Echocardiography-based machine learning algorithm for distinguishing ischemic cardiomyopathy from dilated cardiomyopathy

BMC Cardiovascular Disorders

Research

Xiaolin Su¹  Yi Liu²  Yongjian Deng²  Mei Zhou²  Xiaocong Zeng³ 

[1] Department of Cardiology, Minzu Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China;Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, 530021, Nanning, Guangxi, China;Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, 530021, Nanning, Guangxi, China;Guangxi Key Laboratory Base of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention & Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China;School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China;

关键词: Machine learning;    Heart failure;    Ischemic cardiomyopathy;    Dilated cardiomyopathy;    Echocardiography;   

DOI  :  10.1186/s12872-023-03520-4

 received in 2022-07-27, accepted in 2023-09-19,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundMachine learning (ML) can identify and integrate connections among data and has the potential to predict events. Heart failure is primarily caused by cardiomyopathy, and different etiologies require different treatments. The present study examined the diagnostic value of a ML algorithm that combines echocardiographic data to automatically differentiate ischemic cardiomyopathy (ICM) from dilated cardiomyopathy (DCM).MethodsWe retrospectively collected the echocardiographic data of 200 DCM patients and 199 ICM patients treated in the First Affiliated Hospital of Guangxi Medical University between July 2016 and March 2022. All patients underwent invasive coronary angiography for diagnosis of ICM or DCM. The data were randomly divided into a training set and a test set via 10-fold cross-validation. Four ML algorithms (random forest, logistic regression, neural network, and XGBoost [ML algorithm under gradient boosting framework]) were used to generate a training model for the optimal subset, and the parameters were optimized. Finally, model performance was independently evaluated on the test set, and external validation was performed on 79 patients from another center.ResultsCompared with the logistic regression model (area under the curve [AUC] = 0.925), neural network model (AUC = 0.893), and random forest model (AUC = 0.900), the XGBoost model had the best identification rate, with an average sensitivity of 72% and average specificity of 78%. The average accuracy was 75%, and the AUC of the optimal subset was 0.934. External validation produced an AUC of 0.804, accuracy of 78%, sensitivity of 64% and specificity of 93%.ConclusionsWe demonstrate that utilizing advanced ML algorithms can help to differentiate ICM from DCM and provide appreciable precision for etiological diagnosis and individualized treatment of heart failure patients.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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