DOI: 10.1161/circ.148.suppl_1.16324 ISSN: 0009-7322

Abstract 16324: A Deep Learning Electrocardiogram Model Identifies Risk of Incident Heart Failure in Patients With Subclinical Hypothyroidism

Ryuichiro Yagi, Calum Macrae, Rahul C Deo, Shinichi Goto
  • Physiology (medical)
  • Cardiology and Cardiovascular Medicine

Introduction: Subclinical hypothyroidism (SH) is associated with an increased risk of heart failure (HF). However, there are no established measures to predict HF development within the SH population.

Hypothesis: Risk of incident heart failure in SH can be predicted from a 12-lead electrocardiogram using features extracted by a convolutional neural network-based model.

Methods: To test the hypothesis, we developed a convolutional neural network (CNN)-based model to detect ECG features linked with low thyroid function by training to discriminate the presence of hypothyroidism from 10,745 ECGs (2,693 from patients with low free T4 (FT4) and 8,052 from age, sex-matched patients without evidence of low FT4). The model was applied to baseline ECGs from an external cohort of 2,293 patients with SH (defined TSH >4.5mIU/L and FT4 within 0.9-1.9ng/dl) to estimate the cardiac phenotype induced by hypothyroidism (ECG-hypothyroidism score). Cumulative incidences of HF admission were compared between groups stratified by the tertile of the model output (low, intermediate, and high score groups). Adjusted hazard ratios (aHR) were calculated using Cox proportional hazard model.

Results: HF admission was more frequent in the high score group (9.60/100 person-year), compared with the intermediate or low score groups (5.08 and 2.29 per 100 person-year in the, respectively; Fig ). Subgroup analyses by sex and age group yielded similar results. The association between the ECG-hypothyroidism score and HF admission remained significant after adjustment for patient characteristics, conventional ECG abnormalities, and baseline thyroid function (high vs low score group: aHR 3.56, [95%CI, 2.26-5.61]; high vs intermediate score group: aHR 1.78, [95%CI, 1.25-2.54]).

Conclusions: The CNN-based model, trained to detect prevalent hypothyroidism, accurately identified patients at high risk for HF admission within the SH population using baseline ECG.

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