Predictive Monitoring for respiratory decompensation leading to urgent unplanned intubation in the neonatal intensive care unit

Matthew T. Clark
John L. Hudson
Brooke D. Vergales
John B. Delos, William & Mary

Abstract

BACKGROUND: Infants admitted to the neonatal intensive care unit (NICU), and especially those born with very low birth weight (VLBW; <1,500 g), are at risk for respiratory decompensation requiring endotracheal intubation and mechanical ventilation. I ntubation and mechanical ventilation are associated with increased morbidity, particularly in urgent unplanned cases. METHODS: We tested the hypothesis that the systemic response associated with respiratory decombensation can be detected from physiological monitoring and that statistical models of bedside monitoring data can identify infants at increased risk of urgent unplanned intubation. We studied 287 VLBW infants consecutively admitted to our NICU and found 96 events in 51 patients, excluding intUbations occurring within. 12h of a previous extubation. RESULTS: In order of importance in a multivariable statistical model, we found that the characteristics of reduced O-2 satura, tion, especially as "heart rate was falling; increased heart rate correlation with respiratory rate; and the amount of apnea were aIF significant independent pr,edictors.' The predictive model, validated internally by bootStrap, had a receiver-operating characteristic area of 0.84 +/- 0.04. CONCLUSION: We propose that predictive monitoring in the NICU for urgent unplanned intubation may improve outcomes by allowing clinicians to intervene noninvasively before intubation is required.