The increasing life expectancy of the population and the development of effective therapies result in a growing population of aged cancer survivors, which frequently have comorbidities for developing heart failure (HF). Anthracyclines (AC) are still first line treatment for many cancer types, but up to 35% of patients who received them develop cardiotoxicity and HF. The trade-off between cancer and chronic HF is of massive psychological burden for patients, and of devastating economic consequences for healthcare systems. We aim to test the efficacy of a novel intervention (remote ischemic preconditioning) to reduce the incidence of AC-induced HF. We have selected Non-Hodgkin lymphoma as the target population, since it is diagnosed at advanced comorbid age in both genders. This will also allow us study gender differences in AC-induced HF. A phase II randomized clinical trial enrolling 608 patients undergoing AC chemotherapy will be done. Primary endpoint will be based on serial cardiac magnetic resonances exams. Taking advantage of the recruited population and data gathered, we will further validate 2 novel cardiac magnetic resonance imaging methods: a novel early marker of cardiotoxicity, and a new sequence allowing a massive reduction of acquisition time. We will also study a personalized strategy to empower patients in clinical trial execution, which includes Patient-Reported Outcome and Experience Measures (PROMs and PREMs). Our final goal is to reach the patient level by implementing the novel strategy at the clinical level while paving the way for a future large phase III trial. For this endeavour, we count on a multidisciplinary consortium, where different stakeholders of this process are part of the study, from scientists to industry, and from healthcare providers (physicians and nurses) to patients. RESILIENCE deals with 2 of the most frequent non-communicable diseases in Europe (cancer and HF), responsible for a big proportion of healthcare expenditures.

Cancer immunotherapy brought about significant progress in cancer treatment. It resulted in high efficacy in some cancers; e.g., up to 60% objective response rate in melanoma and 80% complete response rate in acute lymphoblastic leukaemia. Nevertheless, two main challenges still impede improving cancer patients’ health status and quality of life (QoL) after immunotherapy initiation: 1) a crucial need for “predictive markers” of occurrence of immunotherapy-related adverse events (IR-AEs) to predict and improve patients’ health status and promote their QoL; and, 2) the lack of knowledge on patients after start of immunotherapy outside randomised controlled trials. To reach these goals, significantly more diversified sources of data are required. Project QUALITOP aims at developing a European immunotherapy-specific open Smart Digital Platform and using big data analysis, artificial intelligence, and simulation modelling approaches. This will enable collecting and aggregating efficiently real-world data to monitor health status and QoL of cancer patients given immunotherapy. Through causal inference analyses, QUALITOP will identify the determinants of health status regarding IR-AEs and define patient profiles in a real-world context. For this, heterogeneous data sources (big data), both retrospective and prospective --collected for QUALITOP from clinical centres in four EU countries—will integrate lifestyle, genetic, and psychosocial determinants of QoL. Using machine learning approaches, QUALITOP will provide "real-time" recommendations stemming from patient profiles and feedbacks via the Smart Digital Platform. Furthermore, an increased visibility on patients’ behaviour, a better IR-AEs prediction, and an improvement of care coordination will help analysing through simulation modelling approaches the gain in cost-effectiveness. Guidelines will be issued over the short and long-term.