Cancer treatments have improved substantially over the past decades, but some effective therapies such as anthracyclines and HER2-targeted agents are associated with severe cardiovascular adverse effects, including heart failure. Existing cardiovascular risk prediction scores have limited evidence in this setting. The ML-CardioTox study is a prospective, multicenter, observational cohort conducted in 15 centers in France. The primary objective is to develop a one-year prediction score for cancer therapy-related cardiotoxicity using machine learning methods. A dedicated software platform will be used to standardize data collection and support integration of artificial intelligence tools. A total of 600 patients treated with anthracyclines or HER2-targeted therapies in cardio-oncology clinics will be enrolled over a one-year inclusion period starting in December 2024, with a 12-month follow-up. The primary endpoint is the occurrence of cardiotoxicity as defined by the 2022 European Society of Cardiology guidelines (hospitalization for heart failure, initiation or escalation of diuretic therapy, decline in cardiac function on imaging, or increase in cardiac biomarkers such as troponin or natriuretic peptides). Secondary objectives include comparison of the predictive performance of the machine learning-derived score with the established HFA-ICOS risk score. Patients will be managed according to routine clinical practice. This study aims to improve prognostic stratification tools for patients receiving anthracyclines or HER2-targeted therapies, with the goal of better identifying those at high risk of developing cardiotoxicity during follow-up.
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Incidence of cardiotoxicity at 12 months
Timeframe: 12 months after initiation of anthracycline or HER2-targeted therapy.