Atrial fibrillation detection in primary care during blood pressure measurements and using a smartphone cardiac monitor

Abstract Improved atrial fibrillation (AF) screening methods are required. We detected AF with pulse rate variability (PRV) parameters using a blood pressure device (BP+; Uscom, Sydney, Australia) and with a Kardia Mobile Cardiac Monitor (KMCM; AliveCor, Mountain View, CA). In 421 primary care patie...

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Main Authors: John D. Sluyter, Robert Scragg, Malakai ‘Ofanoa, Ralph A. H. Stewart
Format: Article
Language:English
Published: Nature Publishing Group 2021-09-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-021-97475-1
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spelling doaj-49520a6547414117bb78e015af2230952021-09-12T11:23:54ZengNature Publishing GroupScientific Reports2045-23222021-09-0111111010.1038/s41598-021-97475-1Atrial fibrillation detection in primary care during blood pressure measurements and using a smartphone cardiac monitorJohn D. Sluyter0Robert Scragg1Malakai ‘Ofanoa2Ralph A. H. Stewart3School of Population Health, University of AucklandSchool of Population Health, University of AucklandSchool of Population Health, University of AucklandGreen Lane Cardiovascular Service, Auckland City HospitalAbstract Improved atrial fibrillation (AF) screening methods are required. We detected AF with pulse rate variability (PRV) parameters using a blood pressure device (BP+; Uscom, Sydney, Australia) and with a Kardia Mobile Cardiac Monitor (KMCM; AliveCor, Mountain View, CA). In 421 primary care patients (mean (range) age: 72 (31–99) years), we diagnosed AF (n = 133) from 12-lead electrocardiogram recordings, and performed PRV and KMCM measurements. PRV parameters detected AF with area under curve (AUC) values of up to 0.92. Using the mean of two sequential readings increased AUC to up to 0.94 and improved positive predictive value at a given sensitivity (by up to 18%). The KMCM detected AF with 83% sensitivity and 68% specificity. 89 KMCM recordings were “unclassified” or blank, and PRV detected AF in these with AUC values of up to 0.88. When non-AF arrhythmias (n = 56) were excluded, the KMCM device had increased specificity (73%) and PRV had higher discrimination performance (maximum AUC = 0.96). In decision curve analysis, all PRV parameters consistently achieved a positive net benefit across the range of clinical thresholds. In primary care, AF can be detected by PRV accurately and by KMCM, especially in the absence of non-AF arrhythmias or when combinations of measurements are used.https://doi.org/10.1038/s41598-021-97475-1
collection DOAJ
language English
format Article
sources DOAJ
author John D. Sluyter
Robert Scragg
Malakai ‘Ofanoa
Ralph A. H. Stewart
spellingShingle John D. Sluyter
Robert Scragg
Malakai ‘Ofanoa
Ralph A. H. Stewart
Atrial fibrillation detection in primary care during blood pressure measurements and using a smartphone cardiac monitor
Scientific Reports
author_facet John D. Sluyter
Robert Scragg
Malakai ‘Ofanoa
Ralph A. H. Stewart
author_sort John D. Sluyter
title Atrial fibrillation detection in primary care during blood pressure measurements and using a smartphone cardiac monitor
title_short Atrial fibrillation detection in primary care during blood pressure measurements and using a smartphone cardiac monitor
title_full Atrial fibrillation detection in primary care during blood pressure measurements and using a smartphone cardiac monitor
title_fullStr Atrial fibrillation detection in primary care during blood pressure measurements and using a smartphone cardiac monitor
title_full_unstemmed Atrial fibrillation detection in primary care during blood pressure measurements and using a smartphone cardiac monitor
title_sort atrial fibrillation detection in primary care during blood pressure measurements and using a smartphone cardiac monitor
publisher Nature Publishing Group
series Scientific Reports
issn 2045-2322
publishDate 2021-09-01
description Abstract Improved atrial fibrillation (AF) screening methods are required. We detected AF with pulse rate variability (PRV) parameters using a blood pressure device (BP+; Uscom, Sydney, Australia) and with a Kardia Mobile Cardiac Monitor (KMCM; AliveCor, Mountain View, CA). In 421 primary care patients (mean (range) age: 72 (31–99) years), we diagnosed AF (n = 133) from 12-lead electrocardiogram recordings, and performed PRV and KMCM measurements. PRV parameters detected AF with area under curve (AUC) values of up to 0.92. Using the mean of two sequential readings increased AUC to up to 0.94 and improved positive predictive value at a given sensitivity (by up to 18%). The KMCM detected AF with 83% sensitivity and 68% specificity. 89 KMCM recordings were “unclassified” or blank, and PRV detected AF in these with AUC values of up to 0.88. When non-AF arrhythmias (n = 56) were excluded, the KMCM device had increased specificity (73%) and PRV had higher discrimination performance (maximum AUC = 0.96). In decision curve analysis, all PRV parameters consistently achieved a positive net benefit across the range of clinical thresholds. In primary care, AF can be detected by PRV accurately and by KMCM, especially in the absence of non-AF arrhythmias or when combinations of measurements are used.
url https://doi.org/10.1038/s41598-021-97475-1
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