Project description
Heart models to predict response to treatment
Heart failure (HF) and atrial fibrillation (AF) often emerge together in patients. Most patients receive heart rate control treatment as they tend to respond well, but decision mainly relies on empirical data observed once the therapy has been delivered. To identify patients that perform better following sinus rhythm control treatment, the EU-funded PREDICT-HF project proposes to develop patient-specific biophysical cardiac models that simulate HF–AF. Scientists will use these models to determine which condition (HF or AF) preceded the other and combined with patient history advise on optimal therapy selection for individual patients.
Objective
Heart failure (HF) and atrial fibrillation (AF) are common co-morbidities (AF-HF). AF-HF is prevalent in Europe with high rates of hospitalisation and death. AF-HF patients have two treatment options: rate control, where AF is not treated but drugs are used to slow the heart rate, or rhythm control, where AF is treated to restore sinus rhythm. Rate control is the first-line treatment, yet specific patient groups do much better under rhythm control. Identifying patients that will do best under rhythm control remains a significant clinical challenge.
Potential responders to rhythm control can be identified by their disease history, however, this is often unknown, or their response to treatment, which can only be observed once the therapy has been delivered. We propose to address these challenges by developing patient specific biophysical cardiac models to infer patient history and predict patient response to treatment to inform optimal therapy selection for individual patients.
A model for simulating AF-HF in human hearts, representing all four cardiac chambers, will be created. Bayesian uncertainty quantification techniques will be used to combine physical laws, physiology, population data and measurements from individual patients into cardiac models that account for data uncertainty in model parameters and simulation predictions.
Patient specific cardiac models will be used to answer three critical clinical questions in prospective studies. Models will be used to predict: if AF led to HF, or HF led to AF in AF-HF patients where the index disease is unknown, response to rhythm control therapy in AF-HF patients and in which AF-HF patients rate or rhythm control is best.
This proposal outlines an ambitious high risk/return program to address key technical challenges in bringing predictive patient specific models into clinical studies and will apply these innovative techniques to address important clinical questions on the treatment of patients suffering AF-HF.
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Programme(s)
Funding Scheme
ERC-COG - Consolidator GrantHost institution
SW7 2AZ LONDON
United Kingdom