Background
Chagas cardiomyopathy is a frequent and severe manifestation of Chagas disease (CD) and it is a leading cause of morbidity and death in South America. The dilated cardiomyopathy in CD is often discovered only when patients present with symptomatic heart failure. We developed an artificial intelligence electrocardiogram (AI-ECG) based algorithm for the detection of left ventricular systolic dysfunction (LVSD) using a deep convolutional neural network that may be an important tool to screen for LVSD in patients with CD, especially in limited-resource settings. In this study, we validated the AI-ECG algorithm for the first time in a sample of subjects with CD in Brazil.
Methods
We studied a sample of CD patients from the second visit of the Sami-Trop longitudinal study, NIH sponsored cohort that aims to study the natural history of Chagas cardiomyopathy in an endemic region of Brazil. Chagas cardiomyopathy was defined by the presence of major ECG abnormalities using the Minnesota code. ECGs were resampled to 500Hz and were evaluated using the algorithm; as each patient had 3 ECG recordings the average score was used for analysis. We used the area under the receiver operating curve (AUC) to evaluate the algorithm and estimated sensitivity, specificity, and accuracy for the detection of EF<=40% and EF<=35%.
Results
We included 1,437 subjects who had an ECG and echocardiogram, 1330 with CD. 958 were female (66.7%%), the mean age was 60.6 years, 839 (58.4%) had Chagas cardiomyopathy, and 99 patients (6.9%) had EF<=40%. For the detection of EF<=40%, the AI-ECG had an AUC of 0.813, with sensitivity 83.8%, specificity 54.3%, and a negative predictive value of 97.8% using the originally calculated threshold. The sensitivity was 72.7%, specificity 78.9%, negative predictive value 97.5%, positive predictive value 13%, and accuracy 77.3 using an optimal threshold based on the ROC. The AUC was 0.818 for detection of ejection fraction <=35% and 0.805 for the detection of EF<50%.
Conclusion
The AI-augmented ECG can facilitate the screening and monitoring of patients with Chagas disease for early detection of LVSD to enable early treatment and to optimize the use of other resources like echocardiography.