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使用智能手机心电图分析应用程序筛查右心室功能障碍:急性肺栓塞患者的验证研究

Screening for RV Dysfunction Using Smartphone ECG Analysis App: Validation Study with Acute Pulmonary Embolism Patients.

作者信息

Choi Yoo Jin, Park Min Ji, Cho Youngjin, Kim Joonghee, Lee Eunkyoung, Son Dahyeon, Kim Seo-Yoon, Soh Moon Seung

机构信息

Department of Emergency Medicine, Ajou University School of Medicine, 164, World cup-ro, Yeongtong-gu, Suwon-si 16499, Gyeonggi-do, Republic of Korea.

Cardiovascular Center, Department of Internal Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si 13620, Gyeonggi-do, Republic of Korea.

出版信息

J Clin Med. 2024 Aug 14;13(16):4792. doi: 10.3390/jcm13164792.

Abstract

: Acute pulmonary embolism (PE) is a critical condition where the timely and accurate assessment of right ventricular (RV) dysfunction is important for patient management. Given the limited availability of echocardiography in emergency departments (EDs), an artificial intelligence (AI) application that can identify RV dysfunction from electrocardiograms (ECGs) could improve the treatment of acute PE. : This retrospective study analyzed adult acute PE patients in an ED from January 2021 to December 2023. We evaluated a smartphone application which analyzes printed ECGs to generate digital biomarkers for various conditions, including RV dysfunction (QCG-RVDys). The biomarker's performance was compared with that of cardiologists and emergency physicians. : Among 116 included patients, 35 (30.2%) were diagnosed with RV dysfunction. The QCG-RVDys score demonstrated significant effectiveness in identifying RV dysfunction, with a receiver operating characteristic-area under the curve (AUC) of 0.895 (95% CI, 0.829-0.960), surpassing traditional biomarkers such as Troponin I (AUC: 0.692, 95% CI: 0.536-0.847) and ProBNP (AUC: 0.655, 95% CI: 0.532-0.778). Binarized based on the Youden Index, QCG-RVDys achieved an AUC of 0.845 (95% CI: 0.778-0.911), with a sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 91.2% (95% CI: 82.4-100%), 77.8% (95% CI: 69.1-86.4%), 63.3% (95% CI: 54.4-73.9%), and 95.5% (95% CI: 90.8-100%), respectively, significantly outperforming all the expert clinicians, with their AUCs ranging from 0.628 to 0.683. : The application demonstrates promise in rapidly assessing RV dysfunction in acute PE patients. Its high NPV could streamline patient management, potentially reducing the reliance on echocardiography in emergency settings.

摘要

急性肺栓塞(PE)是一种危急病症,及时准确评估右心室(RV)功能障碍对患者管理至关重要。鉴于急诊科(ED)超声心动图的可用性有限,一种能够从心电图(ECG)识别RV功能障碍的人工智能(AI)应用程序可以改善急性PE的治疗。

这项回顾性研究分析了2021年1月至2023年12月在急诊科的成年急性PE患者。我们评估了一款智能手机应用程序,该程序分析打印的心电图以生成针对各种病症的数字生物标志物,包括RV功能障碍(QCG-RVDys)。将该生物标志物的性能与心脏病专家和急诊科医生的性能进行了比较。

在纳入的116例患者中,35例(30.2%)被诊断为RV功能障碍。QCG-RVDys评分在识别RV功能障碍方面显示出显著效果,曲线下面积(AUC)为0.895(95%CI,0.829-0.960),超过了肌钙蛋白I(AUC:0.692,95%CI:0.536-0.847)和脑钠肽前体(ProBNP)(AUC:0.655,95%CI:0.532-0.778)等传统生物标志物。基于约登指数进行二值化后,QCG-RVDys的AUC为0.845(95%CI:0.778-0.911),敏感性、特异性、阳性预测值(PPV)和阴性预测值(NPV)分别为91.2%(95%CI:82.4-100%)、77.8%(95%CI:69.1-86.4%)、63.3%(95%CI:,54.4-73.9%)和95.5%(95%CI:90.8-100%),明显优于所有专家临床医生,他们的AUC范围为0.628至0.683。

该应用程序在快速评估急性PE患者的RV功能障碍方面显示出前景。其高NPV可以简化患者管理,有可能减少急诊情况下对超声心动图的依赖。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ade8/11355826/2df7565740e3/jcm-13-04792-g001.jpg

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