Validation of a wearable biosensor device for vital sign monitoring in septic emergency department patients in Rwanda

Abstract

Objective: Critical care capabilities needed for the management of septic patients, such as continuous vital sign monitoring, are largely unavailable in most emergency departments (EDs) in low- and middle-income country (LMIC) settings. This study aimed to assess the feasibility and accuracy of using a wireless wearable biosensor device for continuous vital sign monitoring in ED patients with suspected sepsis in an LMIC setting.

Methods: This was a prospective observational study of pediatric (≥2 mon) and adult patients with suspected sepsis at the Kigali University Teaching Hospital ED. Heart rate, respiratory rate and temperature measurements were continuously recorded using a wearable biosensor device for the duration of the patients' ED course and compared to intermittent manually collected vital signs.

Results: A total of 42 patients had sufficient data for analysis. Mean duration of monitoring was 32.8 h per patient. Biosensor measurements were strongly correlated with manual measurements for heart rate (r = 0.87, p < 0.001) and respiratory rate (r = 0.75, p < 0.001), although were less strong for temperature (r = 0.61, p < 0.001). Mean (SD) differences between biosensor and manual measurements were 1.2 (11.4) beats/min, 2.5 (5.5) breaths/min and 1.4 (1.0)°C. Technical or practical feasibility issues occurred in 12 patients (28.6%) although were minor and included biosensor detachment, connectivity problems, removal for a radiologic study or exam, and patient/parent desire to remove the device.

Conclusions: Wearable biosensor devices can be feasibly implemented and provide accurate continuous heart rate and respiratory rate monitoring in acutely ill pediatric and adult ED patients with sepsis in an LMIC setting.

Keywords: Rwanda; Sepsis; biosensor; continuous vital signs; critical care; emergency medicine; low- and middle-income country; resource-limited; wearable device; wearable technology.

Figure 1.

The VitalPatch (a) and display screen showing a participant's electrocardiogram signal on a Samsung Galaxy J3 smartphone running the physIQ mobile application (b).

Figure 2.

Correlation scatterplots (left) and Bland–Altman plots (right) for (a) heart rate, (b) respiratory rate and (c) temperature comparing biosensor measurements and manual measurements obtained by a research nurse. Horizontal dashed lines in the Bland–Altman plots indicate the mean differences (bias) and shaded areas indicate 95% limits of agreement.