Smole, Tim and Zunkovic, Bojan and Piculin, Matej and Kokalj, Enja and Robnik-Sikonja, Marko and Kukar, Matjaz and Fotiadis, Dimitrios and Pezoulas, Vasileios C. and Tachos, Nikolaos S. and Barlocco, Fausto and Mazzarotto, Francesco and Popovic, Dejana and Maier, Lars and Velicki, Lazar and MacGowan, Guy A. and Olivotto, Iacopo and Filipovic, Nenad and Jakovljevic, Djordje G. and Bosnic, Zoran (2021) A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy. COMPUTERS IN BIOLOGY AND MEDICINE, 135: 104648. ISSN 0010-4825, 1879-0534
Full text not available from this repository. (Request a copy)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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | SUDDEN CARDIAC DEATH; MULTIPLE IMPUTATION; DIAGNOSIS; MRI; Hypertrophic cardiomyopathy; Risk stratification; Machine learning; Artificial intelligence |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Innere Medizin II |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 21 Sep 2022 09:29 |
| Last Modified: | 21 Sep 2022 09:29 |
| URI: | https://pred.uni-regensburg.de/id/eprint/47832 |
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