ABSTRACT
INTRODUCTION: Atrial fibrillation (AF) is the most common cardiac arrhythmia in the developed world. Using photoplethysmography (PPG) and software algorithms, AF can be detected with high accuracy using smartphone camera-derived data. However, reports of diagnostic accuracy of standalone algorithms using wristband-derived PPG data are sparse, while this provides a means to perform long-term AF screening and monitoring. This study evaluated the diagnostic accuracy of a well-known standalone algorithm using wristband-derived PPG data. MATERIALS AND METHODS: Subjects recruited from a community senior care organization were instructed to wear the Wavelet PPG wristband on one arm and the Alivecor KardiaBand one-lead-ECG wristband on the other. Three consecutive measurements (duration per measurement: 60 s for PPG and 30 s for one-lead ECG) were performed with both devices, simultaneously. The PPG data were analyzed by the Fibricheck standalone algorithm and the ECG data by the Kardia algorithm. The results were compared to a reference standard (interpretation of the one-lead ECG by two independent cardiologists). RESULTS: A total of 180 PPGs and one-lead ECGs were recorded in 60 subjects, with a mean age of 70±17. AF was identified in 6 (10%) of the users, two users (3%) were not classifiable by the PPG algorithm and 1 user (2%) was not classifiable by the one-lead ECG algorithm. The diagnostic performance (sensitivity/specificity/positive predictive value/negative predictive value/accuracy) on user level was 100/96/75/100/97% for the PPG wristband and 100/98/86/100/98% for the one-lead ECG wristband. CONCLUSIONS: In a small real-world cohort of elderly people, the standalone Fibricheck AF algorithm can accurately detect AF using Wavelet wristband-derived PPG data. Results are comparable to the Alivecor Kardia one-lead ECG device, with an acceptable unclassifiable/bad quality rate. This opens the door for long-term AF screening and monitoring.
Subject(s)
Atrial Fibrillation , Photoplethysmography , Aged , Algorithms , Atrial Fibrillation/diagnosis , Electrocardiography , Humans , SmartphoneABSTRACT
Correction to: Neth Heart J 2018 https://doi.org/10.1007/s12471-018-1203-4 Unfortunately the original version of this article did not reflect that J.L. Selder and L. Breukel contributed equally to the .
ABSTRACT
BACKGROUND: In recent years many mobile devices able to record health-related data in ambulatory patients have emerged. However, well-organised programs to incorporate these devices are sparse. Hartwacht Arrhythmia (HA) is such a program, focusing on remote arrhythmia detection using the AliveCor Kardia Mobile (KM) and its algorithm. OBJECTIVES: The aim of this study was to assess the benefit of the KM device and its algorithm in detecting cardiac arrhythmias in a real-world cohort of ambulatory patients. METHODS: All KM ECGs recorded in the HA program between January 2017 and March 2018 were included. Classification by the KM algorithm was compared with that of the Hartwacht team led by a cardiologist. Statistical analyses were performed with respect to detection of sinus rhythm (SR), atrial fibrillation (AF) and other arrhythmias. RESULTS: 5,982 KM ECGs were received from 233 patients (mean age 58 years, 52% male). The KM algorithm categorised 59% as SR, 22% as possible AF, 17% as unclassified and 2% as unreadable. According to the Hartwacht team, 498 (8%) ECGs were uninterpretable. Negative predictive value for detection of AF was 98%. However, positive predictive value as well as detection of other arrhythmias was poor. In 81% of the unclassified ECGs, the Hartwacht team was able to provide a diagnosis. CONCLUSIONS: This study reports on the first symptom-driven remote arrhythmia monitoring program in the Netherlands. Less than 10% of the ECGs were uninterpretable. However, the current performance of the KM algorithm makes the device inadequate as a stand-alone application, supporting the need for manual ECG analysis in HA and similar programs.
