Lin Chin, Kuo Feng-Chih, Chau Tom, Shih Jui-Hu, Lin Chin-Sheng, Chen Chien-Chou, Lee Chia-Cheng, Lin Shih-Hua
School of Medicine, National Defense Medical Center, Taipei, Taiwan ROC.
Graduate Institute of Aerospace and Undersea Medicine, National Defense Medical Center, Taipei, Taiwan ROC.
Commun Med (Lond). 2024 Mar 12;4(1):42. doi: 10.1038/s43856-024-00472-4.
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.
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.
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.
An innovative algorithm effectively identifies overt and subclinical hyperthyroidism and contributes to cardiovascular risk assessment.
甲状腺功能亢进症常常未得到充分认识，会导致心力衰竭和死亡。及时识别高危患者是有效进行抗甲状腺治疗的前提。由于心脏对甲状腺功能亢进症非常敏感，且其电信号特征可通过心电图显示，我们开发了一种人工智能模型，用于通过心电图检测甲状腺功能亢进症，并检验其预测预后的潜力。
使用来自一家学术医疗中心的33246例患者的47245份心电图的大型数据集对深度学习模型进行训练。如果在三天内获得了心电图和血清促甲状腺激素测量值，则将患者纳入研究。血清促甲状腺激素和游离甲状腺素用于定义显性和亚临床甲状腺功能亢进症。我们在内部使用14420例患者对模型进行测试，在外部使用另外两个分别包含11498例和596例患者的测试集进行测试。
深度学习模型在检测甲状腺功能亢进症方面的受试者操作特征曲线下面积（AUC）为0.725 - 0.761，在检测显性甲状腺功能亢进症方面的AUC为0.867 - 0.876，在检测亚临床甲状腺功能亢进症方面的AUC为0.631 - 0.701，优于传统的基于特征的机器学习模型。与甲状腺功能亢进症阴性患者相比，被深度学习模型识别为甲状腺功能亢进症阳性的患者全因死亡率和新发心力衰竭的风险显著更高（1.97 - 2.94倍）。这种心血管疾病分层在亚临床甲状腺功能亢进症中尤为明显，超过了显性甲状腺功能亢进症中的观察结果。
一种创新算法可有效识别显性和亚临床甲状腺功能亢进症，并有助于心血管风险评估。
