{"id":5736,"date":"2025-02-26T13:08:05","date_gmt":"2025-02-26T10:08:05","guid":{"rendered":"https:\/\/jordan-cardiac.org\/?p=5736"},"modified":"2025-02-26T13:08:44","modified_gmt":"2025-02-26T10:08:44","slug":"key-takeaways-ai-guided-decision-for-laao-vs-doac-in-afib-patients","status":"publish","type":"post","link":"https:\/\/jordan-cardiac.org\/en\/key-takeaways-ai-guided-decision-for-laao-vs-doac-in-afib-patients\/","title":{"rendered":"Key Takeaways: AI-Guided Decision for LAAO vs. DOAC in AFib Patients"},"content":{"rendered":"
Key Takeaways: AI-Guided Decision for LAAO vs. DOAC in AFib Patients<\/div>\n
<\/div>\n
(JACC: Clinical Electrophysiology, Feb 12, 2025)<\/div>\n
<\/div>\n
1- AI Identifies Optimal Candidates for LAAO<\/div>\n
<\/span>\u2022 <\/span>Researchers at Mayo Clinic developed an artificial intelligence (AI) algorithm to identify atrial fibrillation (AFib) patients who may benefit from left atrial appendage occlusion (LAAO).<\/div>\n
<\/span>\u2022 <\/span>The study was published in JACC: Clinical Electrophysiology.<\/div>\n
<\/div>\n
2- Challenge: Lifelong Anticoagulation Therapy<\/div>\n
<\/span>\u2022 <\/span>Direct oral anticoagulants (DOACs) are preferred over warfarin for stroke prevention in AFib.<\/div>\n
<\/span>\u2022 <\/span>However, lifelong anticoagulation increases bleeding risk and has poor adherence.<\/div>\n
<\/span>\u2022 <\/span>LAAO offers an alternative, but selecting the right candidates remains unclear.<\/div>\n
<\/div>\n
3- Study Design & AI Model Development<\/div>\n
<\/span>\u2022 <\/span>Researchers analyzed data from 744,000 AFib patients (2015\u20132019), focusing on the 1.9% treated with LAAO due to high stroke risk.<\/div>\n
<\/span>\u2022 <\/span>Propensity score matching was used to compare 14,000 LAAO patients with a similar DOAC-treated cohort.<\/div>\n
<\/span>\u2022 <\/span>AI models, based on causal forest (CF) techniques, were developed to predict whether LAAO would be beneficial, neutral, or harmful.<\/div>\n
<\/div>\n
4- External Validation Confirms AI Accuracy<\/div>\n
<\/span>\u2022 <\/span>The model was validated using data from 377 LAAO patients and 26,000 DOAC patients (2016\u20132021).<\/div>\n
<\/span>\u2022 <\/span>Propensity score matching resulted in 371 comparable patient pairs.<\/div>\n
<\/span>\u2022 <\/span>The model confirmed that patients identified as likely to benefit from LAAO indeed had higher stroke risks.<\/div>\n
<\/div>\n
5- Key Patient Characteristics Favoring LAAO<\/div>\n
<\/span>\u2022 <\/span>Older patients and those with comorbidities\u2014dementia, pneumonia, respiratory failure\u2014benefited more from LAAO over DOAC therapy.<\/div>\n
<\/div>\n
6- Clinical Implications<\/div>\n
<\/span>\u2022 <\/span>This is the first study to apply causal machine learning to a national database for predicting treatment effects in AFib patients.<\/div>\n
<\/span>\u2022 <\/span>The AI algorithm can assist general practitioners and non-specialists in identifying patients for further evaluation for LAAO.<\/div>\n
<\/div>\n
7- Limitations & Considerations<\/div>\n
<\/span>\u2022 <\/span>The AI model\u2019s prediction is one factor in decision-making.<\/div>\n
<\/span>\u2022 <\/span>Other elements\u2014patient preferences, clinical outcomes, cost, and timeframe\u2014should also be considered.<\/div>\n
<\/div>\n
https:\/\/www.jacc.org\/doi\/10.1016\/j.jacep.2024.12.013?_ga=2.115933701.2067354671.1739544187-1502840752.1721316643&_gac=1.23698120.1738072868.CjwKCAiAneK8BhAVEiwAoy2HYZWoEim9TLo9xNnMew0a5DO6mrQNh9w-r5EzfawcIupHA5u3EjhxRhoCxrEQAvD_BwE<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"

Key Takeaways: AI-Guided Decision for LAAO vs. DOAC in AFib Patients (JACC: Clinical Electrophysiology, Feb 12, 2025) 1- AI Identifies Optimal Candidates for LAAO \u2022 Researchers at Mayo Clinic developed an artificial intelligence (AI) algorithm to identify atrial fibrillation (AFib) patients who may benefit from left atrial appendage occlusion (LAAO). \u2022 The study was published […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-5736","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/jordan-cardiac.org\/en\/wp-json\/wp\/v2\/posts\/5736","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jordan-cardiac.org\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/jordan-cardiac.org\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/jordan-cardiac.org\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/jordan-cardiac.org\/en\/wp-json\/wp\/v2\/comments?post=5736"}],"version-history":[{"count":2,"href":"https:\/\/jordan-cardiac.org\/en\/wp-json\/wp\/v2\/posts\/5736\/revisions"}],"predecessor-version":[{"id":5738,"href":"https:\/\/jordan-cardiac.org\/en\/wp-json\/wp\/v2\/posts\/5736\/revisions\/5738"}],"wp:attachment":[{"href":"https:\/\/jordan-cardiac.org\/en\/wp-json\/wp\/v2\/media?parent=5736"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jordan-cardiac.org\/en\/wp-json\/wp\/v2\/categories?post=5736"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jordan-cardiac.org\/en\/wp-json\/wp\/v2\/tags?post=5736"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}