Open AccessDiagnostic Performance of a Smartphone‐Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting
Author(s) -
Chan PakHei,
Wong ChunKa,
Poh Yukkee C.,
Pun Louise,
Leung Wangie WanChiu,
Wong YuFai,
Wong Michelle ManYing,
Poh MingZher,
Chu Daniel WaiSing,
Siu ChungWah
Publication year - 2016
Publication title -
journal of the american heart association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.494
H-Index - 85
ISSN - 2047-9980
DOI - 10.1161/jaha.116.003428
Subject(s) - medicine , atrial fibrillation , rhythm , stroke (engine) , cardiology , diabetes mellitus , photoplethysmogram , predictive value , heart rhythm , mechanical engineering , engineering , endocrinology , filter (signal processing) , computer science , computer vision
Background Diagnosing atrial fibrillation ( AF ) before ischemic stroke occurs is a priority for stroke prevention in AF . Smartphone camera–based photoplethysmographic ( PPG ) pulse waveform measurement discriminates between different heart rhythms, but its ability to diagnose AF in real‐world situations has not been adequately investigated. We sought to assess the diagnostic performance of a standalone smartphone PPG application, Cardiio Rhythm, for AF screening in primary care setting. Methods and Results Patients with hypertension, with diabetes mellitus, and/or aged ≥65 years were recruited. A single‐lead ECG was recorded by using the AliveCor heart monitor with tracings reviewed subsequently by 2 cardiologists to provide the reference standard. PPG measurements were performed by using the Cardiio Rhythm smartphone application. AF was diagnosed in 28 (2.76%) of 1013 participants. The diagnostic sensitivity of the Cardiio Rhythm for AF detection was 92.9% (95% CI ] 77–99%) and was higher than that of the AliveCor automated algorithm (71.4% [95% CI 51–87%]). The specificities of Cardiio Rhythm and the AliveCor automated algorithm were comparable (97.7% [95% CI : 97–99%] versus 99.4% [95% CI 99–100%]). The positive predictive value of the Cardiio Rhythm was lower than that of the AliveCor automated algorithm (53.1% [95% CI 38–67%] versus 76.9% [95% CI 56–91%]); both had a very high negative predictive value (99.8% [95% CI 99–100%] versus 99.2% [95% CI 98–100%]). Conclusions The Cardiio Rhythm smartphone PPG application provides an accurate and reliable means to detect AF in patients at risk of developing AF and has the potential to enable population‐based screening for AF .