Predicting Outcome of Rate or Rhythm Control in Patients with Atrial Fibrillation and Heart Failure

Informacje na temat projektu

PREDICT-HF

Identyfikator umowy o grant: 864055

DOI

10.3030/864055

Data podpisania przez KE 14 Maja 2020

Data rozpoczęcia 1 Września 2020

Data zakończenia 31 Sierpnia 2026

Finansowanie w ramach

• EXCELLENT SCIENCE - European Research Council (ERC)

Koszt całkowity

€ 1 998 927,00

Wkład UE

€ 1 998 927,00

Koordynowany przez

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
United Kingdom

Periodic Reporting for period 3 - PREDICT-HF (Predicting Outcome of Rate or Rhythm Control in Patients with Atrial Fibrillation and Heart Failure)

Okres sprawozdawczy: 2023-05-01 do 2024-10-31

Podsumowanie kontekstu i ogólnych celów projektu

Heart failure and atrial fibrillation are common comorbidities. The treatment of each disease is both informed and restricted by the other. In heart failure patients atrial fibrillation can be treated by rhythm control, where the aim is to return the heart back to sinus rhythm, or rate control, where the atria are left in fibrillation its effects are mitigated. In this study, we aim to investigate if we can use patient specific models to predict in which patients' rate control or rhythm control have the best impact on heart function.

As we age the number of comorbidities experienced by patients increases. Patient-specific models provide a framework for testing multiple therapies and predicting how each therapy impacts each disease. This opens the door to optimizing treatments across patient complex and potentially multiple pathologies.

The overall objectives of this study are to build robust and rapid patient-specific models to predict the impact of sinus rhythm on cardiac function and apply these in two prospective clinical trials on patients with heart failure and atrial fibrillation

Prace wykonane od początku projektu do końca okresu sprawozdawczego oraz najważniejsze dotychczasowe rezultaty

At this point in the project, we have developed multiple techniques and tools that allow us to efficiently simulate the dynamics of the heart, analyze cardiac images, and estimate parameters while considering uncertainty. Additionally, we have created a new representation of four-chamber heart models, proposed methods for calibrating models, and developed a process for performing multi-scale sensitivity analysis.

We have also begun the first trial called RHYTHMIC-CRT, where we will gather data to determine whether patient-specific models and simulations can predict the response of heart failure patients with atrial fibrillation to either a rate or rhythm control strategy.

The second trial called PRoAF-HF, where we will test if the order that co-morbidities developed (did atrial fibrillation proceed heart failure or the other way round), impacts patient outcomes to rhythm control is under review, and will start collecting data shortly.

Overall PREDICT-HF is designed to ultimately reveal whether a rate or rhythm control strategy is optimal for heart failure patients with cardiac resynchronization therapy, which approach is better for patients who developed atrial fibrillation before their heart failure, and how we can use modeling and simulation to predict patient response to rate or rhythm control therapies.

Innowacyjność oraz oczekiwany potencjalny wpływ (w tym dotychczasowe znaczenie społeczno-gospodarcze i szersze implikacje społeczne projektu)

We have created the first human atrial fibre atlas, we have created a four-chamber heart modelling simulation framework, we have applied this framework to create patient cohorts and we have performed the first systematic multi-scale sensitivity analysis of a human heart. All of these move cardiac modeling far beyond the state of the art, at the start of the project.

We have published the first global sensitivity analysis for a four-chamber heart model, we have developed and are now testing a model calibration pipeline

We are currently collecting the most comprehensive set of biomechanics data ever collected in a patient cohort and have specifically included a collection of training and validation data in the study protocol. This will provide a data set that allows a true test of the ability to create and validate patient-specific models.

We have started developing workflows to analyse this data at scale

We will be able to use these models to test if we can predict the acute and sustained response to rate or rhythm control in heart failure patients. This will provide a demonstration of how models can be explicitly used to select patient therapy based on predicted response.

We will also complete two clinical trials that will answer explicit clinical questions:
1) Do CRT patients respond best to rate or rhythm control
2) Does patient response to rhythm control depend on their disease pathway.

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