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Abstract: PO1137

Artificial Intelligence-Assisted Electrocardiography for Early Diagnosis of Thyrotoxic Periodic Paralysis

Session Information

Category: Fluid, Electrolyte, and Acid-Base Disorders

  • 902 Fluid, Electrolyte, and Acid-Base Disorders: Clinical

Authors

  • Lin, Shih-Hua P., Tri-Service General Hospital, Taipei, Taiwan
  • Sung, Chih-Chien, Tri-Service General Hospital, Taipei, Taiwan
  • Tseng, Min-hua, Chang Gung Memorial Hospital, Tao-yang, Taiwan
Background

Thyrotoxic periodic paralysis (TPP) characterized by acute weakness, hypokalemia and hyperthyroidism is a medical emergency with a great challenge in early diagnosis since most TPP patients do not have overt symptoms. Since both hypokalemia and hyperthyroidism in TPP significantly affect the cardiovascular system, electrocardiography (ECG) as a prompt and non-invasive bedside tool universally used in the ED may detect these electrical changes.To assess artificial intelligence (AI)-assisted electrocardiography (ECG) combined with routine laboratory data in the early diagnosis of TPP.

Methods

A deep learning model (DLM) based on ECG12Net, an 82-layer convolutional neural network, was constructed to detect hypokalemia and hyperthyroidism. The development cohort consisted of 39 ECGs from patients with TPP and 502 ECGs of hypokalemic control; the validation cohort consisted of 11 ECGs of TPP and 36 ECGs of non-TPP with weakness. The AI-ECG based TPP diagnostic process was then consecutively evaluated in 22 male patients with TTP-like features.

Results

In the validation cohort, the DLM-based ECG system detected all cases of hypokalemia in TPP patients with a mean absolute error of 0.26 mEq/L and diagnosed TPP with an area under curve (AUC) of ~80%, surpassing the best standard ECG parameter (AUC=0.7285 for the QR interval). Combining the AI predictions with the estimated glomerular filtration rate (eGFR) and serum chloride (Cl-) boosted the diagnostic accuracy of the algorithm to AUC 0.986. In the prospective study, our AI ECG system achieved perfect performance (F-measure 100%) on the task of hypokalemia detection in them and the integrated AI with routine laboratory had a PPV of 100% and F-measure 87.5% for TPP diagnosis.

Conclusion

An AI-ECG system reliably identifies hypokalemia in patients with paralysis and its integration with routine blood chemistries provides valuable decision support for the early diagnosis of TPP to avoid life-treatening complication.