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AbstractBackgroundRespiratory disorders, including apnoea, are common in preterm infants due to their immature respiratory control and function compared with term-born infants. However, our inability to accurately measure respiratory rate in hospitalised infants results in unreported episodes of apnoea and an incomplete picture of respiratory dynamics.MethodsWe develop, validate and use a novel algorithm to identify inter-breath intervals (IBIs) and apnoeas in infants. In 42 infants (a total of 1600 hours of recordings) we assess IBIs from the chest electrical impedance pneumograph using an adaptive amplitude threshold for the detection of individual breaths. The algorithm is refined by comparing its accuracy with clinically-observed breaths and pauses in breathing. We also develop an automated classifier to differentiate periods of true central apnoea from artefactually low amplitude signal. We use this algorithm to explore its ability to identify morphine-induced respiratory depression in 15 infants. Finally, in 22 infants we use the algorithm to investigate whether retinopathy of prematurity (ROP) screening alters the IBI distribution.Findings88% of the central apnoeas identified using our algorithm were missed in the clinical notes. As expected, morphine caused a shift in the IBI distribution towards longer IBIs, with significant differences in all IBI metrics assessed. Following ROP screening, there was a shift in the IBI distribution with a significant increase in the proportion of pauses in breathing that lasted more than 10 seconds (t-statistic=1.82, p=0.023). This was not reflected by changes in the monitor-derived respiratory rate or episodes of apnoea recorded on clinical charts.InterpretationBetter measurement of infant respiratory dynamics is essential to improve care for hospitalised infants. Use of the novel IBI algorithm demonstrates that following ROP screening increased instability in respiratory dynamics can be detected in the absence of clinically-significant apnoeas.FundingWellcome Trust and Royal SocietyResearch in ContextEvidence before this studyRespiratory disorders are one of the most common reasons for admission to a neonatal care unit and many pathologies and clinically-required procedures affect respiration. Despite this, current methods to measure respiratory rate in infants often provide inaccurate measurements due to factors such as poor electrode placement, movement artefact and cardiac interference. Lee and colleagues previously developed an algorithm to better identify episodes of apnoea in infants from the electrical impedance pneumograph following removal of cardiac-frequency interference. This algorithm substantially improves apnoea detection and demonstrates the high number of apnoeas that are missed in medical records. However, false apnoeas can be detected during periods of low amplitude signal caused by shallow breathing or poor electrode placement, and shorter inter-breath intervals (IBIs) cannot be assessed using the method proposed by Lee et al. limiting its use in assessing more subtle changes in an infant’s respiratory dynamics.Added value of this studyWe develop, test and use a new algorithm for the identification of IBIs from the electrical impedance pneumograph. We use an adaptive amplitude threshold for the identification of breaths and develop a classification model to remove periods of low amplitude signal falsely identified as episodes of apnoea. Using the algorithm, we demonstrate that retinopathy of prematurity (ROP) screening causes a significant increase in pauses in breathing that last more than 10 seconds. Our apnoea detection method was more sensitive than the current standard monitor-derived approach that is used to monitor respiratory rate in neonatal care units.Implications of all the available evidenceTo improve understanding of infant respiratory dynamics, better methods of assessment are essential. This will create a more complete clinical understanding of infant well-being, that will lead to improved treatment options for infants with respiratory disorders.

Original publication

DOI

10.1101/2021.05.29.21258043

Type

Journal article

Publisher

Cold Spring Harbor Laboratory

Publication Date

01/06/2021