Daokun Sun, Lap Sum Chan, Faye L Norby, Riccardo M Inciardi, Elsayed Z Soliman, Alvaro Alonso, Scott D Solomon, Amil M Shah, Wei Pan, Lin Yee Chen
{"title":"社区动脉粥样硬化风险(ARIC)研究中左房储层应变对长期房颤的预测能力","authors":"Daokun Sun, Lap Sum Chan, Faye L Norby, Riccardo M Inciardi, Elsayed Z Soliman, Alvaro Alonso, Scott D Solomon, Amil M Shah, Wei Pan, Lin Yee Chen","doi":"10.1016/j.hrthm.2025.10.029","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Left atrial (LA) function measures from the two-dimensional echocardiograms are linked to atrial fibrillation (AF) development, but their standalone predictive value remains unclear.</p><p><strong>Objective: </strong>This study assessed the standalone predictive power of LA function measures for long-term AF prediction (5 and 10 years).</p><p><strong>Methods: </strong>We analyzed 5,442 older adults (median age 74) from the Atherosclerosis Risk in Communities (ARIC) study without prior AF. Participants were split into training and testing sets (7:3 ratio). We evaluated the standalone predictive performance of LA strain measures (reservoir, conduit, and contraction strain) with and without age for long-term AF. The performance was compared to that of the CHARGE-AF score and a machine learning-based predictive model that incorporates the most important routinely measured echocardiographic parameters with LA strain measures.</p><p><strong>Results: </strong>LA reservoir strain alone achieved a Harrell's C-index of 0.664 and 0.646 for 5- and 10-year AF prediction, improving to 0.677 and 0.663 when combined with age, which outperformed the CHARGE-AF score (0.667 and 0.655) and other LA strain measures. A better predictive performance was achieved by the machine learning-based predictive model, including LA reservoir strain, age, race, and 9 echocardiographic parameter; the C-indexes were 0.732 (0.686-0.777) for 5-year and 0.725 (0.693-0.757) for 10-year prediction.</p><p><strong>Conclusion: </strong>The standalone predictive performance of LA reservoir strain for long-term AF risk is comparable to CHARGE-AF score (11 variables) and slightly lower than that of a machine learning-based model with 12 variables, highlighting the importance of LA reservoir in AF risk prediction.</p>","PeriodicalId":12886,"journal":{"name":"Heart rhythm","volume":" ","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Predictive Power of Left Atrial Reservoir Strain for Long-Term Atrial Fibrillation in The Atherosclerosis Risk in Communities (ARIC) Study.\",\"authors\":\"Daokun Sun, Lap Sum Chan, Faye L Norby, Riccardo M Inciardi, Elsayed Z Soliman, Alvaro Alonso, Scott D Solomon, Amil M Shah, Wei Pan, Lin Yee Chen\",\"doi\":\"10.1016/j.hrthm.2025.10.029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Left atrial (LA) function measures from the two-dimensional echocardiograms are linked to atrial fibrillation (AF) development, but their standalone predictive value remains unclear.</p><p><strong>Objective: </strong>This study assessed the standalone predictive power of LA function measures for long-term AF prediction (5 and 10 years).</p><p><strong>Methods: </strong>We analyzed 5,442 older adults (median age 74) from the Atherosclerosis Risk in Communities (ARIC) study without prior AF. Participants were split into training and testing sets (7:3 ratio). We evaluated the standalone predictive performance of LA strain measures (reservoir, conduit, and contraction strain) with and without age for long-term AF. The performance was compared to that of the CHARGE-AF score and a machine learning-based predictive model that incorporates the most important routinely measured echocardiographic parameters with LA strain measures.</p><p><strong>Results: </strong>LA reservoir strain alone achieved a Harrell's C-index of 0.664 and 0.646 for 5- and 10-year AF prediction, improving to 0.677 and 0.663 when combined with age, which outperformed the CHARGE-AF score (0.667 and 0.655) and other LA strain measures. A better predictive performance was achieved by the machine learning-based predictive model, including LA reservoir strain, age, race, and 9 echocardiographic parameter; the C-indexes were 0.732 (0.686-0.777) for 5-year and 0.725 (0.693-0.757) for 10-year prediction.</p><p><strong>Conclusion: </strong>The standalone predictive performance of LA reservoir strain for long-term AF risk is comparable to CHARGE-AF score (11 variables) and slightly lower than that of a machine learning-based model with 12 variables, highlighting the importance of LA reservoir in AF risk prediction.</p>\",\"PeriodicalId\":12886,\"journal\":{\"name\":\"Heart rhythm\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2025-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Heart rhythm\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.hrthm.2025.10.029\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Heart rhythm","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.hrthm.2025.10.029","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
The Predictive Power of Left Atrial Reservoir Strain for Long-Term Atrial Fibrillation in The Atherosclerosis Risk in Communities (ARIC) Study.
Background: Left atrial (LA) function measures from the two-dimensional echocardiograms are linked to atrial fibrillation (AF) development, but their standalone predictive value remains unclear.
Objective: This study assessed the standalone predictive power of LA function measures for long-term AF prediction (5 and 10 years).
Methods: We analyzed 5,442 older adults (median age 74) from the Atherosclerosis Risk in Communities (ARIC) study without prior AF. Participants were split into training and testing sets (7:3 ratio). We evaluated the standalone predictive performance of LA strain measures (reservoir, conduit, and contraction strain) with and without age for long-term AF. The performance was compared to that of the CHARGE-AF score and a machine learning-based predictive model that incorporates the most important routinely measured echocardiographic parameters with LA strain measures.
Results: LA reservoir strain alone achieved a Harrell's C-index of 0.664 and 0.646 for 5- and 10-year AF prediction, improving to 0.677 and 0.663 when combined with age, which outperformed the CHARGE-AF score (0.667 and 0.655) and other LA strain measures. A better predictive performance was achieved by the machine learning-based predictive model, including LA reservoir strain, age, race, and 9 echocardiographic parameter; the C-indexes were 0.732 (0.686-0.777) for 5-year and 0.725 (0.693-0.757) for 10-year prediction.
Conclusion: The standalone predictive performance of LA reservoir strain for long-term AF risk is comparable to CHARGE-AF score (11 variables) and slightly lower than that of a machine learning-based model with 12 variables, highlighting the importance of LA reservoir in AF risk prediction.
期刊介绍:
HeartRhythm, the official Journal of the Heart Rhythm Society and the Cardiac Electrophysiology Society, is a unique journal for fundamental discovery and clinical applicability.
HeartRhythm integrates the entire cardiac electrophysiology (EP) community from basic and clinical academic researchers, private practitioners, engineers, allied professionals, industry, and trainees, all of whom are vital and interdependent members of our EP community.
The Heart Rhythm Society is the international leader in science, education, and advocacy for cardiac arrhythmia professionals and patients, and the primary information resource on heart rhythm disorders. Its mission is to improve the care of patients by promoting research, education, and optimal health care policies and standards.