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Accurate Automated Apnea Analysis in Preterm Infants
Vergales, Brooke D. Paget-Brown, Alix O. Lee, Hoshik Delos, John B.
Vergales, Brooke D.
Paget-Brown, Alix O.
Lee, Hoshik
Delos, John B.
Abstract
Objective In 2006 the apnea of prematurity (AOP) consensus group identified inaccurate counting of apnea episodes as a major barrier to progress in AOP research. We compare nursing records of AOP to events detected by a clinically validated computer algorithm that detects apnea from standard bedside monitors. Study Design Waveform, vital sign, and alarm data were collected continuously from all very low-birth-weight infants admitted over a 25-month period, analyzed for central apnea, bradycardia, and desaturation (ABD) events, and compared with nursing documentation collected from charts. Our algorithm defined apnea as>10seconds if accompanied by bradycardia and desaturation. Results Of the 3,019 nurse-recorded events, only 68% had any algorithm-detected ABD event. Of the 5,275 algorithm-detected prolonged apnea events>30seconds, only 26% had nurse-recorded documentation within 1 hour. Monitor alarms sounded in only 74% of events of algorithm-detected prolonged apnea events>10 seconds. There were 8,190,418 monitor alarms of any description throughout the neonatal intensive care unit during the 747 days analyzed, or one alarm every 2 to 3 minutes per nurse. Conclusion An automated computer algorithm for continuous ABD quantitation is a far more reliable tool than the medical record to address the important research questions identified by the 2006 AOP consensus group.
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2014-02-01
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Physics
DOI
https://doi.org/10.1055/s-0033-1343769