At least 60% of the neonates with opioid withdrawal syndrome (NOWS) require morphine to control withdrawal symptoms. Currently, the morphine dosing strategies are empiric, not optimal and associated with longer hospital stay. The aim of the study was to develop a quantitative, model-based, real-world data-driven approach to morphine dosing to improve clinical outcomes, such as reducing time on treatment. Longitudinal morphine dose, clinical response (Modified Finnegan Score (MFS)), and baseline risk factors were collected using a retrospective cohort design from the electronic medical records of neonates with NOWS (N = 177) admitted to the University of Maryland Medical Center. A dynamic linear mixed effects model was developed to describe the relationship between MFS and morphine dose adjusting for baseline risk factors using a split-sample data approach (70% training: 30% test). The training model was evaluated in the test dataset using a simulation based approach. Maternal methadone and benzodiazepine use, and race were significant predictors of the MFS response. Positive autocorrelations of 0.56 and 0.12 were estimated between consecutive MFS responses. On an average, for a 1,000 μg increase in the morphine dose, the MFS decreased by 0.3 units. The model evaluation showed that observed and predicted median time on treatment were similar (13.0 vs. 13.8 days). A model-based framework was developed to describe the MFSmorphine dose relationship using real-world data that could potentially be used to develop an adaptive, individualized morphine dosing strategy to improve clinical outcomes in infants with NOWS. Neonatal opioid withdrawal syndrome (NOWS), also known as neonatal abstinence syndrome, is a drug withdrawal syndrome experienced by 55-94% of in utero opioid-exposed neonates shortly after birth. 1 The dramatic increase of opioid use in pregnancy has led to a 5-fold increase in NOWS with an incidence of 1.5-8.0 per 1,000 hospital births from 2004 to 2014. 2-4 On average in the United States, 1 infant is born every

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Readily available store brand, or “home,” thermometers are used countless times in the home and clinic as a first diagnostic measure of body temperature. Measurement inaccuracies may lead to unnecessary medical visits or medication (false positives), or, potentially worse, lack of intervention when a person is truly sick (false negatives). A critical first step in the design process is to determine the shortcomings of the existing designs. For this project, we evaluated the accuracy of three currently available store brand thermometers in a pediatric population. The accuracies of the thermometers were assessed by comparing their body temperature predictions to those measured by a specially designed and calibrated and fast-responding reference thermometer. The reference thermometer was placed at the measurement site simultaneously with the store brand thermometer and recorded the temperature at the measurement site continuously. More than 300 healthy or sick pediatric subjects were enrolled in this study. Temperatures were measured at both the oral and axillary (under the arm) sites. The store brand thermometer measurements characteristically deviated from the reference thermometer temperature after 120 s, and the deviations did not follow a consistent pattern. The Brand C thermometers had the greatest deviations of up to 3.7 °F (2.1 °C), while the Brand A thermometers had the lowest deviations; however, they still deviated by up to 1.9 °F (1.1 °C). The data showed that the tested store brand thermometers had lower accuracy than the ±0.2 °F (0.1 °C) indicated in their Instructions for Use. Our recorded reference (transient) data showed that there was a wide variation in the transient temperature profiles. The store brand thermometers tested stated in their documentation that they are able to predict a body temperature based on transient temperature values over the first 5–10 s of measurements, implying that they use an embedded algorithm to extrapolate to the steady-state temperature. Significant deviations from the maximum temperature after time t = 4.6t0.63 illustrated that the transient temperature profiles may not be represented by an exponential function with a single time constant, t0.63. The accuracy of those embedded algorithms was not confirmed by our study, since the predicted body temperatures do not capture the large variations observed over the initial 10 s of the measurements. A thermometer with an error of several degrees Fahrenheit may result in a false positive or negative diagnosis of fever in children. The transient temperature measurements from our clinical study represent unique and critical data for helping to design the next generation of readily available, highly accurate, home thermometers.