Improving optimal medical therapy and physical activity for heart failure patients


Improving optimal medical therapy and physical activity for heart failure patients

29 September, 2020

About 15 million patients suffer from heart failure in Europe. For this patients, annual mortality rate is between 10% to 20%. Heart failure patients, in particular those with (multiple) comorbidities, do not receive optimal medical therapy, leading to potentially avoidable specialist-visits and frequent hospitalizations, impaired quality of life or even life-threatening complications. All this results in high costs for society.

Optimal medical therapy and lifestyle changes such as increased physical activity are the cornerstones of treatment. According to the European Society for Cardiology guidelines, regular aerobic exercise is recommended to all heart failure patients with reduced ejection fraction (the left ventricle loses its ability to contract normally). In fact, several studies have shown that physical activity is just as effective as medical therapy and can lower hospitals admissions and decrease mortality. Nevertheless, physical activity in this group of patients is challenging.

The Heart Failure pilot within BigMedilytics —involving Erasmus Universitair Medisch Centrum Rotterdam (as leader of the pilot), Achmea, and the Netherlands Organisation for Applied Scientific Research (TNO)— is focused on introducing personal healthcare concepts to the benefits of patients with heart failure, underlining the impact of appropriated infrastructure to deploy eHealth applications for value-based healthcare. The goal is to reach optimal medical therapy in 100% of subjects.

In order to improve physical activity for this group of patients, the Heart Failure pilot within BigMedilytics follows three tracks:

  • Intervention study to demonstrate that monitoring physical activity in combination with motivational feedback benefits the level of participation in centre-based cardiac rehabilitation and, hence, the outcomes of these patients.
  • Identifying patients with high risk of hospitalizations or rehospitalizations and/or high mortality using big data techniques to select patients for the intervention in the future.
  • Combining data from both tracks using a secure multi party computation (MPC) technique to improve the patient selections with privacy sensitive data of different parties. In fact, recently, the pilot successfully demonstrated that it is possible to securely link and analyze personal data from multiple organizations.