Abstract 4147292: An ECG-based Heart Failure Screening Tool for People with Sickle Cell Disease

Background: Tissue hypoxia and chronic anemia associated with sickle cell disease (SCD) leads to structural and physiological alterations in the heart. Early detection of heart failure (HF) in patients with SCD can assist with timely interventions, but current methods (e.g., echocardiogram and heart MRI) are not easily accessible in resource-deprived settings. The integration of artificial intelligence (AI)-powered tools utilizing low-cost ECG data to increase the power to detect more patients eligible for early treatment, thus improving patient outcomes, and needs to be validated. Hypothesis: We hypothesize that ECG-AI models developed to detect incident HF in the general population can detect HF in SCD patients. Methods/Approach: We previously developed an ECG-AI model employing convolutional neural networks to classify patients with HF using a large ECG-repository at Wake Forest Baptist Health (WFBH). This model was developed using 1,078,198 digital ECGs from 165,243 patients, 73% White, 19% Black, and 52% female individuals, with a mean age (SD) of 58 (15) years. The hold-out AUC of this previous model in distinguishing ECGs of HF patients from controls was 0.87. In this study, we externally validated this ECG-AI model using SCD patients’ data from the University of Tennessee Health Science Center (UTHSC). Additionally, a logistic regression (LR) model was constructed in the UTHSC cohort by incorporating other simple demographic variables with the outcome of ECG-AI model. Results/Data: The UTHSC external validation cohort included data from 2,107 SCD patients (188 HF and 1,919 SCD patients with no HF), 98% were Black, 72% were female, with a mean age of 39 (14) years. Despite demographic differences between the validation (more Blacks) and derivation cohorts (lower age), our ECG-AI model accurately identified HF with an AUC of 0.80 (0.77-0.82) in the UTHSC SCD cohort. When incorporating ECG-AI outcome (an ECG-based risk value between 0 and 1), age, sex, and race in a LR model, the AUC significantly improved (DeLong Test, p<0.01) to 0.84 (0.82-0.87) with a sensitivity of 0.76 (0.73-0.79) and specificity of 0.76 (0.74-0.79). Conclusion: The ECG-AI model can detect HF in SCD patients from ECG data alone with moderately high accuracy. The accuracy improves with incorporation of simple demographic data. Future studies will incorporate other clinical risk factors of HF and sickle cell genotype data for further improved accuracy allowing its use for surveillance.