Accurate automated apnea analysis in preterm infants

Am J Perinatol. 2014 Feb;31(2):157-62. doi: 10.1055/s-0033-1343769. Epub 2013 Apr 16.

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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Apnea / diagnosis*
  • Diagnosis, Computer-Assisted*
  • Electrocardiography
  • Humans
  • Infant, Newborn
  • Infant, Premature
  • Infant, Premature, Diseases / diagnosis*
  • Intensive Care Units, Neonatal
  • Monitoring, Physiologic / methods*
  • Plethysmography, Impedance