基于智能手机的光电容积脉搏波描记术应用程序在基层医疗环境中用于心房颤动筛查的诊断性能。

Diagnostic Performance of a Smartphone-Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting.

作者信息

Chan Pak-Hei, Wong Chun-Ka, Poh Yukkee C, Pun Louise, Leung Wangie Wan-Chiu, Wong Yu-Fai, Wong Michelle Man-Ying, Poh Ming-Zher, Chu Daniel Wai-Sing, Siu Chung-Wah

机构信息

Cardiology Division, Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.

Cardiio Inc., Cambridge, MA.

出版信息

J Am Heart Assoc. 2016 Jul 21;5(7):e003428. doi: 10.1161/JAHA.116.003428.

DOI:10.1161/JAHA.116.003428

PMID:27444506

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5015379/

Abstract

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.

摘要

背景

在缺血性卒中发生前诊断心房颤动（AF）是预防房颤相关卒中的首要任务。基于智能手机摄像头的光电容积脉搏波描记法（PPG）可区分不同的心律，但其在实际应用中诊断房颤的能力尚未得到充分研究。我们旨在评估一款独立的智能手机PPG应用程序Cardiio Rhythm在基层医疗环境中筛查房颤的诊断性能。

方法与结果

招募患有高血压、糖尿病和/或年龄≥65岁的患者。使用AliveCor心脏监测仪记录单导联心电图，随后由2名心脏病专家对记录进行审核，以提供参考标准。使用Cardiio Rhythm智能手机应用程序进行PPG测量。1013名参与者中有28名（2.76%）被诊断为房颤。Cardiio Rhythm检测房颤的诊断敏感性为92.9%（95%CI：77 - 99%），高于AliveCor自动算法（71.4%[95%CI 51 - 87%]）。Cardiio Rhythm和AliveCor自动算法的特异性相当（97.7%[95%CI：97 - 99%]对99.4%[95%CI 99 - 100%]）。Cardiio Rhythm的阳性预测值低于AliveCor自动算法（53.1%[95%CI 38 - 67%]对76.9%[95%CI 56 - 91%]）；两者的阴性预测值都非常高（99.8%[95%CI 99 - 100%]对99.2%[95%CI 98 - 100%]）。