In JACC. Asia
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.
Liu Chih-Min, Shih Edward S C, Chen Jhih-Yu, Huang Chih-Han, Wu I-Chien, Chen Pei-Fen, Higa Satoshi, Yagi Nobumori, Hu Yu-Feng, Hwang Ming-Jing, Chen Shih-Ann
2022-Jun
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
