A machine-learning-based method to predict adverse events in patients with dilated cardiomyopathy and severely reduced ejection fractions
| Autor: | Shenglei Shu, Xiao-Yue Zhou, Ziming Hong, Tianjng Zhang, Qinmu Peng, Jing Wang, Chuansheng Zheng |
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| Rok vydání: | 2021 |
| Předmět: |
Adult
Cardiomyopathy Dilated Male medicine.medical_specialty Risk Assessment Machine Learning Electrocardiography Ventricular Dysfunction Left Predictive Value of Tests Internal medicine medicine Left ventricular ejection Humans Radiology Nuclear Medicine and imaging In patient cardiovascular diseases Adverse effect Retrospective Studies Ejection fraction Full Paper business.industry Dilated cardiomyopathy Stroke Volume General Medicine Middle Aged medicine.disease Magnetic Resonance Imaging cardiovascular system Cardiology Female business |
| Zdroj: | Br J Radiol |
| ISSN: | 1748-880X |
| Popis: | Objective: Patients with dilated cardiomyopathy (DCM) and severely reduced left ventricular ejection fractions (LVEFs) are at very high risks of experiencing adverse cardiac events. A machine learning (ML) method could enable more effective risk stratification for these high-risk patients by incorporating various types of data. The aim of this study was to build an ML model to predict adverse events including all-cause deaths and heart transplantation in DCM patients with severely impaired LV systolic function. Methods: One hundred and eighteen patients with DCM and severely reduced LVEFs (Results: Twelve patients died, and 17 patients underwent heart transplantation during the median follow-up of 508 days. The ML model included systolic blood pressure, left ventricular end-systolic and end-diastolic volume indices, and late gadolinium enhancement (LGE) extents on CMR imaging, and a support vector machine was selected as a classifier. The model showed excellent performance in predicting adverse events in DCM patients with severely reduced LVEF (the AUC and accuracy values were 0.873 and 0.763, respectively). Conclusions: This ML technique could effectively predict adverse events in DCM patients with severely reduced LVEF. Advances in knowledge: The ML method has superior ability in risk stratification in severe DCM patients. |
| Databáze: | OpenAIRE |
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