Identification of thresholds for accuracy comparisons of heart rate and respiratory rate in neonates

Background: Heart rate (HR) and respiratory rate (RR) can be challenging
to measure accurately and reliably in neonates. The introduction of
innovative, non-invasive measurement technologies suitable for
resource-constrained settings is limited by the lack of appropriate
clinical thresholds for accuracy comparison studies. Methods: We collected
measurements of photoplethysmography-recorded HR and capnography-recorded
exhaled carbon dioxide across multiple 60-second epochs (observations) in
enrolled neonates admitted to the neonatal care unit at Aga Khan
University Hospital in Nairobi, Kenya. Trained study nurses manually
recorded HR, and the study team manually counted individual breaths from
capnograms. For comparison, HR and RR also were measured using an
automated signal detection algorithm. Clinical measurements were analyzed
for repeatability. Results: A total of 297 epochs across 35 neonates were
recorded. Manual HR showed a bias of -2.4 (-1.8%) and a spread between the
95% limits of agreement (LOA) of 40.3 (29.6%) compared to the
algorithm-derived median HR. Manual RR showed a bias of -3.2 (-6.6%) and a
spread between the 95% LOA of 17.9 (37.3%) compared to the
algorithm-derived median RR, and a bias of -0.5 (1.1%) and a spread
between the 95% LOA of 4.4 (9.1%) compared to the algorithm-derived RR
count. Manual HR and RR showed repeatability of 0.6 (interquartile range
(IQR) 0.5-0.7), and 0.7 (IQR 0.5-0.8), respectively. Conclusions:
Appropriate clinical thresholds should be selected a priori when
performing accuracy comparisons for HR and RR. Automated measurement
technologies typically use a smoothing or averaging filter, which
significantly impacts accuracy. A wider spread between the LOA, as much as
30%, should be considered to account for the observed physiological
nuances and within- and between-neonate variability and different
averaging methods. Wider adoption of thresholds by data standards
organizations and technology developers and manufacturers will increase
the robustness of clinical comparison studies.