R01HL153390
Project Grant
Overview
Grant Description
Factors Associated with Response to Cardiac Resynchronization Therapy in Heart Failure Patients with Non-LBBB ECG Pattern
PI: Valentina Kutyifa, MD, PhD
University of Rochester Medical Center, Rochester, NY
Morbidity, mortality, and healthcare costs associated with the treatment of systolic heart failure (HF) are rapidly increasing. Cardiac resynchronization therapy with a defibrillator (CRT-D) has been shown to effectively reduce HF events and death in HF patients with a wide QRS and low ejection fraction, making it a cost-effective treatment option. However, not all patients respond unequivocally to CRT-D, and those without the presence of an ECG abnormality, specifically left bundle branch block (non-LBBB), pose a significant treatment challenge.
Due to conflicting and limited data on response to CRT-D in this cohort, it is possible that a large proportion of HF patients with non-LBBB are currently being treated with limited or no benefit from the device. Therefore, there is a need for better selection of patients for this expensive therapy. One of our recent studies suggested a clinical benefit in patients with non-LBBB and marked echocardiography response, and identified predictors. However, there is a need to prospectively validate these predictors of echocardiography response to CRT-D in non-LBBB in this hard-to-treat patient population, and identify potential novel ECG and echocardiography predictors, utilizing novel statistical methods of machine learning.
We propose a prospective, observational, single-arm study in a currently guideline-indicated cohort to validate and identify predictors of echocardiography response to CRT-D, including novel ECG and echocardiography markers, and to assess subsequent clinical outcomes in 270 HF patients with an implanted CRT-D and non-LBBB ECG pattern. The primary aim of the study is to prospectively validate our previously identified clinical predictors of echocardiography response to CRT-D in HF patients with non-LBBB that could enable better patient selection. Our secondary aim is to identify the incremental value of novel ECG and echocardiography variables to predict echocardiography response to CRT-D in non-LBBB patients, including ECG variables of sum absolute QRST integral and ventricular electrical activation delay, and echocardiography-derived variables of left ventricular dyssynchrony and contractility.
Then, we will apply the developed predictive model to prospectively identify non-LBBB patients with CRT-D at risk of heart failure, ventricular arrhythmias, or death. The tertiary aim is to identify novel ECG and echocardiography predictors of response in non-LBBB using machine learning analysis. The study population will include 270 HF patients with non-LBBB (135 with mild HF and 135 with advanced HF) and an implanted CRT-D with 6 months echocardiography follow-up analyzed by an echocardiography core lab, and assessing clinical outcomes of heart failure, ventricular arrhythmias, or death.
PI: Valentina Kutyifa, MD, PhD
University of Rochester Medical Center, Rochester, NY
Morbidity, mortality, and healthcare costs associated with the treatment of systolic heart failure (HF) are rapidly increasing. Cardiac resynchronization therapy with a defibrillator (CRT-D) has been shown to effectively reduce HF events and death in HF patients with a wide QRS and low ejection fraction, making it a cost-effective treatment option. However, not all patients respond unequivocally to CRT-D, and those without the presence of an ECG abnormality, specifically left bundle branch block (non-LBBB), pose a significant treatment challenge.
Due to conflicting and limited data on response to CRT-D in this cohort, it is possible that a large proportion of HF patients with non-LBBB are currently being treated with limited or no benefit from the device. Therefore, there is a need for better selection of patients for this expensive therapy. One of our recent studies suggested a clinical benefit in patients with non-LBBB and marked echocardiography response, and identified predictors. However, there is a need to prospectively validate these predictors of echocardiography response to CRT-D in non-LBBB in this hard-to-treat patient population, and identify potential novel ECG and echocardiography predictors, utilizing novel statistical methods of machine learning.
We propose a prospective, observational, single-arm study in a currently guideline-indicated cohort to validate and identify predictors of echocardiography response to CRT-D, including novel ECG and echocardiography markers, and to assess subsequent clinical outcomes in 270 HF patients with an implanted CRT-D and non-LBBB ECG pattern. The primary aim of the study is to prospectively validate our previously identified clinical predictors of echocardiography response to CRT-D in HF patients with non-LBBB that could enable better patient selection. Our secondary aim is to identify the incremental value of novel ECG and echocardiography variables to predict echocardiography response to CRT-D in non-LBBB patients, including ECG variables of sum absolute QRST integral and ventricular electrical activation delay, and echocardiography-derived variables of left ventricular dyssynchrony and contractility.
Then, we will apply the developed predictive model to prospectively identify non-LBBB patients with CRT-D at risk of heart failure, ventricular arrhythmias, or death. The tertiary aim is to identify novel ECG and echocardiography predictors of response in non-LBBB using machine learning analysis. The study population will include 270 HF patients with non-LBBB (135 with mild HF and 135 with advanced HF) and an implanted CRT-D with 6 months echocardiography follow-up analyzed by an echocardiography core lab, and assessing clinical outcomes of heart failure, ventricular arrhythmias, or death.
Awardee
Funding Goals
TO FOSTER HEART AND VASCULAR RESEARCH IN THE BASIC, TRANSLATIONAL, CLINICAL AND POPULATION SCIENCES, AND TO FOSTER TRAINING TO BUILD TALENTED YOUNG INVESTIGATORS IN THESE AREAS, FUNDED THROUGH COMPETITIVE RESEARCH TRAINING GRANTS. SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM: TO STIMULATE TECHNOLOGICAL INNOVATION, USE SMALL BUSINESS TO MEET FEDERAL RESEARCH AND DEVELOPMENT NEEDS, FOSTER AND ENCOURAGE PARTICIPATION IN INNOVATION AND ENTREPRENEURSHIP BY SOCIALLY AND ECONOMICALLY DISADVANTAGED PERSONS, AND INCREASE PRIVATE-SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT FUNDING. SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM: TO STIMULATE TECHNOLOGICAL INNOVATION, FOSTER TECHNOLOGY TRANSFER THROUGH COOPERATIVE R&D BETWEEN SMALL BUSINESSES AND RESEARCH INSTITUTIONS, AND INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL R&D.
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Rochester,
New York
146113847
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 365% from $734,112 to $3,413,377.
University Of Rochester was awarded
Predictors of CRT-D Response in Non-LBBB HF Patients
Project Grant R01HL153390
worth $3,413,377
from National Heart Lung and Blood Institute in July 2021 with work to be completed primarily in Rochester New York United States.
The grant
has a duration of 5 years and
was awarded through assistance program 93.837 Cardiovascular Diseases Research.
The Project Grant was awarded through grant opportunity NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed).
Status
(Ongoing)
Last Modified 6/20/25
Period of Performance
7/8/21
Start Date
6/30/26
End Date
Funding Split
$3.4M
Federal Obligation
$0.0
Non-Federal Obligation
$3.4M
Total Obligated
Activity Timeline
Subgrant Awards
Disclosed subgrants for R01HL153390
Transaction History
Modifications to R01HL153390
Additional Detail
Award ID FAIN
R01HL153390
SAI Number
R01HL153390-3542176637
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Private Institution Of Higher Education
Awarding Office
75NH00 NIH National Heart, Lung, and Blood Institute
Funding Office
75NH00 NIH National Heart, Lung, and Blood Institute
Awardee UEI
F27KDXZMF9Y8
Awardee CAGE
03CZ7
Performance District
NY-25
Senators
Kirsten Gillibrand
Charles Schumer
Charles Schumer
Budget Funding
Federal Account | Budget Subfunction | Object Class | Total | Percentage |
---|---|---|---|---|
National Heart, Lung, and Blood Institute, National Institutes of Health, Health and Human Services (075-0872) | Health research and training | Grants, subsidies, and contributions (41.0) | $1,392,109 | 100% |
Modified: 6/20/25