Data-driven optimization of therapy for heart failure

ABSTRACT
This collaborative project integrates concepts from engineering, artificial intelligence, computer modeling,
physiology, and clinical cardiology to explore new therapeutic strategies for patients who have heart failure. The
moonshot goal is a simulation framework that can predict how a patient's heart will grow and remodel during a
potential therapeutic intervention. Once the framework has been validated with patient data, it could be deployed
to compare the outcomes predicted for different treatments. A clinician could then use the predictions to guide
their choice of therapy.
This project seeks to advance computational cardiology and move the field closer to a randomized clinical trial
that tests whether patients treated with model-optimized therapies have better outcomes than patients who
received standard clinical care.
The multidisciplinary research team consists of 3 scientists (Ken Campbell, PhD; Jonathan Wenk, PhD; Lik-
Chuan Lee, PhD) and 2 cardiologists (Emma Birks, MD/PhD; Gaurang Vaidya, MD). Together, their skillsets
range from molecular biophysics, through computer modeling and engineering, to clinical care and Ventricular
Assist Devices.
The plan has 3 Aims:
1) Develop PyMyoVent as a testbed for implementing baroreflex control and myocardial growth.
2) Use MyoFE to create and validate patient-specific biventricular finite element models that incorporate
growth and functional remodeling.
3) Deploy personalized MyoFE models to predict optimal therapies for patients who have heart failure.
The plan is highly innovative reward and makes intelligent use of clinical data collected as part of normal care
from 100 patients who are enrolled in a research registry at the University of Kentucky. These data will include
pressure signals transmitted wirelessly from patients who have had a CardioMEMS device inserted around their
pulmonary artery. Fundamental contributions include the creation of finite element models that are controlled by
a baroreflex and grow and adapt in response to physiological signals including myofilament stress and cellular
energy use.

Grant Number: 5R01HL163977-04
NIH Institute/Center: NIH
Principal Investigator: Kenneth Campbell