Vital sign monitoring using wearable devices in a Vietnamese intensive care unit

• Published in: BMJ innovations Vol. 7; no. Suppl 1; pp. s7 - s11
• Main Authors: Van, Hoang Minh Tu, Hao, Nguyen Van, Phan Nguyen Quoc, Khanh, Hai, Ho Bich, Khoa, Le Dinh Van, Yen, Lam Minh, Nhat, Phung Tran Huy, Hai Duong, Ha Thi, Thuy, Duong Bich, Zhu, Tingting, Greeff, Heloise, Clifton, David, Thwaites, C Louise, Kien, Dang Trung, Trinh, Dong Huu Khanh, Donovan, Joseph, Duc, Du Hong, Geskus, Ronald, Chanh, Ho Quang, Hien, Ho Van, Trieu, Huynh Trung, Kestelyn, Evelyne, Nhan, Le Nguyen Thanh, An, Luu Phuoc, Vuong, Nguyen Lam, Quyen, Nguyen Than Ha, Thanh, Nguyen Thi Le, Dung, Nguyen Thi Phuong, Van, Ninh Thi Thanh, Khanh, Phan Nguyen Quoc, Lam, Phung Khanh, Thwaites, Guy, Thwaites, Louise, Duc, Tran Minh, Hung, Trinh Manh, Turner, Hugo, Nuil, Jennifer Ilo Van, Yacoub, Sophie, Tam, Cao Thi, Duong, Ha Thi Hai, Nghia, Ho Dang Trung, Chau, Le Buu, Tai, Luong Thi Hue, Phu, Nguyen Hoan, Viet, Nguyen Quoc, Nguyen, Nguyen Thanh, Phong, Nguyen Thanh, Anh, Nguyen Thi Kim, Duoc, Nguyen Van Thanh, Chau, Nguyen Van Vinh, Oanh, Pham Kieu Nguyet, Qui, Phan Tu, Tho, Phan Vinh, English, Mike, Lu, Huiqi, McKnight, Jacob, Paton, Chris, Georgiou, Pantellis, Perez, Bernard Hernandez, Hill-Cawthorne, Kerri, Holmes, Alison, Karolcik, Stefan, Ming, Damien, Moser, Nicolas, Manzano, Jesus Rodriguez, Gomez, Alberto, Kerdegari, Hamideh, Modat, Marc, Razavi, Reza, Dutt, Abhilash Guru, Karlen, Walter, Verling, Michaela, Wicki, Elias, Denehy, Linda, Rollinson, Thomas
• Format: Journal Article
• Language: English
• Published: London All India Institute of Medical Sciences 01.03.2021; BMJ Publishing Group LTD
• Subjects: critical care; delivery of health care; early diagnosis; Early-stage innovation report; Heart rate; Intensive care; Medical diagnosis; patient care; Patients; Physiology; Pulse oximetry; Tetanus; Wearable computers
• ISSN: 2055-8074; 2055-642X
• DOI: 10.1136/bmjinnov-2021-000707

In low-income and middle-income countries (LMIC), lack of staff and equipment means this is difficult to achieve.2 3 An alternative solution for resource-limited settings, enabled by recent advances in sensor technologies, is the use of low-cost wearable devices.4 Some of these devices have the additional advantage of being able to record continuous data, which allow more complex analysis and may facilitate even better risk prediction.5–7 There is, however, limited use of these devices in the unique and challenging environments of LMIC intensive care units (ICUs).3 The majority of validatory data concerning wearables come from community settings in relatively healthy ambulatory individuals.8–10 In hospital environments, the accuracy of data derived from unstable or critically ill patients is less certain.10 Studies indicate a reasonable correlation of wearable-derived heart rate measurements with nurses’ manual observations, but less when comparing respiratory rate measurements.11 12 Similarly, medical-grade wearable patches in surgical patients showed good agreement in heart rate but not respiratory rate measurements when compared with ‘gold standard’ ICU monitors.10 13 The suitability of low-cost wearables to record continuous waveform data in critically ill patients is even less certain, as these systems may be limited by high levels of noise, movement artefact and missing data.7 8 14 15 In view of this, we aimed to pilot low-cost wearable devices for continuous vital sign monitoring in critically ill patients with tetanus in a Vietnamese ICU, using two medical-grade devices able to export continuous waveform data: a patch ECG and a wrist-worn pulse oximeter. Calculation of these indices is based on mathematical evaluation of successive RR intervals and requires good-quality waveform data, and thus in calculating these we aimed to provide a clinically relevant indicator of waveform quality which could eventually be developed for disease prognostication. The resulting RR interval series were filtered with a heart rate range of 40–200 beats per minute (bmp) and subject to HRV analysis using the R package RHRV.19 We chose to use eight time domain measures and six frequency domain measures (calculated using the Lomb-Scargle periodogram) as described in Pichot et al 17 for comparison. HF, low to high frequency ratio; MADRR, median of the absolute differences between RR intervals; pNN50, percentage of successive RR intervals that differ by more than 50 ms; rMSSD, route mean square of successive RR interval difference; SDNN, SD of NN intervals; SDSD, SD of successive RR intervals; TINN, baseline width of the RR interval histogram; VLF, very low frequency.