PLoS ONE (Jan 2022)

Evaluation of Sibel's Advanced Neonatal Epidermal (ANNE) wireless continuous physiological monitor in Nairobi, Kenya.

  • Jesse Coleman,
  • Amy Sarah Ginsburg,
  • William Macharia,
  • Roseline Ochieng,
  • Dorothy Chomba,
  • Guohai Zhou,
  • Dustin Dunsmuir,
  • Shuai Xu,
  • J Mark Ansermino

DOI
https://doi.org/10.1371/journal.pone.0267026
Journal volume & issue
Vol. 17, no. 6
p. e0267026

Abstract

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BackgroundNeonatal multiparameter continuous physiological monitoring (MCPM) technologies assist with early detection of preventable and treatable causes of neonatal mortality. Evaluating accuracy of novel MCPM technologies is critical for their appropriate use and adoption.MethodsWe prospectively compared the accuracy of Sibel's Advanced Neonatal Epidermal (ANNE) technology with Masimo's Rad-97 pulse CO-oximeter with capnography and Spengler's Tempo Easy reference technologies during four evaluation rounds. We compared accuracy of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), and skin temperature using Bland-Altman plots and root-mean-square deviation analyses (RMSD). Sibel's ANNE algorithms were optimized between each round. We created Clarke error grids with zones of 20% to aid with clinical interpretation of HR and RR results.ResultsBetween November 2019 and August 2020 we collected 320 hours of data from 84 neonates. In the final round, Sibel's ANNE technology demonstrated a normalized bias of 0% for HR and 3.1% for RR, and a non-normalized bias of -0.3% for SpO2 and 0.2°C for temperature. The normalized spread between 95% upper and lower limits-of-agreement (LOA) was 4.7% for HR and 29.3% for RR. RMSD for SpO2 was 1.9% and 1.5°C for temperature. Agreement between Sibel's ANNE technology and the reference technologies met the a priori-defined thresholds for 95% spread of LOA and RMSD. Clarke error grids showed that all HR and RR observations were within a 20% difference.ConclusionOur findings suggest acceptable agreement between Sibel's ANNE and reference technologies. Clinical effectiveness, feasibility, usability, acceptability, and cost-effectiveness investigations are necessary for large-scale implementation.