Abstract 12465: Clinical Results of a Novel Chest-Based Wearable During Transcatheter Aortic Valve Replacement Surgery for Real-Time Remote Monitoring

Abstract

Objective: Transcatheter Aortic Valve Replacement (TAVR) is the most common strategy for treating stenotic aortic valves in older adults. However, up to 18% of patients in the US are readmitted within 30 days, with about a third of these patients readmitted due to cardiac issues [1]. It would therefore be desirable to monitor discharged patients remotely to catch early signs of deterioration before more serious complications occur, and thereby reduce hospital readmission rates. The current gold standard for arrhythmia monitoring involves a single-lead Holter monitor in the form of a wearable chest-based patch. However, these devices usually do not track cardiac rhythms in real-time, and are typically mailed in after the wear period is over resulting in further delays. Methods: A novel chest patch capable of single-lead ECG that achieves real-time monitoring via Bluetooth and smartphone communications was used. To validate, the patch was placed on the chest of patients undergoing a TAVR; ECG was recorded and compared to gold-standard equipment. Results: ECG rhythms were collected from four patients using the patch and compared to an institutional hemodynamic monitoring system (GE MACLAB). The ECGs from each system were time aligned using a Fourier Transform method [2], R-peaks were identified using the Pan-Tompkins algorithm, heart rates were calculated over the course of the procedure by averaging R-R intervals over a 10 sec sliding window, and lastly Pearson correlation coefficients were calculated between the patch and gold-standard equipment. Pearson coefficients ranged from 0.995 to 0.999 with a Mean Absolute Error of 0.55 BPM and Root Mean Square Error of 2 BPM (see fig). Conclusion: These early results suggest an excellent agreement and accuracy between the wearable device and gold-standard ECG equipment, and may pave the way for monitoring discharged patients remotely. [1] Kolte, Dhaval et al 2017 [2] Fridman, Lex et al 2016