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Early Identification of Childhood Cancer Survivors at High Risk for Late Onset Cardiomyopathy: An Artificial Intelligence Approach utilizing Electrocardiography

Award Number: R01CA261834
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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Project Summary/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


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = $536,471 )
2024	2024	WAKE FOREST UNIVERSITY HEALTH SCIENCES	MEDICAL CENTER BLVD	WINSTON SALEM	NC	27157	FORSYTH	USA	Cancer Cause and Prevention Research	000	3	3/20/2024	NON-COMPETING CONTINUATION	$536,471
														Subtotal = $536,471

Issue Date FY: 2023 ( Subtotal = $487,727 )
2023	2023	WAKE FOREST UNIVERSITY HEALTH SCIENCES	MEDICAL CENTER BLVD	WINSTON SALEM	NC	27157	FORSYTH	USA	Cancer Cause and Prevention Research	000	2	3/25/2023	NON-COMPETING CONTINUATION	$477,973
2023	2023	WAKE FOREST UNIVERSITY HEALTH SCIENCES	MEDICAL CENTER BLVD	WINSTON SALEM	NC	27157	FORSYTH	USA	Cancer Cause and Prevention Research	001	2	9/13/2023	NON-COMPETING CONTINUATION	$9,754
														Subtotal = $487,727

Issue Date FY: 2022 ( Subtotal = $563,803 )
2022	2022	WAKE FOREST UNIVERSITY HEALTH SCIENCES	MEDICAL CENTER BLVD	WINSTON SALEM	NC	27157	FORSYTH	USA	Cancer Cause and Prevention Research	000	1	4/15/2022	NEW	$563,803
														Subtotal = $563,803

Grand Total All Awards = $1,588,001

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Early Identification of Childhood Cancer Survivors at High Risk for Late Onset Cardiomyopathy: An Artificial Intelligence Approach utilizing Electrocardiography

Award Number: R01CA261834

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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