Abstract Body (Do not enter title and authors here): Background
There are delays on identification of people with low left ventricular ejection fraction (LVEF) and Heart Failure with preserved EF (HFpEF). There is a need for low cost and accessible tools to identify individuals who can benefit from more comprehensive exams such as echocardiogram (ECHO).
Research Goal
To develop electrocardiographic artificial intelligence (ECG-AI) models that can classify low EF and HFpEF.
Methods
We developed an ECG-AI model using convolutional neural networks to classify 12-lead ECG’s into four categories: rEF( LVEF<40), mEF (40≤LVEF<50), HFpEF (clinical HF diagnosis with EF≥50) and controls with no HF diagnosis within 5-years of the index ECG. For rEF, mEF, and HFpEF categories, ECHO were used to determine LVEF. Patients with clinical HF diagnosis without ECHO data were excluded. In patients with ECHO, only available ECGs within 30-days of the ECHO study was utilized. The ECG-AI models were trained on ~80%, validated on ~10%, and tested on the remaining 10% hold out data from Wake Forest Baptist Health (WFBH) ECG repository and externally validated using data from the University of Tennessee Health Science Center (UTHSC).
Results
The ECG-AI model was developed using 1,078,198 digital ECGs, 114,068 ECHO derived LVEF values, and EHR data from 165,243 patients (73% White, 19% Black, 52% female, with a mean age (SD) of 58(15) years). There were 32,962, 40,997, 11,037, and 993,202 ECGs from 8,555 rEF, 13,116 mEF, 3,704 HFpEF, and 139,868 control patients, respectively. The ECG-AI achieved a hold-out AUC of 0.76 (0.74-0.77) in classifying ECGs as HFpEF or not (Table 1). The UTHSC external validation cohort included 273 rEF, 167 mEF, 459 HFpEF, and 35,254 control patients, with 35% White, 62% Black, 60% female, mean age of 51(18) years. Our model identified HFpEF patients with an AUC of 0.75 (0.73-0.76) in the UTHSC cohort.
Conclusion
ECG data alone result in moderate, moderately high, and very high accuracies in classifying HFpEF, mEF, and rEF, respectively. This could guide future models with incorporation of simple patient demographics and comorbidities to improve accuracy. Such ECG-AI models can assist with identifying who may benefit from further clinical evaluation and imaging studies.