Modeling heart rate patterns to quantify neonatal opioid withdrawal syndrome.

Abstract

Neonatal Opioid Withdrawal Syndrome (NOWS) is managed using intermittent, observation-based assessments. Opioid withdrawal causes autonomic dysfunction, altering control of heart rate and breathing. We hypothesized that heart rate (HR) and oxygenation (SpO₂) metrics could detect signatures of NOWS and provide an objective measure of NOWS to direct clinical care. To characterize differences in HR and SpO₂ for infants with pharmacologically treated NOWS (tNOWS) versus non-opioid-exposed controls in the period from 24 to 48 h after birth, and to model the risk of tNOWS. We included term infants with tNOWS and controls admitted to one of three academic NICUs. We calculated HR and SpO₂ metrics in the 24 to 48 h after birth. We used multivariable logistic regression to detect tNOWS and examined the relationship between model risk scores and contemporaneous Eat, Sleep, Console (ESC) assessments. We studied 64 infants with tNOWS and 96 control infants. Higher HR and increased HR variability were associated with tNOWS. A logistic regression model using HR-based metrics identified infants with tNOWS with an AUC of 0.758. HR patterns detected tNOWS in term infants. A predictive model using continuous HR data provides a noninvasive measure associated with withdrawal severity in infants requiring opioid replacement. Heart rate patterns identified NOWS in term infants. A predictive model using continuous HR data provides a noninvasive measure associated with withdrawal severity in infants requiring opioid replacement. Clinicians may be able to use the risk estimates produced by this model for targeted interventions for patients where treatment is indicated.