Professor Steven Niederer from the School of Biomedical Engineering & Imaging Sciences at King’s College London has received a £1.5 million Engineering & Physical Sciences Research Council grant to explore the technical development of a digital twin for the heart.
The technical study is to allow researchers to create computational models of specific patient hearts which can track and forecast how patients will respond to medications and surgeries, along with improving monitoring into heart health.
KCL describe how these computational models can provide a common framework for integrating multiple data sets from individual patients. Based on physiology and physics rather than population statistics, they enable computational simulations to reveal previously concealed diagnostic information, and can predict treatment outcomes and patient trajectories.
The researchers explained at present, if two patients have reduced cardiac function and one shows improvement whilst the other does not, their therapy remains the same. KCL aims to monitor these patient trajectories using digital twins in order to include this information in patient care decisions.
Professor Niederer is to address the clinical gap by combining measurements of the shape and motion of the heart with physics-based and physiological constraints, to garner estimates on factors such as the material properties of the heart; stiffness; boundary conditions to the heart; and how it contracts. Through the digital twin, researchers will then be able to track how these material properties change through time as well as how shape and anatomy alters over time.
Professor Niederer’s project is to focus on two retrospective heart failure studies that recorded how patients with newly diagnosed heart failure responded to starting treatment, and how patients with heart failure in remission responded to changes in medication. Through analysis, he will assess whether longitudinal changes can be used to see how the heart is responding to medication, to better identify who improves and who does not.
“If we can characterise patients and swiftly identify what is causing their heart failure, then you can give them medication that will target the specific mechanism,” Professor Niederer said. “This allows clinicians to better explain conditions and treatment pathways and gives confidence in that decision.”
He added: “Ideally we will be able to use these models to make better forecasts about how patients respond to their therapy. We will be able to use these models to identify what was the primary cause of a patients cardiac disfunction and then we will be able to identify therapies that will both target their primary cause and their anticipated needs.”
