Artificial Intelligence-Enabled Electrocardiogram Improves the Diagnosis and Prediction of Mortality in Patients With Pulmonary Hypertension

Chih-Min Liu; Edward S C Shih; Jhih-Yu Chen; Chih-Han Huang; I-Chien Wu; Pei-Fen Chen; Satoshi Higa; Nobumori Yagi; Yu-Feng Hu; Ming-Jing Hwang; Shih-Ann Chen

doi:10.1016/j.jacasi.2022.02.008

Artificial Intelligence-Enabled Electrocardiogram Improves the Diagnosis and Prediction of Mortality in Patients With Pulmonary Hypertension

JACC Asia. 2022 May 17;2(3):258-270. doi: 10.1016/j.jacasi.2022.02.008. eCollection 2022 Jun.

Authors

Chih-Min Liu^{1

2}, Edward S C Shih³, Jhih-Yu Chen³, Chih-Han Huang^{3

4}, I-Chien Wu⁵, Pei-Fen Chen⁵, Satoshi Higa⁶, Nobumori Yagi⁷, Yu-Feng Hu^{1

2

3}, Ming-Jing Hwang^{3

4}, Shih-Ann Chen^{1

2

8}

Affiliations

¹ Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
² Institute of Clinical Medicine and Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
³ Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
⁴ Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei, Taiwan.
⁵ Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan.
⁶ Cardiac Electrophysiology and Pacing Laboratory, Division of Cardiovascular Medicine, Makiminato Central Hospital, Okinawa, Japan.
⁷ Division of Cardiovascular Medicine, Nakagami Hospital, Okinawa, Japan.
⁸ Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan.

Abstract

Background: Pulmonary hypertension is a disabling and life-threatening cardiovascular disease. Early detection of elevated pulmonary artery pressure (ePAP) is needed for prompt diagnosis and treatment to avoid detrimental consequences of pulmonary hypertension.

Objectives: This study sought to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify patients with ePAP and related prognostic implications.

Methods: From a hospital-based ECG database, the authors extracted the first pairs of ECG and transthoracic echocardiography taken within 2 weeks of each other from 41,097 patients to develop an AI model for detecting ePAP (PAP > 50 mm Hg by transthoracic echocardiography). The model was evaluated on independent data sets, including an external cohort of patients from Japan.

Results: Tests of 10-fold cross-validation neural-network deep learning showed that the area under the receiver-operating characteristic curve of the AI model was 0.88 (sensitivity 81.0%; specificity 79.6%) for detecting ePAP. The diagnostic performance was consistent across age, sex, and various comorbidities (diagnostic odds ratio >8 for most factors examined). At 6-year follow-up, the patients predicted by the AI model to have ePAP were independently associated with higher cardiovascular mortality (HR: 3.69). Similar diagnostic performance and prediction for cardiovascular mortality could be replicated in the external cohort.

Conclusions: The ECG-based AI model identified patients with ePAP and predicted their future risk for cardiovascular mortality. This model could serve as a useful clinical test to identify patients with pulmonary hypertension so that treatment can be initiated early to improve their survival prognosis.

Keywords: AI, artificial intelligence; AIC, Akaike Information Criterion; AUC, area under the curve; ECG, electrocardiogram; PAH, pulmonary arterial hypertension; PAP, pulmonary artery pressure; PH, pulmonary hypertension; TTE, transthoracic echocardiography; all-cause mortality; artificial intelligence; cardiovascular mortality; deep learning; ePAP, elevated pulmonary artery pressure; electrocardiogram; pulmonary hypertension.