Advancing Treatment and Management of Congestive Heart Failure through Integration of Digital Twin Technology and Big Data Analytics

Abstract

Congestive heart failure (CHF) is a chronic, progressive condition in which the heart is unable to pump sufficient blood to meet the body's needs. The prevalence and healthcare costs associated with CHF are expected to rise as the population ages. Advances in digital health technologies present new opportunities to improve CHF treatment and management. This paper proposes integrating digital twin technology and big data analytics to create personalized virtual heart models that can enable precision medicine, predictive analytics, and virtual clinical trials. A digital twin is a virtual representation of a physical asset that uses real-world data to simulate its near real-time status. For CHF, the digital twin can incorporate the patient's clinical data, imaging, genomics, and wearables data to create a dynamic model of their heart function. Big data analytics of population health data can identify clinical best practices to inform the digital twin models. The virtual heart models can then be used to optimize medications, predict decompensation, and test new therapies in silico. This novel approach can ultimately empower patients and providers with data-driven, individually tailored solutions for managing CHF. Critical research is needed to develop robust virtual physiology models and validate their use for therapeutic decision-making. If successful, the integration of digital twins and big data could significantly advance CHF care and outcomes.