基于手机的光电容积脉搏波技术在初级保健中检测心房颤动的应用：FibriCheck 应用程序的诊断准确性研究。
Mobile Phone-Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App.
机构信息
Department of Cardiology, Ziekenhuis Oost-Limburg, Genk, Belgium.
Department of Public Health and Primary Care, University of Leuven, Leuven, Belgium.
出版信息
JMIR Mhealth Uhealth. 2019 Mar 27;7(3):e12284. doi: 10.2196/12284.
BACKGROUND
Mobile phone apps using photoplethysmography (PPG) technology through their built-in camera are becoming an attractive alternative for atrial fibrillation (AF) screening because of their low cost, convenience, and broad accessibility. However, some important questions concerning their diagnostic accuracy remain to be answered.
OBJECTIVE
This study tested the diagnostic accuracy of the FibriCheck AF algorithm for the detection of AF on the basis of mobile phone PPG and single-lead electrocardiography (ECG) signals.
METHODS
A convenience sample of patients aged 65 years and above, with or without a known history of AF, was recruited from 17 primary care facilities. Patients with an active pacemaker rhythm were excluded. A PPG signal was obtained with the rear camera of an iPhone 5S. Simultaneously, a single‑lead ECG was registered using a dermal patch with a wireless connection to the same mobile phone. PPG and single-lead ECG signals were analyzed using the FibriCheck AF algorithm. At the same time, a 12‑lead ECG was obtained and interpreted offline by independent cardiologists to determine the presence of AF.
RESULTS
A total of 45.7% (102/223) subjects were having AF. PPG signal quality was sufficient for analysis in 93% and single‑lead ECG quality was sufficient in 94% of the participants. After removing insufficient quality measurements, the sensitivity and specificity were 96% (95% CI 89%-99%) and 97% (95% CI 91%-99%) for the PPG signal versus 95% (95% CI 88%-98%) and 97% (95% CI 91%-99%) for the single‑lead ECG, respectively. False-positive results were mainly because of premature ectopic beats. PPG and single‑lead ECG techniques yielded adequate signal quality in 196 subjects and a similar diagnosis in 98.0% (192/196) subjects.
CONCLUSIONS
The FibriCheck AF algorithm can accurately detect AF on the basis of mobile phone PPG and single-lead ECG signals in a primary care convenience sample.
背景
利用内置摄像头的光电体积描记术（PPG）技术的手机应用程序因其成本低、使用方便且广泛普及，正在成为心房颤动（AF）筛查的一种有吸引力的替代方法。然而，一些关于其诊断准确性的重要问题仍有待解答。
目的
本研究基于手机 PPG 和单导联心电图（ECG）信号，测试了 FibriCheck AF 算法检测 AF 的诊断准确性。
方法
从 17 个基层医疗机构招募了年龄在 65 岁及以上、有或无已知 AF 病史的方便样本患者。排除正在使用起搏器节律的患者。使用 iPhone 5S 的后置摄像头获取 PPG 信号。同时，使用具有无线连接至同一移动电话的皮肤贴片记录单导联 ECG。使用 FibriCheck AF 算法分析 PPG 和单导联 ECG 信号。同时，获得 12 导联心电图并由独立心脏病专家离线解读，以确定 AF 的存在。
结果
共有 45.7%（102/223）的患者存在 AF。PPG 信号质量足以进行分析的比例为 93%，单导联 ECG 质量足以进行分析的比例为 94%。去除质量不足的测量值后，PPG 信号的敏感性和特异性分别为 96%（95%CI 89%-99%）和 97%（95%CI 91%-99%），单导联 ECG 的敏感性和特异性分别为 95%（95%CI 88%-98%）和 97%（95%CI 91%-99%）。假阳性结果主要是由于过早的异位搏动。PPG 和单导联 ECG 技术在 196 例患者中获得了足够的信号质量，并在 98.0%（192/196）的患者中得出了相似的诊断。
结论
FibriCheck AF 算法可以基于手机 PPG 和单导联 ECG 信号在基层医疗便利样本中准确检测 AF。
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