Accurate automated apnea analysis in preterm infants

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 > 10 seconds 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 > 30 seconds, 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.

Fig. 1

Algorithm-detected episode of central neonatal apnea. This constellation of signals is consistent with an episode of central apnea with bradycardia and desaturation. Time 0 is the center point of the apnea event. The results of the automated detection appear below the clinical data tracings as described in text. Abbreviations: ABD, apnea, bradycardia, and desaturation event; bpm, beats per minute; EKG, electrocardiograph; IP, impedance pneumography; SpO₂, pulse oximetry.