Elisa Hennings MD , Michael Coslovsky PhD , Rebecca E. Paladini PhD , Stefanie Aeschbacher PhD , Sven Knecht PhD , Vincent Schlageter PhD , Philipp Krisai MD , Patrick Badertscher MD , Christian Sticherling MD , Stefan Osswald MD , Michael Kühne MD , Christine S. Zuern MD , Swiss-AF Investigators
{"title":"应用人工智能评估动态心电图记录中房颤负荷","authors":"Elisa Hennings MD , Michael Coslovsky PhD , Rebecca E. Paladini PhD , Stefanie Aeschbacher PhD , Sven Knecht PhD , Vincent Schlageter PhD , Philipp Krisai MD , Patrick Badertscher MD , Christian Sticherling MD , Stefan Osswald MD , Michael Kühne MD , Christine S. Zuern MD , Swiss-AF Investigators","doi":"10.1016/j.cvdhj.2023.01.003","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Emerging evidence indicates that a high atrial fibrillation (AF) burden is associated with adverse outcome. However, AF burden is not routinely measured in clinical practice. An artificial intelligence (AI)-based tool could facilitate the assessment of AF burden.</p></div><div><h3>Objective</h3><p>We aimed to compare the assessment of AF burden performed manually by physicians with that measured by an AI-based tool.</p></div><div><h3>Methods</h3><p>We analyzed 7-day Holter electrocardiogram (ECG) recordings of AF patients included in the prospective, multicenter Swiss-AF Burden cohort study. AF burden was defined as percentage of time in AF, and was assessed manually by physicians and by an AI-based tool (Cardiomatics, Cracow, Poland). We evaluated the agreement between both techniques by means of Pearson correlation coefficient, linear regression model, and Bland-Altman plot.</p></div><div><h3>Results</h3><p>We assessed the AF burden in 100 Holter ECG recordings of 82 patients. We identified 53 Holter ECGs with 0% or 100% AF burden, where we found a 100% correlation. For the remaining 47 Holter ECGs with an AF burden between 0.01% and 81.53%, Pearson correlation coefficient was 0.998. The calibration intercept was -0.001 (95% CI -0.008; 0.006), and the calibration slope was 0.975 (95% CI 0.954; 0.995; multiple R<sup>2</sup> 0.995, residual standard error 0.017). Bland-Altman analysis resulted in a bias of -0.006 (95% limits of agreement -0.042 to 0.030).</p></div><div><h3>Conclusion</h3><p>The assessment of AF burden with an AI-based tool provided very similar results compared to manual assessment. An AI-based tool may therefore be an accurate and efficient option for the assessment of AF burden.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"4 2","pages":"Pages 41-47"},"PeriodicalIF":2.6000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10123500/pdf/","citationCount":"0","resultStr":"{\"title\":\"Assessment of the atrial fibrillation burden in Holter electrocardiogram recordings using artificial intelligence\",\"authors\":\"Elisa Hennings MD , Michael Coslovsky PhD , Rebecca E. Paladini PhD , Stefanie Aeschbacher PhD , Sven Knecht PhD , Vincent Schlageter PhD , Philipp Krisai MD , Patrick Badertscher MD , Christian Sticherling MD , Stefan Osswald MD , Michael Kühne MD , Christine S. Zuern MD , Swiss-AF Investigators\",\"doi\":\"10.1016/j.cvdhj.2023.01.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Emerging evidence indicates that a high atrial fibrillation (AF) burden is associated with adverse outcome. However, AF burden is not routinely measured in clinical practice. An artificial intelligence (AI)-based tool could facilitate the assessment of AF burden.</p></div><div><h3>Objective</h3><p>We aimed to compare the assessment of AF burden performed manually by physicians with that measured by an AI-based tool.</p></div><div><h3>Methods</h3><p>We analyzed 7-day Holter electrocardiogram (ECG) recordings of AF patients included in the prospective, multicenter Swiss-AF Burden cohort study. AF burden was defined as percentage of time in AF, and was assessed manually by physicians and by an AI-based tool (Cardiomatics, Cracow, Poland). We evaluated the agreement between both techniques by means of Pearson correlation coefficient, linear regression model, and Bland-Altman plot.</p></div><div><h3>Results</h3><p>We assessed the AF burden in 100 Holter ECG recordings of 82 patients. We identified 53 Holter ECGs with 0% or 100% AF burden, where we found a 100% correlation. For the remaining 47 Holter ECGs with an AF burden between 0.01% and 81.53%, Pearson correlation coefficient was 0.998. The calibration intercept was -0.001 (95% CI -0.008; 0.006), and the calibration slope was 0.975 (95% CI 0.954; 0.995; multiple R<sup>2</sup> 0.995, residual standard error 0.017). Bland-Altman analysis resulted in a bias of -0.006 (95% limits of agreement -0.042 to 0.030).</p></div><div><h3>Conclusion</h3><p>The assessment of AF burden with an AI-based tool provided very similar results compared to manual assessment. An AI-based tool may therefore be an accurate and efficient option for the assessment of AF burden.</p></div>\",\"PeriodicalId\":72527,\"journal\":{\"name\":\"Cardiovascular digital health journal\",\"volume\":\"4 2\",\"pages\":\"Pages 41-47\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10123500/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cardiovascular digital health journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S266669362300004X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cardiovascular digital health journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266669362300004X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Assessment of the atrial fibrillation burden in Holter electrocardiogram recordings using artificial intelligence
Background
Emerging evidence indicates that a high atrial fibrillation (AF) burden is associated with adverse outcome. However, AF burden is not routinely measured in clinical practice. An artificial intelligence (AI)-based tool could facilitate the assessment of AF burden.
Objective
We aimed to compare the assessment of AF burden performed manually by physicians with that measured by an AI-based tool.
Methods
We analyzed 7-day Holter electrocardiogram (ECG) recordings of AF patients included in the prospective, multicenter Swiss-AF Burden cohort study. AF burden was defined as percentage of time in AF, and was assessed manually by physicians and by an AI-based tool (Cardiomatics, Cracow, Poland). We evaluated the agreement between both techniques by means of Pearson correlation coefficient, linear regression model, and Bland-Altman plot.
Results
We assessed the AF burden in 100 Holter ECG recordings of 82 patients. We identified 53 Holter ECGs with 0% or 100% AF burden, where we found a 100% correlation. For the remaining 47 Holter ECGs with an AF burden between 0.01% and 81.53%, Pearson correlation coefficient was 0.998. The calibration intercept was -0.001 (95% CI -0.008; 0.006), and the calibration slope was 0.975 (95% CI 0.954; 0.995; multiple R2 0.995, residual standard error 0.017). Bland-Altman analysis resulted in a bias of -0.006 (95% limits of agreement -0.042 to 0.030).
Conclusion
The assessment of AF burden with an AI-based tool provided very similar results compared to manual assessment. An AI-based tool may therefore be an accurate and efficient option for the assessment of AF burden.
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