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Am J Perinatol 2014; 31(02): 157-162
DOI: 10.1055/s-0033-1343769
Original Article
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Accurate Automated Apnea Analysis in Preterm Infants

Brooke D. Vergales
1   Division of Neonatology, Department of Pediatrics, University of Virginia, Charlottesville, Virginia
,
Alix O. Paget-Brown
1   Division of Neonatology, Department of Pediatrics, University of Virginia, Charlottesville, Virginia
,
Hoshik Lee
2   Department of Physics, The College of William and Mary, Williamsburg, Virginia
,
Lauren E. Guin
3   Department of Internal Medicine, University of Virginia, Charlottesville, Virginia
,
Terri J. Smoot
3   Department of Internal Medicine, University of Virginia, Charlottesville, Virginia
,
Craig G. Rusin
3   Department of Internal Medicine, University of Virginia, Charlottesville, Virginia
6   Division of Cardiology, Department of Pediatrics, Texas Children's Hospital, Baylor College of Medicine, Houston, Texas
,
Matthew T. Clark
4   Department of Chemical Engineering, University of Virginia, Charlottesville, Virginia
,
John B. Delos
2   Department of Physics, The College of William and Mary, Williamsburg, Virginia
,
Karen D. Fairchild
1   Division of Neonatology, Department of Pediatrics, University of Virginia, Charlottesville, Virginia
,
Douglas E. Lake
3   Department of Internal Medicine, University of Virginia, Charlottesville, Virginia
5   Department of Statistics, University of Virginia, Charlottesville, Virginia
,
Randall Moorman
3   Department of Internal Medicine, University of Virginia, Charlottesville, Virginia
,
John Kattwinkel
1   Division of Neonatology, Department of Pediatrics, University of Virginia, Charlottesville, Virginia
› Author Affiliations
Further Information

Publication History

30 November 2012

25 February 2013

Publication Date:
16 April 2013 (online)

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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.

Keywords

apnea of prematurity - newborn intensive care - medical record - patient monitoring
 

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