A multivariable logistic regression model using continuous heart rate data identified term infants with pharmacologically treated neonatal opioid withdrawal syndrome with an AUC of 0.758.
Cohort (n=160)
Yes
Do continuous heart rate patterns detect pharmacologically treated Neonatal Opioid Withdrawal Syndrome in term infants?
Continuous heart rate monitoring can objectively detect and quantify the severity of neonatal opioid withdrawal syndrome, potentially guiding targeted pharmacological interventions.
Effect estimate: AUC 0.758
Background: 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 (SpO 2 ) metrics could detect signatures of NOWS and provide an objective measure of NOWS to direct clinical care. Objective: To characterize differences in HR and SpO 2 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. Methods: We included term infants with tNOWS and controls admitted to one of three academic NICUs. We calculated HR and SpO 2 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. Results: 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. Conclusions: 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. Impact: 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.
Kausch et al. (Tue,) conducted a cohort in Neonatal Opioid Withdrawal Syndrome (NOWS) (n=160). Continuous heart rate monitoring and predictive modeling vs. Non-opioid-exposed controls was evaluated on Discrimination of infants with tNOWS from unexposed controls (AUC 0.758). A multivariable logistic regression model using continuous heart rate data identified term infants with pharmacologically treated neonatal opioid withdrawal syndrome with an AUC of 0.758.