Optimized Multi-modality Machine Learning Approach During Cardio-toxic Chemotherapy to Predict Arising Heart Failure

Overview

The present project will develop an automated machine learning approach using multi-modality data (imaging, laboratory, electrocardiography and questionnaire) to increase the understanding and prediction of arising heart failure in patients scheduled for cardio-toxic chemotherapy. This algorithmus will be developed by the technical cooperation partner at Technion, the institut for biomedical engineering in Haifa, Israel.

The present project will develop an automated machine learning approach using multi-modality data (imaging, laboratory, electrocardiography and questionnaire) to increase the understanding and prediction of arising heart failure in patients scheduled for cardio-toxic chemotherapy. This algorithmus will be developed by the technical cooperation partner Prof. Adam who leads the Technion, the institut for biomedical engineering. Specific aims: 1. To collect all achievable data from patients scheduled for cardiotoxic chemotherapy at baseline, up to 6 months after ending therapy - regarding imaging (MRI, echocardiography with conventional and strain parameter), electrocardiography, biomedical markers (to define the function of liver, kidney, heart and hematopoietic bone marrow), clinical parameter and quality of life questionnaire: 2. To optimize and evaluate a robust machine learning approach that integrate and assess all these data to detect early myocardial damage and to identify an optimal parameter (single or in combination) for prediction of subclinical left ventricular (LV) dysfunction (stage 1 of the current study). 3. To perform a clinical study (stage 2 of the current study) of chemotherapy patients, and to identify subclinical LV dysfunction, which will be used to guide cardioprotective therapy using the new machine learning approach in comparison to the actual standard procedure using only echocardiographic left ventricular ejection fraction (LVEF). The purpose of this study is to evaluate and optimize a machine learning approach to combine and integrate data from different imaging modalities with laboratory, electrocardiography and questionnaire information to define the value of all these parameter in patient management, by identification of subclinical LV dysfunction, which will be used to guide cardioprotective therapy in comparison to a standard approach using only conventional echocardiographic parameters. MRI, conventional echocardiographic parameters and echocardiographic myocardial deformation imaging are employing different modalities and approaches to obtain insight into myocardial tissue and deformation. We hypothesize that a new and optimized automated algorithm using these modalities and integrating laboratory, electrocardiography and questionnaire information will improve the detection of early LV dysfunctions, and will bring new insight to the potential response of chemo patients to cardiotoxic therapy. We expect that this algorithm leads to the use of adjunctive therapy that will limit the development of LV dysfunction, interruptions of chemotherapy and development of heart failure in follow-up and thus will reduce morbidity and costs.

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