Evaluation of a contactless neonatal physiological monitor in Nairobi, Kenya

Abstract

Background: Globally, 2.5 million neonates died in 2018, accounting for 46% of under-5 deaths. Multiparameter continuous physiological monitoring (MCPM) of neonates allows for early detection and treatment of life-threatening health problems. However, neonatal monitoring technology is largely unavailable in low-resource settings.

Methods: In four evaluation rounds, we prospectively compared the accuracy of the EarlySense under-mattress device to the Masimo Rad-97 pulse CO-oximeter with capnography reference device for heart rate (HR) and respiratory rate (RR) measurements in neonates in Kenya. EarlySense algorithm optimisations were made between evaluation rounds. In each evaluation round, we compared 200 randomly selected epochs of data using Bland-Altman plots and generated Clarke error grids with zones of 20% to aid in clinical interpretation.

Results: Between 9 July 2019 and 8 January 2020, we collected 280 hours of MCPM data from 76 enrolled neonates. At the final evaluation round, the EarlySense MCPM device demonstrated a bias of -0.8 beats/minute for HR and 1.6 breaths/minute for RR, and normalised spread between the 95% upper and lower limits of agreement of 6.2% for HR and 27.3% for RR. Agreement between the two MCPM devices met the a priori-defined threshold of 30%. The Clarke error grids showed that all observations for HR and 197/200 for RR were within a 20% difference.

Conclusion: Our research indicates that there is acceptable agreement between the EarlySense and Masimo MCPM devices in the context of large within-subject variability; however, further studies establishing cost-effectiveness and clinical effectiveness are needed before large-scale implementation of the EarlySense MCPM device in neonates.

Trial registration number: NCT03920761.

Keywords: intensive care units; neonatal; neonatology; technology.

Conflict of interest statement

Competing interests: YSH and RK are employed by EarlySense. All other authors declare no competing interests.

© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.

Figures

Figure 1

(A) Overview of the set-up showing the Masimo Rad-97 device with touchscreen interface (1), pulse oximeter probe (2), Nomoline for capnography (3), EarlySense processing unit (4), EarlySense under-mattress sensor (5). (B) Close-up of the EarlySense sensor under a mattress. The EarlySense sensor is connected to the processing unit that processes, stores data and sends results wirelessly to the remote display unit where the data are presented.

Figure 2

Bland-Altman plots for heart rate (HR). (A) Open-label round. (B) Closed-label round one. (C) Closed-label round two. (D) Closed-label round three. Colours indicate which participant neonate is associated with the measurement pair.

Figure 3

Clarke error grids for closed-label round three measurements. (A) Comparison of heart rate (HR) measurements. (B) Comparison of EarlySense respiratory rate (RR) to Masimo Rad-97 RR manual count. Each dot represents a data pair, with the colour intensity proportional to density of data pairs. Region A (in green) contains data pairs that are within 20% of the Masimo Rad-97 device value. Region B (in yellow) contains data pairs not within 20% that would not lead to unnecessary treatment. Regions C, D and E are in red. C includes data pairs leading to unnecessary treatment. D includes data pairs with a failure in detecting low or high HR/RR events and E includes data pairs where low and high HR/RR events are confused.

Figure 4

Bland-Altman plots for manual counts of respiratory rate (RR). (A) Open-label round. (B) Closed-label round one. (C) Closed-label round two. (D) Closed-label round three. Colours indicate which participant neonate is associated with the measurement pair.