Early identification of childhood cancer survivors at high risk for late onset cardiomyopathy: an artificial intelligence approach utilizing electrocardiography

Active Project Grant Awarded Last action

Award ID
R01CA261834
Recipient
Awarding agency
Obligated
$4,639,332

Award facts

Assistance listing
93.393 Cancer Cause and Prevention Research
Completion date 91% complete
Originally
Place of performance
Winston-Salem, NC 27157, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

Abstract
Due to improved treatment and supportive care, five-year survival rates for childhood cancer now exceed 85%. However, patients treated with anthracycline chemotherapy or chest-directed radiation have a dose-related risk for adverse cardiovascular sequelae, including cardiomyopathy, coronary artery disease and valvular heart disease, with a negative impact on quality of life and overall survival. Earlier recognition and interventions to manage cardiac morbidity among childhood cancer survivors (CCS) could provide opportunities to improve quality of remaining life. To facilitate early detection of cardiomyopathy, the children's oncology group's guidelines recommend life-long screening of CCS with echocardiography (echo) every 2 to 5 years. While offering an opportunity for early detection of myocardial dysfunction, screening guidelines do not identify patients with preserved systolic function who may develop cardiomyopathy in the future. Our overarching long-term goal is to develop a generalizable artificial intelligence (AI)-tool using ECG tracings that can identify CCS at high risk for future cardiomyopathy. We have shown on a subset of ST. Jude lifetime cohort (sjlife) study data that CCS at high risk for cardiomyopathy withing 10 years can be predicted with high accuracy (AUC of 0.87) via artificial intelligence (AI) using raw digital electrocardiography (ECG) data only. Our goal in this project is to develop a robust (aim 1), generalizable (aim 2), and remotely applicable (aim 3) AI-tool that can identify CCS at cardiomyopathy risk from low-cost and highly-accessible ECG data. We will achieve our goal by following three specific aims: aim 1. Develop an AI tool to predict risk of future cardiomyopathy among CCS: we will utilize data from 3,731 sjlife participants to refine and internally validate a novel AI-tool predicting CCS at high risk for cardiomyopathy (defined as ejection fraction < 50% or >10% drop), in the subsequent 3, 5, and 10 years. We will use signal processing and deep learning to generate features representing ecgs and use these features in machine learning to predict cardiomyopathy. Aim 2. Perform an external validation of the AI tool on a subgroup of the amsterdam later cohort. We will externally validate our AI-tool on 343 CCS treated for childhood cancer at the emma children's hospital/academic medical center in netherland. We will assess the concordance of the AI-tool performance on the later cohort VS hold out test cohort at sjlife. Aim 3. Evaluate the feasibility of remote cardiomyopathy prediction via smartwatch. We will collect ecgs on a subset of sjlife participants via a smartwatch during their routine exam and assess the. Concordance of risk predictions by AI-tool using smartwatch ECG VS clinical ECG. Impact: our results offer the potential to positively impact CCS health by 1) identifying those who may benefit from more frequent or advanced cardiac imaging, and 2) guiding future studies in remote and real time prediction of late-onset cardiomyopathy. 0

Transactions

Action Obligated Date
Amendment $0
Amendment $38,234
Amendment $15,185
Amendment $15,185
Amendment $38,234
Amendment $480,774
Amendment $480,774
Amendment $480,774
Amendment $536,471
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Amendment $9,754
Amendment $9,754
Amendment $477,973
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Original award $563,803
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Subawards

Subrecipient Amount Awarded
University Of Tennessee Dr. Davis will be responsible for study design, analysis and interpretation of study data. He will also participate in weekly meetings to discuss study progress… $37,948
University Of Tennessee $43,164
Prinses Maxima Centrum Voor Kinderoncologie B.V. The provision of validation cohort data for the project "Early Identification of Childhood Cancer Survivors at High Risk for Late Onset Cardiomyopathy: An… $38,234

Quick answers

Who received award R01CA261834?

R01CA261834 was awarded to Wake Forest University Health Sciences by National Cancer Institute (National Institutes of Health) on April 15, 2022.

How much is award R01CA261834 worth?

$4,639,332 has been obligated on R01CA261834.

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