國家衛生研究院 NHRI:Item 3990099045/14410
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    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/14410


    Title: Artificial intelligence-enabled electrocardiogram improves the diagnosis and prediction of mortality in patients with pulmonary hypertension
    Authors: Liu, CM;Shih, ESC;Chen, JY;Huang, CH;Wu, IC;Chen, PF;Higa, S;Yagi, N;Hu, YF;Hwang, MJ;Chen, SA
    Contributors: Institute of Population Health Sciences
    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.
    Date: 2022-06
    Relation: JACC: Asia. 2022 Jun;2(3, Part 1):258-270.
    Link to: http://dx.doi.org/10.1016/j.jacasi.2022.02.008
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85132135600
    Appears in Collections:[I-Chien Wu] Periodical Articles

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