Modeling heart rate patterns to quantify neonatal opioid withdrawal syndrome.

Area of Science:

• Neonatalogy
• Pharmacology
• Physiology

Background:

• Neonatal Opioid Withdrawal Syndrome (NOWS) management relies on subjective, intermittent assessments.
• Opioid withdrawal impacts autonomic function, affecting heart rate (HR) and oxygenation (SpO2).
• Objective physiological markers are needed for accurate NOWS assessment and clinical care guidance.

Purpose of the Study:

• To compare HR and SpO2 metrics between infants with treated NOWS (tNOWS) and controls from 24-48 hours post-birth.
• To develop a predictive model for tNOWS risk.
• To correlate model risk scores with clinical assessments (Eat, Sleep, Console).

Main Methods:

• Inclusion of term infants with tNOWS and non-opioid-exposed controls from three academic NICUs.
• Calculation of HR and SpO2 metrics within 24-48 hours post-birth.
• Utilizing multivariable logistic regression to identify tNOWS and assess model performance (AUC=0.758).

Main Results:

• Study included 64 infants with tNOWS and 96 controls.
• Higher HR and increased HR variability were significantly associated with tNOWS.
• A logistic regression model based on HR metrics demonstrated good performance in identifying tNOWS.

Conclusions:

• Distinct HR patterns can identify NOWS in term infants.
• A predictive model using continuous HR data offers a noninvasive method to assess withdrawal severity.
• Clinicians can potentially use these risk estimates for targeted interventions in infants with NOWS.