Bridget M.I. Slaats Bsc , Sebastiaan Blok PhD , G. Aernout Somsen MD, PhD , Igor I. Tulevski MD, PhD , Reinoud E. Knops MD, PhD, FHRS , Bert-Jan H. van den Born MD, PhD , Michiel M. Winter MD, PhD
{"title":"使用 KardiaMobile 算法能否扩大心律失常电子健康计划的规模?","authors":"Bridget M.I. Slaats Bsc , Sebastiaan Blok PhD , G. Aernout Somsen MD, PhD , Igor I. Tulevski MD, PhD , Reinoud E. Knops MD, PhD, FHRS , Bert-Jan H. van den Born MD, PhD , Michiel M. Winter MD, PhD","doi":"10.1016/j.cvdhj.2023.11.004","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Remote monitoring devices for atrial fibrillation are known to positively contribute to the diagnostic process and therapy compliance. However, automatic algorithms within devices show varying sensitivity and specificity, so manual double-checking of electrocardiographic (ECG) recordings remains necessary.</p></div><div><h3>Objective</h3><p>The purpose of this study was to investigate the validity of the KardiaMobile algorithm within the Dutch telemonitoring program (HartWacht).</p></div><div><h3>Methods</h3><p>This retrospective study determined the diagnostic accuracy of the algorithm using assessments by a telemonitoring team as reference. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and F1 scores were determined.</p></div><div><h3>Results</h3><p>A total of 2298 patients (59.5% female; median age 57 ± 15 years) recorded 86,816 ECGs between April 2019 and January 2021. The algorithm showed sensitivity of 0.956, specificity 0.985, PPV 0.996, NPV 0.847, and F1 score 0.976 for the detection of sinus rhythm. A total of 29 false-positive outcomes remained uncorrected within the same patients. The algorithm showed sensitivity of 0.989, specificity 0.953, PPV 0.835, NPV 0.997, and F1 score 0.906 for detection of atrial fibrillation. A total of 2 false-negative outcomes remained uncorrected.</p></div><div><h3>Conclusion</h3><p>Our research showed high validity of the algorithm for the detection of both sinus rhythm and, to a lesser extent, atrial fibrillation. This finding suggests that the algorithm could function as a standalone instrument particularly for detection of sinus rhythm.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"5 2","pages":"Pages 78-84"},"PeriodicalIF":2.6000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693623000774/pdfft?md5=2c239dcb8183411753a06321db03891a&pid=1-s2.0-S2666693623000774-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Can eHealth programs for cardiac arrhythmias be scaled-up by using the KardiaMobile algorithm?\",\"authors\":\"Bridget M.I. Slaats Bsc , Sebastiaan Blok PhD , G. Aernout Somsen MD, PhD , Igor I. Tulevski MD, PhD , Reinoud E. Knops MD, PhD, FHRS , Bert-Jan H. van den Born MD, PhD , Michiel M. Winter MD, PhD\",\"doi\":\"10.1016/j.cvdhj.2023.11.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Remote monitoring devices for atrial fibrillation are known to positively contribute to the diagnostic process and therapy compliance. However, automatic algorithms within devices show varying sensitivity and specificity, so manual double-checking of electrocardiographic (ECG) recordings remains necessary.</p></div><div><h3>Objective</h3><p>The purpose of this study was to investigate the validity of the KardiaMobile algorithm within the Dutch telemonitoring program (HartWacht).</p></div><div><h3>Methods</h3><p>This retrospective study determined the diagnostic accuracy of the algorithm using assessments by a telemonitoring team as reference. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and F1 scores were determined.</p></div><div><h3>Results</h3><p>A total of 2298 patients (59.5% female; median age 57 ± 15 years) recorded 86,816 ECGs between April 2019 and January 2021. The algorithm showed sensitivity of 0.956, specificity 0.985, PPV 0.996, NPV 0.847, and F1 score 0.976 for the detection of sinus rhythm. A total of 29 false-positive outcomes remained uncorrected within the same patients. The algorithm showed sensitivity of 0.989, specificity 0.953, PPV 0.835, NPV 0.997, and F1 score 0.906 for detection of atrial fibrillation. A total of 2 false-negative outcomes remained uncorrected.</p></div><div><h3>Conclusion</h3><p>Our research showed high validity of the algorithm for the detection of both sinus rhythm and, to a lesser extent, atrial fibrillation. This finding suggests that the algorithm could function as a standalone instrument particularly for detection of sinus rhythm.</p></div>\",\"PeriodicalId\":72527,\"journal\":{\"name\":\"Cardiovascular digital health journal\",\"volume\":\"5 2\",\"pages\":\"Pages 78-84\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666693623000774/pdfft?md5=2c239dcb8183411753a06321db03891a&pid=1-s2.0-S2666693623000774-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cardiovascular digital health journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666693623000774\",\"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/S2666693623000774","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Can eHealth programs for cardiac arrhythmias be scaled-up by using the KardiaMobile algorithm?
Background
Remote monitoring devices for atrial fibrillation are known to positively contribute to the diagnostic process and therapy compliance. However, automatic algorithms within devices show varying sensitivity and specificity, so manual double-checking of electrocardiographic (ECG) recordings remains necessary.
Objective
The purpose of this study was to investigate the validity of the KardiaMobile algorithm within the Dutch telemonitoring program (HartWacht).
Methods
This retrospective study determined the diagnostic accuracy of the algorithm using assessments by a telemonitoring team as reference. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and F1 scores were determined.
Results
A total of 2298 patients (59.5% female; median age 57 ± 15 years) recorded 86,816 ECGs between April 2019 and January 2021. The algorithm showed sensitivity of 0.956, specificity 0.985, PPV 0.996, NPV 0.847, and F1 score 0.976 for the detection of sinus rhythm. A total of 29 false-positive outcomes remained uncorrected within the same patients. The algorithm showed sensitivity of 0.989, specificity 0.953, PPV 0.835, NPV 0.997, and F1 score 0.906 for detection of atrial fibrillation. A total of 2 false-negative outcomes remained uncorrected.
Conclusion
Our research showed high validity of the algorithm for the detection of both sinus rhythm and, to a lesser extent, atrial fibrillation. This finding suggests that the algorithm could function as a standalone instrument particularly for detection of sinus rhythm.