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

Informazioni relative al progetto

PREDICT-HF

ID dell’accordo di sovvenzione: 864055

DOI

10.3030/864055

Data della firma CE 14 Maggio 2020

Data di avvio 1 Settembre 2020

Data di completamento 31 Agosto 2026

Finanziato da

• EXCELLENT SCIENCE - European Research Council (ERC)

Costo totale

€ 1 998 927,00

Contributo UE

€ 1 998 927,00

Coordinato da

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)

Periodo di rendicontazione: 2023-05-01 al 2024-10-31

Sintesi del contesto e degli obiettivi generali del progetto

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

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

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.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

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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