Use of handheld electrocardiograph (SnapECG) for the remote monitoring of arrhythmias

Qian Gu; Zengguang Chen; Jiazheng Ma; Yaqing Zhou; Jinshuang Li; Lianghong Ying; Rui Hua; Wenhao Zhang; Ran Li; Fengwei Zou; Xiaoxuan Gong; Yiyang Zhan; Chunjian Li

doi:10.1177/20552076221113393

Use of handheld electrocardiograph (SnapECG) for the remote monitoring of arrhythmias

Digit Health. 2022 Jul 14:8:20552076221113393. doi: 10.1177/20552076221113393. eCollection 2022 Jan-Dec.

Authors

Qian Gu^{1

2}, Zengguang Chen^{1

3

2}, Jiazheng Ma^{1

2}, Yaqing Zhou^{1

2}, Jinshuang Li⁴, Lianghong Ying⁵, Rui Hua¹, Wenhao Zhang¹, Ran Li¹, Fengwei Zou⁶, Xiaoxuan Gong¹, Yiyang Zhan¹, Chunjian Li¹

Affiliations

¹ Department of Cardiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
² Qian Gu, Zengguang Chen, Jiazheng Ma, and Yaqing Zhou contributed equally to this work.
³ The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, Jiangsu, China.
⁴ Suqian Hospital Affiliated of Xuzhou Medical University, Suqian, Jiangsu, China.
⁵ Huai'an Hospital Affiliated of Xuzhou Medical University, Jiangsu, China.
⁶ Montefiore Medical Center, Bronx, NY, U.S.A.

Abstract

Objective: To investigate the value of a SnapECG monitoring in diagnosing arrhythmias compared with the conventional management.

Methods: In the first phase, the SnapECG and 12-lead electrocardiogram (ECG) were simultaneously adopted to detect arrhythmias in 439 hospitalized patients. The accuracies of the SnapECG in detecting different arrhythmias were assessed. In the second phase, 62 patients with palpitations were randomized to receive the SnapECG monitoring or conventional management for 3 months. The diagnosis rate, time of diagnosis, episodes before diagnosis, associated expenses, and scores of the modified European Heart Rhythm Association (EHRA), Self-rating Anxiety Scale (SAS), and the 36-item short-form health survey questionnaire (SF-36) were compared between groups.

Results: In the first phase, the SnapECG monitoring showed a sensitivity of 83.55% and specificity of 96.79% in identifying tachyarrhythmias, and a sensitivity of 95.29% and specificity of 97.54% in identifying bradyarrhythmias. In the second phase, 1642 ECGs were recorded by the SnapECG, among which 290 abnormal ECGs were identified. Compared with the conventional management, the SnapECG monitoring increased the diagnosis rate of symptomatic arrhythmias (70.97% vs. 19.35%, P < 0.05), shortened the time of diagnosis (48.26 ± 36.78 days vs. 71.45 ± 30.01 days, P < 0.05) and consequently reduced the episodes of symptomatic arrhythmias prior to establishing diagnosis. The scores of modified EHRA, SAS, SF-36 significantly improved at 3-month compared with their baseline levels in the SnapECG group.

Conclusions: Remote monitoring with the SnapECG can achieve early diagnosis of symptomatic arrhythmias. However, its sensitivity in identifying P-wave-related arrhythmias warrants further improvement.

Keywords: SAS score; SF-36 score; arrhythmia; electrocardiograph; modified EHRA score; remote monitoring.