Background: Hyperthyroidism is frequently under-recognized and leads to heart failure and mortality. Timely identification of high-risk patients is a prerequisite to effective antithyroid therapy. Since the heart is very sensitive to hyperthyroidism and its electrical signature can be demonstrated by electrocardiography, we developed an artificial intelligence model to detect hyperthyroidism by electrocardiography and examined its potential for outcome prediction.
Methods: The deep learning model was trained using a large dataset of 47,245 electrocardiograms from 33,246 patients at an academic medical center. Patients were included if electrocardiograms and measurements of serum thyroid-stimulating hormone were available that had been obtained within a three day period. Serum thyroid-stimulating hormone and free thyroxine were used to define overt and subclinical hyperthyroidism. We tested the model internally using 14,420 patients and externally using two additional test sets comprising 11,498 and 596 patients, respectively.
Results: The performance of the deep learning model achieves areas under the receiver operating characteristic curves (AUCs) of 0.725-0.761 for hyperthyroidism detection, AUCs of 0.867-0.876 for overt hyperthyroidism, and AUC of 0.631-0.701 for subclinical hyperthyroidism, superior to a traditional features-based machine learning model. Patients identified as hyperthyroidism-positive by the deep learning model have a significantly higher risk (1.97-2.94 fold) of all-cause mortality and new-onset heart failure compared to hyperthyroidism-negative patients. This cardiovascular disease stratification is particularly pronounced in subclinical hyperthyroidism, surpassing that observed in overt hyperthyroidism.
Conclusions: An innovative algorithm effectively identifies overt and subclinical hyperthyroidism and contributes to cardiovascular risk assessment.
Hyperthyroidism occurs when the thyroid gland produces too much hormone and can cause various symptoms including faster heartbeat, weight loss, and nervousness. Diagnosis is often missed, which can lead to heart problems and even death. Measurements of the heart’s electrical activity can be obtained using Electrocardiograms (ECGs). We made a computational model that can detect hyperthyroidism from ECGs. Our model was better able to identify people with hyperthyroidism than currently available methods, especially the more severe forms of the condition. If future work demonstrates our model is safe and accurate, it could potentially be used to detect hyperthyroidism sooner, enabling faster treatment and improved health of people with hyperthyroidism.
© 2024. The Author(s).