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.
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