Background <p>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.</p> Objective <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p> Impact <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Heart rate patterns identified NOWS in term infants.</p> </ItemContent> <ItemContent> <p>A predictive model using continuous HR data provides a noninvasive measure associated with withdrawal severity in infants requiring opioid replacement.</p> </ItemContent> <ItemContent> <p>Clinicians may be able to use the risk estimates produced by this model for targeted interventions for patients where treatment is indicated.</p> </ItemContent> </UnorderedList></p>