Assessment of heart rate agreement on continuous photoplethysmography monitoring using a smartwatch versus beat-to-beat synchronized ECG monitoring. (14th October 2021) - Gruwez, H; Evens, S; Desteghe, L; Dreesen, P; Knaepen, L; Wouters, F; Dauw, J; Deferm, S; Smeets, C; Pison, L; Haemers, P; Heidbuchel, H; et al.
Abstract: Background: In the awakening era of mobile health, wearables equipped with photoplethysmography (PPG) technology to monitor the heart rate (HR) and rhythm are on the rise. Smartwatches and wristbands enable HR monitoring for consumers at massive scale. Unfortunately, once consumers become patients, physicians are limited by insufficient evidence to support the clinical use of PPG based wearables. Accurate identification of heartbeats is the first step in the interpretation of PPG traces and should be validated. Purpose: To assess the agreement between continuous PPG monitoring using a smartwatch and continuous ECG Holter monitoring in the identification of heartbeats and calculation of the HR. Methods: One hundred patients (≥18 years) without a pacemaker-dependent heart rhythm who were referred to a university hospital and a large tertiary hospital for elective 24-hour ECG Holter monitoring were asked to wear a continuous PPG monitoring smartwatch (i.e. Samsung GWA2 or Empatica E4) simultaneously with the 24-hour Holter monitor. All activities of daily life were allowed. The ECG trace and PPG waveform were synchronised and fragmented in one-minute fragments. The one-minute ECG fragments were labelled as AF, non-AF, or insufficient quality based on the routine clinical interpretation (i.e. software + physician overreading), and the average HR during each fragment was calculated by Holter algorithm. The PPG fragments were analysed by an artificial intelligence (AI) algorithm (i.e. FibriCheck) that labelled fragments as sufficient or insufficient quality, identified the number of heartbeats and calculated the HR. The agreement between the HR on ECG and PPG in sufficient quality tracings was analysed with linear regression, Pearson's product-moment correlation and Bland-Altman analysis. A subanalysis was performed for AF rhythm and non-AF rhythms. Results: A total of 72, 725 simultaneous ECG and PPG one-minute fragments were recorded in 96 patients, after excluding 4 patients (due to 3 Holter and 1 smartwatch technical error) and 42, 520 minutes (36.9%) of insufficient quality (ECG 1, 454 (1.3%); PPG 25, 704 (22.3%), ECG and PPG 15, 362 (13.3%)). The correlation (r=0.935) between ECG and PPG HR was statistically significant (CI 0.934–0.936; P<0.001), with a mean difference between ECG and PPG of 0.8bpm. The lower and upper limit boundary (LLB and ULB; defined as ±1.96 SD) were −8.0bpm and 9.7bpm, respectively, i.e. 95% of PPG measurements identified the HR within 8bpm below or 10bpm above the ECG reference. The mean difference between ECG and PPG HR in the AF subgroup (n=10, 255 (14.1%)) was 0.9bpm (LLB −8.4bpm; ULB 10.2bpm) and 0.8bpm in the non-AF subgroup (LLB −0.8bpm; ULB 9.6bpm). Conclusion: The AI algorithm analysing continuous out-of-hospital PPG tracings can annotate heartbeats and assess HR without a clinically significant bias compared to continuous ECG monitoring, both during AF and non-AF rhythms in a heterogenous patient population. Funding Acknowledgement: Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Research Foundation-Flanders, Strategic Basic Research Fund