Development and Validation of a Model to Predict Neonatal Abstinence Syndrome

Independent Impact of Eat, Sleep, Console Assessment on Neonatal Opioid Withdrawal Syndrome

Compared with the Finnegan Neonatal Abstinence Scoring System (FNASS), the Eat, Sleep, Console (ESC) approach reduces pharmacotherapy and length of stay (LOS) for neonatal opioid withdrawal syndrome (NOWS) infants. The independent outcome contribution of ESC is unknown as the approach combines ESC assessment with additional management changes. Our objective was to evaluate ESC assessment's independent impact on outcomes compared with FNASS. We conducted a retrospective cohort study of in utero opioid-exposed infants ≥35 weeks gestation managed with FNASS versus ESC. Outcomes included pharmacotherapy initiation, LOS, length of pharmacotherapy, and emergency department visit/readmissions. Among 151 FNASS and 100 ESC managed infants, pharmacotherapy initiation (P = .47), LOS for all infants (P = .49), and LOS for pharmacologically treated infants (P = .68) were similar. Length of pharmacotherapy did not differ (P = .84). Emergency department evaluation/NOWS readmission was equally rare (P = .65). Using equivalent models of care, comparison of ESC and FNASS assessment tools showed no difference in NOWS outcomes.

Performance of Phenotype Algorithms for the Identification of Opioid-Exposed Infants

OBJECTIVE

Observational studies examining outcomes among opioid-exposed infants are limited by phenotype algorithms that may under identify opioid-exposed infants without neonatal opioid withdrawal syndrome (NOWS). We developed and validated the performance of different phenotype algorithms to identify opioid-exposed infants using electronic health record data.

METHODS

We developed phenotype algorithms for the identification of opioid-exposed infants among a population of birthing person-infant dyads from an academic health care system (2010-2022). We derived phenotype algorithms from combinations of 6 unique indicators of in utero opioid exposure, including those from the infant record (NOWS or opioid-exposure diagnosis, positive toxicology) and birthing person record (opioid use disorder diagnosis, opioid drug exposure record, opioid listed on medication reconciliation, positive toxicology). We determined the positive predictive value (PPV) and 95% confidence interval for each phenotype algorithm using medical record review as the gold standard.

RESULTS

Among 41 047 dyads meeting exclusion criteria, we identified 1558 infants (3.80%) with evidence of at least 1 indicator for opioid exposure and 32 (0.08%) meeting all 6 indicators of the phenotype algorithm. Among the sample of dyads randomly selected for review (n = 600), the PPV for the phenotype requiring only a single indicator was 95.4% (confidence interval: 93.3-96.8) with varying PPVs for the other phenotype algorithms derived from a combination of infant and birthing person indicators (PPV range: 95.4-100.0).

CONCLUSIONS

Opioid-exposed infants can be accurately identified using electronic health record data. Our publicly available phenotype algorithms can be used to conduct research examining outcomes among opioid-exposed infants with and without NOWS.

Sex differences in neonatal outcomes following prenatal opioid exposure

The impact of the opioid epidemic on pregnant people and children is a growing public health crisis. Understanding how opioids affect the developing brain during pregnancy and postnatally remains a critical area of investigation. Biological sex plays a crucial role in all physiologic processes, with the potential for a significant impact on neonatal outcomes, including those infants with opioid exposure. Here, we aim to explore current literature on the effect of sex on neonatal outcomes following prenatal opioid exposure. Sex differences in adults with opioid use disorder have been well studied, including increased mortality among males and higher rates of psychiatric comorbidities and likelihood of relapse in females. However, such differences are not yet well understood in neonates. Emerging clinical data suggest sex-specific effects in infants with prenatal opioid exposure on the expression of genes related to feeding regulation and reward signaling pathways. Increased susceptibility to white matter injury has also been noted in female infants following prenatal opioid exposure. Understanding the impact of sex as a biological variable on neonatal outcomes following prenatal opioid exposure is paramount to improving the health and well-being of infants, children, and adults impacted by the opioid epidemic.

Can umbilical cord testing add to maternal urine drug screen for evaluation of infants at risk of neonatal opioid withdrawal syndrome?

OBJECTIVE

This study evaluated maternal urine drug screen (UDS) at delivery and umbilical cord drug testing and its association with neonatal opioid withdrawal syndrome (NOWS) diagnosis and severity following opioid exposed pregnancy.

METHODS

A retrospective chart review of 770 mother-infant dyads at five birthing hospitals in the United States Appalachian region for a five-year period was performed. Variables of interest included dyad demographics, results of maternal UDS at delivery and umbilical cord drug testing, and three neonatal outcomes: NOWS diagnosis, pharmacologic treatment administered for NOWS, and length of hospital stay (LOS) of the newborn.

RESULTS

Opioid-positivity was between 8.5% and 66.3% based on maternal UDS at delivery or umbilical cord testing. Odds of NOWS diagnosis and increased infant LOS was best associated with opioid detection in maternal UDS alone (OR = 5.62, 95% CI [3.06, 10.33] and OR = 8.33, 95% CI [3.67, 18.89], respectively). However, odds of pharmacologic treatment for NOWS was best associated with opioid detection in both maternal UDS and umbilical cord testing on the same dyad (OR = 3.22, 95% CI [1.14, 9.09]).

CONCLUSION

Maternal UDS is a better option compared to umbilical cord testing for evaluation of opioid-exposed infants and risk of NOWS diagnosis and increased infant LOS.

Predicting Heterogeneity in Patient Response to Morphine Treatment for Neonatal Opioid Withdrawal Syndrome

Infants with neonatal opioid withdrawal syndrome commonly receive morphine treatment to manage their withdrawal signs. However, the effectiveness of this pharmacotherapy in managing the infants' withdrawal signs vary widely. We sought to understand how information available early in infant monitoring can anticipate this treatment response, focusing on early modified Finnegan Neonatal Abstinence Scoring System (FNASS) scores, polygenic risk for opioid dependence (polygenic risk score (PRS)), and drug exposure. Using k-means clustering, we divided the 213 infants in our cohort into 3 groups based on their FNASS scores in the 12 hours before and after the initiation of pharmacotherapy. We found that these groups were pairwise significantly different for risk factors, including methadone exposure, and for in-hospital outcomes, including total morphine received, length of stay, and highest FNASS score. Whereas PRS was not predictive of receipt of treatment, PRS was pairwise significantly different between a subset of the groups. Using tree-based machine learning methods, we then constructed network graphs of the relationships among these groups, FNASS scores, PRS, drug exposures, and in-hospital outcomes. The resulting networks also showed meaningful connection between early FNASS scores and PRS, as well as between both of those and later in-hospital outcomes. These analyses present clinicians with the opportunity to better anticipate infant withdrawal progression and prepare accordingly, whether with expedited morphine treatment or non-pharmacotherapeutic alternative treatments.

Office-Based Management of Perinatal Substance Use and Substance Use Disorder for the General Obstetrician-Gynecologist

This is a review of substance use and substance use disorder in pregnancy, intended for the generalist obstetrician-gynecologist. Herein, the authors discuss legal considerations, outline definitions, review screening tools, introduce special considerations and harm reduction, caution the use of urinary toxicology testing, and touch on the screening, brief intervention, and referral to treatment model. Furthermore, the authors provide a brief overview of the prevalence, maternal and neonatal risks, and treatment approaches for commonly used substances.

Transfer Timing and the Length of Medication Weaning for Neonatal Opioid Withdrawal Syndrome

OBJECTIVE

Many infants with neonatal opioid withdrawal syndrome (NOWS) from prenatal exposure to opioids require transfer to a pediatric inpatient unit for medication weaning. The purpose of this study is to assess the difference in the duration of medication weaning between infants transferred by day of life (DOL) 14 versus later (DOL 15 and after) to a tertiary care setting for pharmacological and nonpharmacological management of NOWS.

METHODS

This single-site retrospective cohort study uses medical chart data from infants with NOWS transferred to specialized care between May 2016 and June 2021 (n = 87). The primary outcome is length of medication weaning, calculated as the number of days between transfer from the NICU to a tertiary care setting and the cessation of pharmacotherapy.

RESULTS

The majority of the infants in this sample are transferred from acute to tertiary care after DOL 15 (62% versus 38% by DOL 14). The predicted number of days to wean is 14.2 among those infants transferred by DOL 14, whereas the duration of weaning is 6.6 days longer among the later transfer group (20.8 days), adjusting for key covariates. The duration of weaning is also prolonged among infants with greater NOWS symptom severity and with prenatal exposure to psychotropic medications.

CONCLUSIONS

Delayed treatment prolongs NOWS symptoms and increases the burden on the health care system. Earlier referral from NICUs to pediatric inpatient units with environmental supports could reduce prolonged medication exposure and length of hospitalization for infants diagnosed with NOWS.

Polygenic risk scores and the need for pharmacotherapy in neonatal abstinence syndrome

BACKGROUND

The aim of this study was to identify genetic variants associated with NAS through a genome-wide association study (GWAS) and estimate a Polygenic Risk Score (PRS) model for NAS.

METHODS

A prospective case-control study included 476 in utero opioid-exposed term neonates. A GWAS of 1000 genomes-imputed genotypes was performed to identify variants associated with need for pharmacotherapy for NAS. PRS models for estimating genetic predisposition were generated via a nested cross-validation approach using 382 neonates of European ancestry. PRS predictive ability, discrimination, and calibration were assessed.

RESULTS

Cross-ancestry GWAS identified one intergenic locus on chromosome 7 downstream of SNX13 exhibiting genome-wide association with need for pharmacotherapy. PRS models derived from the GWAS for a subset of the European ancestry neonates reliably discriminated between need for pharmacotherapy using cis variant effect sizes within validation sets of European and African American ancestry neonates. PRS were less effective when applying variant effect sizes across datasets and in calibration analyses.

CONCLUSIONS

GWAS has the potential to identify genetic loci associated with need for pharmacotherapy for NAS and enable development of clinically predictive PRS models. Larger GWAS with additional ancestries are needed to confirm the observed SNX13 association and the accuracy of PRS in NAS risk prediction models.

IMPACT

Genetic associations appear to be important in neonatal abstinence syndrome. This is the first genome-wide association in neonates with neonatal abstinence syndrome. Polygenic risk scores can be developed examining single-nucleotide polymorphisms across the entire genome. Polygenic risk scores were higher in neonates receiving pharmacotherapy for treatment of their neonatal abstinence syndrome. Future studies with larger cohorts are needed to better delineate these genetic associations.

Prediction model for nonopiate-induced neonatal abstinence syndrome

BACKGROUND

Neonatal abstinence syndrome (NAS) induced by opiate use is common worldwide. Psychiatric drugs are a more common cause of NAS in Japan but infants of mothers taking psychiatric medications do not always develop NAS. The purpose of this study was to develop a practical model for predicting the onset of nonopiate-induced NAS, using variables available at birth.

METHODS

In this diagnostic study, prediction models were developed using multivariable logistic regression with retrospective data collected at our hospital between 2010 and 2019. The NAS diagnosis was based on the Isobe score, and maternal medications were converted to dose equivalents.

RESULTS

A total of 164 maternal and infant dyads met the inclusion criteria; 91 were included in the analysis, of whom 29 infants (32%) were diagnosed with NAS. Final models were created with and without the drug indices. The model without the drug indices consisted of neonatal head circumference in z-scores and Apgar scores at 5 min < 9, and the model with the drug indices included these, as well as antipsychotics and hypnotics indices. The C-statistics were 0.747 (95% CI: 0.638-0.856), and 0.795 (95% CI: 0.683-0.907), respectively, indicating that the models possessed good predictive accuracy for NAS onset.

CONCLUSIONS

This study developed models that predicted nonopiate-induced NAS accurately. They may be further improved through the use of drug indices.

Do small for gestational age infants have less severe neonatal abstinence syndrome?

BACKGROUND

Small for gestational age (SGA) infants are likely to have decreased placental transfer of opioids and other substances and lower amounts of fat deposition, hence less severe neonatal abstinence syndrome (NAS). The goal of this study is to correlate SGA status and severity of NAS in infants admitted to the neonatal intensive care unit (NICU).

METHODS

This is a retrospective analysis of term and late-preterm infants (≥35 weeks gestation) exposed to in-utero substances, born between September 2006 and May 2021, and admitted to an inner-city NICU for medical therapy for NAS. Indicators of the severity of NAS (duration of medical treatment, duration of hospitalization, use of phenobarbital, and use of clonidine) were compared between infants characterized as SGA (birth weight <10th percentile for gestational age) to those not categorized as SGA (non-SGA).

RESULTS

A total of 992 infants met the study criteria; 205 (20.7%) in the SGA group and 787 (79.3%) in the non-SGA group. The median duration of medical treatment was significantly lower in infants in the SGA group (22 days vs. 26 days, p = 0.04) and they were less likely to be treated with phenobarbital (19% vs. 26.8%, p = 0.02).

CONCLUSION

SGA infants displayed less severe NAS symptoms as indicated by shorter a duration of medical treatment and decreased need for phenobarbital. Our findings may impact decisions around identifying the optimum treatment protocols catered to SGA infants with NAS.

Neonatal Opioid Withdrawal Syndrome: A Comparison of As-Needed Pharmacotherapy

BACKGROUND AND OBJECTIVE

Methadone and morphine are commonly administered medications for neonatal opioid withdrawal syndrome (NOWS). Infants are increasingly treated with as-needed or "pro re nata" (PRN) medication. The optimal pharmacologic agent for PRN treatment of NOWS has not been examined. This study's objective is to compare NOWS hospital outcomes between infants treated with PRN methadone versus morphine.

METHODS

We performed a retrospective cohort study of infants pharmacologically treated for NOWS across 4 Massachusetts hospitals between January 2018 and February 2021. Infants born ≥36 weeks gestation with prenatal opioid exposure treated with PRN methadone or morphine were included. Mixed effects logistic and linear regression models were employed to evaluate differences in transition rates to scheduled dosing, length of stay, and number of PRN doses administered depending on PRN treatment agent.

RESULTS

There were 86 infants in the methadone group and 52 in the morphine group. There were no significant differences in NOWS hospital outcomes between groups in adjusted models: transition to scheduled dosing (methadone 31.6% vs morphine 28.6%, adjusted odds ratio 1.21, 95% confidence interval [CI] 0.87-1.19), mean length of stay (methadone 15.5 vs morphine 14.3 days, adjusted risk ratio 1.06, 95% CI 0.80-1.41), and the mean number of PRN doses (methadone 2.3 vs morphine 3.4, adjusted risk ratio 0.65, 95% CI 0.41-1.02). There was an association with nonpharmacologic care practices and improved NOWS hospital outcomes.

CONCLUSIONS

There were no significant differences in NOWS hospitalization outcomes based on pharmacologic agent type; nonpharmacologic care practices were most strongly associated with improved NOWS hospitalization outcomes.

Prenatal opioid-exposed infant extracellular miRNA signature obtained at birth predicts severity of neonatal opioid withdrawal syndrome

Prenatal opioid exposure (POE) is commonly associated with neonatal opioid withdrawal syndrome (NOWS), which is characterized by a broad variability in symptoms and severity. Currently there are no diagnostic tools to reliably predict which infants will develop severe NOWS, while risk stratification would allow for proactive decisions about appropriate clinical monitoring and interventions. The aim of this prospective cohort study was to assess if extracellular microRNAs (miRNAs) in umbilical cord plasma of infants with POE could predict NOWS severity. Participants (n = 58) consisted of pregnant women receiving medications for opioid use disorder and their infants. NOWS severity was operationalized as the need for pharmacologic treatment and prolonged hospitalization (≥ 14 days). Cord blood miRNAs were assessed using semi-quantitative qRT-PCR arrays. Receiver operating characteristic curves and area under the curve (AUC) were estimated. The expression of three miRNAs (miR-128-3p, miR-30c-5p, miR-421) predicted need for pharmacologic treatment (AUC: 0.85) and prolonged hospitalization (AUC: 0.90). Predictive validity improved after two miRNAs (let-7d-5p, miR-584-5p) were added to the need for pharmacologic treatment model (AUC: 0.94) and another two miRNAs (let-7b-5p, miR-10-5p) to the prolonged hospitalization model (AUC: 0.99). Infant cord blood extracellular miRNAs can proactively identify opioid-exposed neonates at high-risk for developing severe NOWS.

Standardizing the Clinical Definition of Opioid Withdrawal in the Neonate

OBJECTIVE

To standardize the clinical definition of opioid withdrawal in neonates to address challenges in clinical care, quality improvement, research, and public policy for this patient population.

STUDY DESIGN

Between October and December 2020, we conducted 2 modified-Delphi panels using ExpertLens, a virtual platform for performing iterative expert engagement panels. Twenty clinical experts specializing in care for the substance-exposed mother-neonate dyad explored the necessity of key evidence-based clinical elements in defining opioid withdrawal in the neonate leading to a diagnosis of neonatal abstinence syndrome (NAS)/neonatal opioid withdrawal syndrome (NOWS). Expert consensus was assessed using descriptive statistics, the RAND/UCLA Appropriateness Method, and thematic analysis of participants' comments.

RESULTS

Expert panels concluded the following were required for diagnosis: in utero exposure (known by history, not necessarily by toxicology testing) to opioids with or without the presence of other psychotropic substances, and the presence of at least two of the most common clinical signs characteristic of withdrawal (excessive crying, fragmented sleep, tremors, increased muscle tone, gastrointestinal dysfunction).

CONCLUSIONS

Results indicate that both a known history of in utero opioid exposure and a distinct set of withdrawal signs are necessary to standardize a definition of neonatal withdrawal. Implementation of a standardized definition requires both patient engagement and a mother-neonate dyadic approach mindful of program and policy implications.

Accuracy versus reliability-based modelling approaches for medical decision making

Forecasting in the medical domain is critical to the quality of decisions made by physicians, patients, and health planners. Modeling is one of the most important components of decision support systems, which are frequently used to simulate and analyze under-studied systems in order to make more appropriate decisions in medical science. In the medical modeling literature, various approaches with varying structures and characteristics have been proposed to cover a wide range of application categories and domains. Regardless of the differences between modeling approaches, all of them aim to maximize the accuracy or reliability of the results in order to achieve the most generalizable model and, as a result, a higher level of profitability decisions. Despite the theoretical significance and practical impact of reliability on generalizability, particularly in high-risk decisions and applications, a significant number of models in the fields of medical forecasting, classification, and time series prediction have been developed to maximize accuracy in mind. In other words, given the volatility of medical variables, it is also necessary to have stable and reliable forecasts in order to make sound decisions. The quality of medical decisions resulting from accuracy and reliability-based intelligent and statistical modeling approaches is compared and evaluated in this paper in order to determine the relative importance of accuracy and reliability on the quality of made decisions in decision support systems. For this purpose, 33 different case studies from the UCI in three categories of supervised modeling, namely causal forecasting, time series prediction, and classification, were considered. These cases were chosen from various domains, such as disease diagnosis (obesity, Parkinson's disease, diabetes, hepatitis, stenosis of arteries, orthopedic disease, autism) and cancer (lung, breast, cervical), experiments, therapy (immunotherapy, cryotherapy), fertility prediction, and predicting the number of patients in the emergency room and ICU. According to empirical findings, the reliability-based strategy outperformed the accuracy-based strategy in causal forecasting cases by 2.26%, classification cases by 13.49%, and time series prediction cases by 3.08%. Furthermore, compared to similar accuracy-based models, the reliability-based models can generate a 6.28% improvement. As a result, they can be considered an appropriate alternative to traditional accuracy-based models for medical decision support systems modeling purposes.