A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy. (August 2021)

• Record Type: Journal Article
• Authors: Smole, Tim; Žunkovič, Bojan; Pičulin, Matej; Kokalj, Enja; Robnik-Šikonja, Marko; Kukar, Matjaž; Fotiadis, Dimitrios I.; Pezoulas, Vasileios C.; Tachos, Nikolaos S.; Barlocco, Fausto; Mazzarotto, Francesco; Popović, Dejana; Maier, Lars; Velicki, Lazar; MacGowan, Guy A.; Olivotto, Iacopo; Filipović, Nenad; Jakovljević, Djordje G.; Bosnić, Zoran
• Is Part Of: Computers in biology and medicine. Volume 135(2021)
• Publication Date: 2021-08
• Subjects: Hypertrophic cardiomyopathy -- Risk stratification -- Machine learning -- Artificial intelligence; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104648
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools. Method: Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death. Results: The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively. Conclusions: The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general. Graphical abstract: Image 1 Highlights: Machine learning-based risk stratification is an alternative to statistical methods. Machine learning risk-stratification model outperforms related statistical models. Boosted trees achieved the highest risk stratification predictive accuracy. Explanation of machine learning models confirms some of the known risk factors.