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Accurate automated apnea analysis in preterm infants
Brooke D Vergales1, Alix O Paget-Brown1, Hoshik Lee2
1Division of Neonatology, Department of Pediatrics, University of Virginia, Charlottesville, Virginia.
American Journal of Perinatology
|April 18, 2013
Summary
Accurate apnea of prematurity (AOP) detection is crucial for research. A computer algorithm analyzing bedside monitor data proved more reliable than nursing records for quantifying AOP events in premature infants.
Area of Science:
- Neonatal Medicine
- Medical Device Technology
- Clinical Research
Background:
- The 2006 apnea of prematurity (AOP) consensus group highlighted inaccurate apnea episode counting as a significant research impediment.
- Reliable quantification of AOP events is essential for advancing research and clinical understanding.
Purpose of the Study:
- To compare the accuracy of nursing records in documenting apnea of prematurity (AOP) events against a validated computer algorithm.
- To evaluate the reliability of automated detection of apnea, bradycardia, and desaturation (ABD) events using standard bedside monitors.
Main Methods:
- Continuous collection of waveform, vital sign, and alarm data from very low-birth-weight infants over 25 months.
- Analysis of data for central apnea, bradycardia, and desaturation (ABD) events, defined by an algorithm as apnea >10 seconds with bradycardia and desaturation.
- Comparison of algorithm-detected events with nursing documentation from patient charts.
Main Results:
- Only 68% of nurse-recorded events correlated with algorithm-detected ABD events.
- A significant discrepancy was found, with only 26% of algorithm-detected prolonged apnea events (>30 seconds) documented by nurses within an hour.
- Monitor alarms were present for only 74% of algorithm-detected prolonged apnea events (>10 seconds), and alarms were frequent (1 every 2-3 minutes per nurse).
Conclusions:
- An automated computer algorithm for continuous ABD quantitation offers superior reliability compared to medical records for AOP research.
- This technology addresses the critical need for accurate event quantification identified by the 2006 AOP consensus group.
- Automated monitoring systems are vital for advancing research in apnea of prematurity.
