{"id":22879,"date":"2022-10-21T04:15:00","date_gmt":"2022-10-20T20:15:00","guid":{"rendered":"http:\/\/csccm.org.cn\/?p=22879"},"modified":"2022-10-21T07:35:28","modified_gmt":"2022-10-20T23:35:28","slug":"lancet%e5%8f%91%e8%a1%a8%e8%ae%ba%e6%96%87%ef%bc%9a%e7%aa%a6%e6%80%a7%e5%bf%83%e5%be%8b%e6%9c%9f%e9%97%b4%e4%ba%ba%e5%b7%a5%e6%99%ba%e8%83%bd%e6%8c%87%e5%af%bc%e7%9a%84%e5%bf%83%e7%94%b5%e5%9b%be","status":"publish","type":"post","link":"https:\/\/csccm.org.cn\/?p=22879","title":{"rendered":"[Lancet\u53d1\u8868\u8bba\u6587]\uff1a\u7aa6\u6027\u5fc3\u5f8b\u671f\u95f4\u4eba\u5de5\u667a\u80fd\u6307\u5bfc\u7684\u5fc3\u7535\u56fe\u623f\u98a4\u7b5b\u67e5"},"content":{"rendered":"\n<p>ARTICLES|<a href=\"https:\/\/www.thelancet.com\/journals\/lancet\/onlinefirst\">ONLINE FIRST<\/a><\/p>\n\n\n\n<h1 class=\"wp-block-heading\">Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial<\/h1>\n\n\n\n<h3 class=\"wp-block-heading\">Peter A Noseworthy, Zachi I Attia, Emma M Behnken, et al\u00a0<\/h3>\n\n\n\n<h3 class=\"wp-block-heading\">Lancet Published:September 27, 2022<\/h3>\n\n\n\n<h3 class=\"wp-block-heading\">DOI:<a href=\"https:\/\/doi.org\/10.1016\/S0140-6736(22)01637-3\">https:\/\/doi.org\/10.1016\/S0140-6736(22)01637-3<\/a><\/h3>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"seccestitle10\">Summary<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Background<\/h3>\n\n\n\n<p>Previous atrial fibrillation screening trials have highlighted the need for more targeted approaches. We did a pragmatic study to evaluate the effectiveness of an artificial intelligence (AI) algorithm-guided targeted screening approach for identifying previously unrecognised atrial fibrillation.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Methods<\/h3>\n\n\n\n<p>For this non-randomised interventional trial, we prospectively recruited patients with stroke risk factors but with no known atrial fibrillation who had an electrocardiogram (ECG) done in routine practice. Participants wore a continuous ambulatory heart rhythm monitor for up to 30 days, with the data transmitted in near real time through a cellular connection. The AI algorithm was applied to the ECGs to divide patients into high-risk or low-risk groups. The primary outcome was newly diagnosed atrial fibrillation. In a secondary analysis, trial participants were propensity-score matched (1:1) to individuals from the eligible but unenrolled population who served as real-world controls. This study is registered with&nbsp;<a href=\"http:\/\/clinicaltrials.gov\/\" target=\"_blank\" rel=\"noreferrer noopener\">ClinicalTrials.gov<\/a>,&nbsp;<a href=\"http:\/\/clinicaltrials.gov\/show\/NCT04208971\" target=\"_blank\" rel=\"noreferrer noopener\">NCT04208971<\/a>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Findings<\/h3>\n\n\n\n<p>1003 patients with a mean age of 74 years (SD 8\u00b78) from 40 US states completed the study. Over a mean 22\u00b73 days of continuous monitoring, atrial fibrillation was detected in six (1\u00b76%) of 370 patients with low risk and 48 (7\u00b76%) of 633 with high risk (odds ratio 4\u00b798, 95% CI 2\u00b711\u201311\u00b775, p=0\u00b70002). Compared with usual care, AI-guided screening was associated with increased detection of atrial fibrillation (high-risk group: 3\u00b76% [95% CI 2\u00b73\u20135\u00b74] with usual care&nbsp;<em>vs<\/em>10\u00b76% [8\u00b73\u201313\u00b72] with AI-guided screening, p&lt;0\u00b70001; low-risk group: 0\u00b79%&nbsp;<em>vs<\/em>&nbsp;2\u00b74%, p=0\u00b712) over a median follow-up of 9\u00b79 months (IQR 7\u00b71\u201311\u00b70).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Interpretation<\/h3>\n\n\n\n<p>An AI-guided targeted screening approach that leverages existing clinical data increased the yield for atrial fibrillation detection and could improve the effectiveness of atrial fibrillation screening.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Funding<\/h3>\n\n\n\n<p>Mayo Clinic Robert D and Patricia E Kern Center for the Science of Health Care Delivery.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>ARTICLES|ONLINE FIRST Artificial intelligence-guided sc [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[32,23],"tags":[],"_links":{"self":[{"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/posts\/22879"}],"collection":[{"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=22879"}],"version-history":[{"count":1,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/posts\/22879\/revisions"}],"predecessor-version":[{"id":22880,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/posts\/22879\/revisions\/22880"}],"wp:attachment":[{"href":"https:\/\/csccm.org.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=22879"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=22879"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=22879"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}