML models were constructed using these common variables. The area under the ROC curve, accuracy, sensitivity, and specificity were used to evaluate the performance of the six ML models. Finally, the optimal model was selected, and a nomogram was created to visually represent its clinical effectiveness. Three different methods were used to select seven representative common variables. These variables were then utilized to construct six different ML models, which were subsequently compared. The findings indicated that the LR model exhibited the most optimal performance in terms of AUC, accuracy, sensitivity, and specificity. The outcome, admission mode (walking and non-walking), communication ability, CRP, TC, HDL, and LDL were identified as independent predicators of readmissions in NSTEMI patients after PCI. The prediction model constructed by the LR algorithm was the best. The established column graph model established proved to be effective in identifying high-risk groups with high accuracy and differentiation. It holds a specific predictive value for the occurrence of readmissions after direct PCI in NSTEMI patients.