Risk of Autism after Prenatal Topiramate, Valproate, or Lamotrigine Exposure.

BACKGROUND: Maternal use of valproate during pregnancy has been associated with an increased risk of neurodevelopmental disorders in children. Although most studies of other antiseizure medications have not shown increased risks of these disorders, there are limited and conflicting data regarding the risk of autism spectrum disorder associated with maternal topiramate use. METHODS: We identified a population-based cohort of pregnant women and their children within two health care utilization databases in the United States, with data from 2000 through 2020. Exposure to specific antiseizure medications was defined on the basis of prescription fills from gestational week 19 until delivery. Children who had been exposed to topiramate during the second half of pregnancy were compared with those unexposed to any antiseizure medication during pregnancy with respect to the risk of autism spectrum disorder. Valproate was used as a positive control, and lamotrigine was used as a negative control. RESULTS: The estimated cumulative incidence of autism spectrum disorder at 8 years of age was 1.9% for the full population of children who had not been exposed to antiseizure medication (4,199,796 children). With restriction to children born to mothers with epilepsy, the incidence was 4.2% with no exposure to antiseizure medication (8815 children), 6.2% with exposure to topiramate (1030 children), 10.5% with exposure to valproate (800 children), and 4.1% with exposure to lamotrigine (4205 children). Propensity score-adjusted hazard ratios in a comparison with no exposure to antiseizure medication were 0.96 (95% confidence interval [CI], 0.56 to 1.65) for exposure to topiramate, 2.67 (95% CI, 1.69 to 4.20) for exposure to valproate, and 1.00 (95% CI, 0.69 to 1.46) for exposure to lamotrigine. CONCLUSIONS: The incidence of autism spectrum disorder was higher among children prenatally exposed to the studied antiseizure medications than in the general population. However, after adjustment for indication and other confounders, the association was substantially attenuated for topiramate and lamotrigine, whereas an increased risk remained for valproate. (Funded by the National Institute of Mental Health.).

Buprenorphine versus Methadone for Opioid Use Disorder in Pregnancy.

BACKGROUND: Opioid agonist therapy is strongly recommended for pregnant persons with opioid use disorder. Buprenorphine may be associated with more favorable neonatal and maternal outcomes than methadone, but existing data are limited. METHODS: We conducted a cohort study involving pregnant persons who were enrolled in public insurance programs in the United States during the period from 2000 through 2018 in which we examined outcomes among those who received buprenorphine as compared with those who received methadone. Exposure to the two medications was assessed in early pregnancy (through gestational week 19), late pregnancy (gestational week 20 through the day before delivery), and the 30 days before delivery. Risk ratios for neonatal and maternal outcomes were adjusted for confounders with the use of propensity-score overlap weights. RESULTS: The data source for the study consisted of 2,548,372 pregnancies that ended in live births. In early pregnancy, 10,704 pregnant persons were exposed to buprenorphine and 4387 to methadone. In late pregnancy, 11,272 were exposed to buprenorphine and 5056 to methadone (9976 and 4597, respectively, in the 30 days before delivery). Neonatal abstinence syndrome occurred in 52.0% of the infants who were exposed to buprenorphine in the 30 days before delivery as compared with 69.2% of those exposed to methadone (adjusted relative risk, 0.73; 95% confidence interval [CI], 0.71 to 0.75). Preterm birth occurred in 14.4% of infants exposed to buprenorphine in early pregnancy and in 24.9% of those exposed to methadone (adjusted relative risk, 0.58; 95% CI, 0.53 to 0.62); small size for gestational age in 12.1% and 15.3%, respectively (adjusted relative risk, 0.72; 95% CI, 0.66 to 0.80); and low birth weight in 8.3% and 14.9% (adjusted relative risk, 0.56; 95% CI, 0.50 to 0.63). Delivery by cesarean section occurred in 33.6% of pregnant persons exposed to buprenorphine in early pregnancy and 33.1% of those exposed to methadone (adjusted relative risk, 1.02; 95% CI, 0.97 to 1.08), and severe maternal complications developed in 3.3% and 3.5%, respectively (adjusted relative risk, 0.91; 95% CI, 0.74 to 1.13). Results of exposure in late pregnancy were consistent with results of exposure in early pregnancy. CONCLUSIONS: The use of buprenorphine in pregnancy was associated with a lower risk of adverse neonatal outcomes than methadone use; however, the risk of adverse maternal outcomes was similar among persons who received buprenorphine and those who received methadone. (Funded by the National Institute on Drug Abuse.).

Metformin Use in the First Trimester of Pregnancy and Risk for Nonlive Birth and Congenital Malformations: Emulating a Target Trial Using Real-World Data.

BACKGROUND: Metformin is a first-line pharmacotherapy for type 2 diabetes, but there is limited evidence about its safety in early pregnancy. OBJECTIVE: To evaluate the teratogenicity of metformin use in the first trimester of pregnancy. DESIGN: In an observational cohort of pregnant women with pregestational type 2 diabetes receiving metformin monotherapy before the last menstrual period (LMP), a target trial with 2 treatment strategies was emulated: insulin monotherapy (discontinue metformin treatment and initiate insulin within 90 days of LMP) or insulin plus metformin (continue metformin and initiate insulin within 90 days of LMP). SETTING: U.S. Medicaid health care administration database (2000 to 2018). PARTICIPANTS: 12 489 pregnant women who met the eligibility criteria. MEASUREMENTS: The risk and risk ratio of nonlive births, live births with congenital malformations, and congenital malformations among live births were estimated using standardization to adjust for covariates. RESULTS: A total of 850 women were in the insulin monotherapy group and 1557 in the insulin plus metformin group. The estimated risk for nonlive birth was 32.7% under insulin monotherapy (reference) and 34.3% under insulin plus metformin (risk ratio, 1.02 [95% CI, 1.01 to 1.04]). The estimated risk for live birth with congenital malformations was 8.0% (CI, 5.7% to 10.2%) under insulin monotherapy and 5.7% (CI, 4.5% to 7.3%) under insulin plus metformin (risk ratio, 0.72 [CI, 0.51 to 1.09]). LIMITATION: Possible residual confounding by glycemic control and body mass index. CONCLUSION: Compared with switching to insulin monotherapy, continuing metformin and adding insulin in early pregnancy resulted in little to no increased risk for nonlive birth among women receiving metformin before pregnancy. Under conventional statistical criteria, anything between a 49% decrease and a 9% increase in risk for congenital malformations was highly compatible with our data. PRIMARY FUNDING SOURCE: National Institutes of Health.

Internal validation of gestational age estimation algorithms in health-care databases using pregnancies conceived through fertility procedures.

Fertility procedures recorded in health-care databases can be used to estimate the start of pregnancy, which can serve as a reference standard to validate gestational age estimates based on International Classification of Diseases codes. In a cohort of 17 398 US MarketScan pregnancies (2011-2020) in which conception was achieved via fertility procedures, we estimated gestational age at the end of pregnancy using algorithms based on (1) time (days) since the fertility procedure (the reference standard); (2) International Classification of Diseases, Ninth Revision (ICD-9)/International Classification of Diseases, Tenth Revision (ICD-10) (before/after October 2015) codes indicating gestational length recorded at the end of pregnancy (method A); and (3) ICD-10 end-of-pregnancy codes enhanced with Z3A codes denoting specific gestation weeks recorded at prenatal visits (method B). We calculated the proportion of pregnancies with an estimated gestational age falling within 14 days ($\pm$14 days) of the reference standard. Method A accuracy was similar for ICD-9 and ICD-10 codes. After 2015, method B was more accurate than method A: For term births, within-14-day agreement was 90.8% for method A and 98.7% for method B. Corresponding estimates were 70.1% and 95.6% for preterm births; 35.3% and 92.6% for stillbirths; 54.3% and 64.2% for spontaneous abortions; and 16.7% and 84.6% for elective terminations. ICD-10-based algorithms that incorporate Z3A codes improve the accuracy of gestational age estimation in health-care databases, especially for preterm births and non-live births.

Development and Validation of an Algorithm to Predict Stillbirth Gestational Age in Medicaid Billing Records.

INTRODUCTION: With Medicaid covering half of US pregnancies, Medicaid Analytic eXtract (MAX) provides a valuable data source to enrich understanding about stillbirth etiologies. OBJECTIVE: We developed and validated a claims-based algorithm to predict GA at stillbirth. METHOD: We linked the stillbirths identified in MAX 1999-2013 to Florida Fetal Death Records (FDRs) to obtain clinical estimates of GA (N=825). We tested several algorithms including using a fixed median GA, median GA at the time of specific prenatal screening tests, and expanded versions considering additional predictors of stillbirth within including linear regression and random forest models. We estimated the proportion of pregnancies with differences of ± 1, 2, 3 and 4 weeks between the predicted and FDR GA and the model mean square error (MSE). We validated the selected algorithms in two external samples. RESULTS: The best performing algorithm was a random forest model (MSE of 12.67 weeks2) with 84% of GAs within ± 4 weeks. Assigning a fixed GA of 28 weeks resulted in an MSE of 60.21 weeks2 and proportions of GA within ± 4 weeks of 32%. We observed consistent results in the external samples. DISCUSSION: Our prediction algorithm for stillbirths can facilitate pregnancy research in the Medicaid population.

Hierarchical Clustering Analysis to Inform Classification of Congenital Malformations for Surveillance of Medication Safety in Pregnancy.

There is growing interest in the secondary use of healthcare data to evaluate medication safety in pregnancy. Tree-based scan statistics (TBSS) offer an innovative approach to help identify potential safety signals. TBSS utilize hierarchically organized outcomes, generally based on existing clinical coding systems that group outcomes by organ system. When assessing teratogenicity, such groupings often lack a sound embryologic basis given the etiologic heterogeneity of congenital malformations. The study objective was to enhance the grouping of congenital malformations to be used in scanning approaches through implementation of hierarchical clustering analysis (HCA) and to pilot test an HCA-enhanced TBSS approach for medication safety surveillance in pregnancy in two test cases using >4.2 million mother-child dyads from two US-nationwide databases. HCA identified (1) malformation combinations belonging to the same organ system already grouped in existing classifications, (2) known combinations across different organ systems not previously grouped, (3) unknown combinations not previously grouped, and (4) malformations seemingly standing on their own. Testing the approach with valproate and topiramate identified expected signals, and a signal for an HCA-cluster missed by traditional classification. Augmenting existing classifications with clusters identified through large data exploration may be promising when defining phenotypes for surveillance and causal inference studies.

Development and validation of claims-based algorithms for estimating gestational age of spontaneous abortion and termination.

To study the risk of spontaneous abortion (SAB) or termination using healthcare utilization databases, algorithms to estimate the gestational age (GA) are needed. Using Medicaid data, we developed a hierarchical algorithm to classify pregnancy outcomes. We identified the subset of potential SAB and termination cases, and abstracted the GA from linked electronic medical records (gold standard). We developed three approaches: (1) assign median GA for SAB and termination cases in the US; (2) draw a random GA from the population distributions; (3) estimate GA based on regression models. Algorithm performance was assessed based on the proportion of pregnancies with estimated GA within 1-4 weeks of the gold standard, the mean squared error (MSE) and the R-squared. Approach 1 and Approach 3 had similar performance, though approach 3 using random forest models with variables selected via the Boruta algorithm had better MSE and R-squared. For SAB, 58.0% of pregnancies were correctly classified within 2 weeks of the gold standard (MSE: 8.7, R-squared: 0.09). For termination, the proportions were 66.3% (MSE: 11.7; R-squared: 0.35). SABs and terminations can be studied in healthcare utilization data with careful implementation of validated algorithms though higher level of GA misclassification is expected compared to live births.

Association Between Surgeon/Anesthesiologist Sex Discordance and 1-year Mortality Among Adults Undergoing Noncardiac Surgery: A Population-based Retrospective Cohort Study.

OBJECTIVE: To investigate the association between surgeon-anesthesiologist sex discordance and patient mortality after noncardiac surgery. BACKGROUND: Evidence suggests different practice patterns exist among female and male physicians. However, the influence of physician sex on team-based practices in the operating room and subsequent patient outcomes remains unclear in the context of noncardiac surgery. METHODS: We conducted a population-based, retrospective cohort study of adult Ontario residents who underwent index, inpatient noncardiac surgery between January 2007 and December 2017. The primary exposure was physician sex discordance (ie, the surgeon and anesthesiologist were of the opposite sex). The primary outcome was 1-year mortality. The association between physician sex discordance and patient outcomes was modeled using multivariable Cox proportional hazard regression with adjustment for relevant physician, patient, and hospital characteristics. RESULTS: Of 541,209 patients, 158,084 (29.2%) were treated by sex-discordant physician teams. Physician sex discordance was associated with a lower rate of mortality at 1 year [5.2% vs. 5.7%; adjusted HR: 0.95 (0.91-0.99)]. Patients treated by teams composed of female surgeons and male anesthesiologists were more likely to be alive at 1 year than those treated by all-male physician teams [adjusted HR: 0.90 (0.81-0.99)]. CONCLUSIONS: Noncardiac surgery patients had a lower likelihood of 1-year mortality when treated by sex-discordant surgeon-anesthesiologist teams. The likelihood of mortality was further reduced if the surgeon was female. Further research is needed to explore the underlying mechanisms of these observations and design strategies to diversify operating room teams to optimize performance and patient outcomes.

Maternal periconception hyperglycemia, preconception diabetes, and risk of major congenital malformations in offspring.

STUDY QUESTION: What are the roles of maternal preconception diabetes and related periconceptional hyperglycemia on the risk of major congenital malformations (MCMs) in offspring? SUMMARY ANSWER: Maternal periconceptional glycated hemoglobin (HbA1c) levels over 5.6% were associated with an increased risk of congenital heart defects (CHD) in the offspring, and maternal preconception diabetes was associated with an increased risk of CHD, including when HbA1c levels were within euglycemic ranges. WHAT IS KNOWN ALREADY: Maternal preconception diabetes has been linked with MCMs in the offspring. However, evidence concerning associations with specific periconception serum measures of hyperglycemia, and susceptibility of different organ systems, is inconsistent. Moreover, limited evidence exists concerning the effectiveness of antidiabetic medications in mitigating diabetes-related teratogenic risks. STUDY DESIGN, SIZE, DURATION: A large Israeli birth cohort of 46 534 children born in 2001-2020. PARTICIPANTS/MATERIALS, SETTING, METHODS: Maternal HbA1c test results were obtained from 90 days before conception to mid-pregnancy. Maternal diabetes, other cardiometabolic conditions, and MCMs in newborns were ascertained based on clinical diagnoses, medication dispensing records, and laboratory test results using previously validated algorithms. Associations were modeled using generalized additive logistic regression models with thin plate penalized splines. MAIN RESULTS AND THE ROLE OF CHANCE: Maternal periconceptional HbA1c value was associated with CHD in newborns, with the risk starting to increase at HbA1c values exceeding 5.6%. The association between HbA1c and CHD was stronger among mothers with type 2 diabetes mellitus (T2DM) compared to the other diabetes groups. Maternal pre-existing T2DM was associated with CHD even after accounting for HbA1C levels and other cardiometabolic comorbidities (odds ratio (OR)=1.89, 95% CI 1.18, 3.03); and the OR was materially unchanged when only mothers with pre-existing T2DM who had high adherence to antidiabetic medications and normal HbA1c levels were considered. LIMITATIONS, REASONS FOR CAUTION: The rarity of some specific malformation groups limited the ability to conduct more granular analyses. The use of HbA1c as a time-aggregated measure of glycemic control may miss transient glycemic dysregulation that could be clinically meaningful for teratogenic risks. WIDER IMPLICATIONS OF THE FINDINGS: The observed association between pre-existing diabetes and the risk of malformations within HbA1c levels suggests underlying causal pathways that are partly independent of maternal glucose control. Therefore, treatments for hyperglycemia might not completely mitigate the teratogenic risk associated with maternal preconception diabetes. STUDY FUNDING/COMPETING INTEREST(S): The work was supported by NIH grants K99ES035433, R01HD097778, and P30ES000002. None of the authors reports competing interests. TRIAL REGISTRATION NUMBER: N/A.

Brief Assessment of Patient Phenotype to Explain Variability in Postsurgical Pain and Opioid Consumption after Cesarean Delivery: Performance of a Novel Brief Questionnaire Compared to Long Questionnaires.

BACKGROUND: Understanding factors that explain why some women experience greater postoperative pain and consume more opioids after cesarean delivery is crucial to building an evidence base for personalized prevention. Comprehensive psychosocial assessment with validated questionnaires in the preoperative period can be time-consuming. A three-item questionnaire has shown promise as a simpler tool to be integrated into clinical practice, but its brevity may limit the ability to explain heterogeneity in psychosocial pain modulators among individuals. This study compared the explanatory ability of three models: (1) the 3-item questionnaire, (2) a 58-item questionnaire (long) including validated questionnaires (e.g., Brief Pain Inventory, Patient Reported Outcome Measurement Information System [PROMIS]) plus the 3-item questionnaire, and (3) a novel 19-item questionnaire (brief) assessing several psychosocial factors plus the 3-item questionnaire. Additionally, this study explored the utility of adding a pragmatic quantitative sensory test to models. METHODS: In this prospective, observational study, 545 women undergoing cesarean delivery completed questionnaires presurgery. Pain during local anesthetic skin wheal before spinal placement served as a pragmatic quantitative sensory test. Postoperatively, pain and opioid consumption were assessed. Linear regression analysis assessed model fit and the association of model items with pain and opioid consumption during the 48 h after surgery. RESULTS: A modest amount of variability was explained by each of the three models for postoperative pain and opioid consumption. Both the brief and long questionnaire models performed better than the three-item questionnaire but were themselves statistically indistinguishable. Items that were independently associated with pain and opioid consumption included anticipated postsurgical pain medication requirement, surgical anxiety, poor sleep, pre-existing pain, and catastrophic thinking about pain. The quantitative sensory test was itself independently associated with pain across models but only modestly improved models for postoperative pain. CONCLUSIONS: The brief questionnaire may be more clinically feasible than longer validated questionnaires, while still performing better and integrating a more comprehensive psychosocial assessment than the three-item questionnaire.

Prescription medication use during pregnancy in the United States from 2011 to 2020: trends and safety evidence.

BACKGROUND: Medication use during pregnancy has increased in the United States despite the lack of safety data for many medications. OBJECTIVE: This study aimed to inform research priorities by examining trends in medication use during pregnancy and identifying gaps in safety information on the most commonly prescribed medications. STUDY DESIGN: We identified population-based cohorts of commercially (MarketScan 2011-2020) and publicly (Medicaid Analytic eXtract/Transformed Medicaid Statistical Information System Analytic Files 2011-2018) insured pregnancies ending in live birth from 2 health care utilization databases. Medication use was based on filled prescriptions between the date of last menstrual period through delivery, as well as the period before the last menstrual period and during specific trimesters. We also included a cross-sectional representative sample of pregnancies ascertained by the National Health and Nutrition Examination Survey (2011-2020), with information on prescription medication use during the preceding month obtained through maternal interviews. Teratogen Information System was used to classify the available evidence on teratogenic risk. RESULTS: Among over 3 million pregnancies, the medications most commonly dispensed at any time during pregnancy were analgesics, antibiotics, and antiemetics. The top medications were ondansetron (16.8%), amoxicillin (13.5%), and azithromycin (12.4%) in MarketScan, nitrofurantoin (22.2%), acetaminophen (21.3%; mostly as part of acetaminophen-hydrocodone products), and ondansetron (19.5%) in Medicaid Analytic eXtract/Transformed Medicaid Statistical Information System Analytic Files, and levothyroxine (5.0%), sertraline (2.9%), and insulin (2.9%) in the National Health and Nutrition Examination Survey group. The most commonly dispensed suspected teratogens during the first trimester were antithyroid medications. The use of antidiabetic and psychotropic medications has continued to increase in the United States during the last decade, opioid dispensation has decreased by half, and antibiotics and antiemetics continue to be common. For one-quarter of medications, there is insufficient evidence available to characterize their safety profile in pregnancy. CONCLUSION: There is a need for more drug research in pregnant patients. Future research should focus on anti-infectives with high utilization and limited level of evidence on safety for use during pregnancy. Although lack of evidence is not evidence of safety concerns, it does not indicate risk either. In many instances, the benefits outweigh the risks when these medications are used clinically, and some of the medications with no proven safety may be necessary to treat patients.

Quality Improvement in the Digital Age: The Promise of Using Informatics to Improve Obstetric Anesthesia Care.

Informatics describes the study and use of processes for obtaining and utilizing data. In the clinical context, these data are then used to inform and educate providers to improve patient care. In the current digital age, informatic solutions can help clinicians to understand past or current quality issues (afferent tools), to benchmark personal performance against national averages (feedback tools), and to disseminate information to encourage best practice and quality care (efferent tools). There are countless examples of how these tools can be adapted for use in obstetric anesthesia, with evidence to support their implementation. This article thus aimed to summarize the many ways in which informatics can help clinicians to harness the power of data to improve quality and safety in obstetric anesthesia.

Opioid analgesic exposure during the first trimester of pregnancy and the risk of major congenital malformations in infants: a systematic review and meta-analysis†.

BACKGROUND: Prescribed opioid analgesics are frequently used to manage pain in pregnancy. However, the available literature regarding the teratogenic potential of opioid use during pregnancy has not been systematically summarised. This systematic review and meta-analysis aimed to assess the quality of the evidence on these potential risks and calculate a pooled estimate of risk for any opioid analgesic and individual opioids. METHODS: We searched PubMed, Embase and CINAHL for published studies assessing the risk of major congenital malformations in infants following first-trimester exposure to opioid analgesics compared with a reference group, excluding studies examining opioid agonist therapy or illicit opioid use. We assessed the risk of bias using the Risk of Bias in Non-Randomised Studies of Intervention tool. We pooled adjusted risk estimates from studies rated at serious risk of bias or better in a random-effects meta-analysis. RESULTS: Of 12 identified studies, 11 were at high risk of bias (eight serious; three critical). Relative to unexposed infants, those exposed to any opioid use during the first trimester of pregnancy were not at an increased risk of major congenital malformations overall (relative risk 1.04, 95%CI 0.98-1.11); cardiovascular malformations (relative risk 1.07, 95%CI 0.96-1.20); or central nervous system malformations (relative risk 1.06, 95%CI 0.92-1.21). Raised risk estimates were observed for gastrointestinal malformations (relative risk 1.40, 95%CI 0.38-5.16) and cleft palate (relative risk 1.57, 95%CI 0.48-5.13) following any opioid exposure and atrial septal defects (relative risk 1.20, 95%CI 1.05-1.36) following codeine exposure. CONCLUSIONS: Although the meta-analysis did not indicate substantial increased risk for most malformations examined, this risk remains uncertain due to the methodological limitations of the included studies. Healthcare professionals and pharmaceutical regulators should be aware of the issues related to the quality of research in this field.

Comparative Safety Analysis of Oral Antipsychotics for In-Hospital Adverse Clinical Events in Older Adults After Major Surgery : A Nationwide Cohort Study.

BACKGROUND: Antipsychotics are commonly used to manage postoperative delirium. Recent studies reported that haloperidol use has declined, and atypical antipsychotic use has increased over time. OBJECTIVE: To compare the risk for in-hospital adverse events associated with oral haloperidol, olanzapine, quetiapine, and risperidone in older patients after major surgery. DESIGN: Retrospective cohort study. SETTING: U.S. hospitals in the Premier Healthcare Database. PATIENTS: 17 115 patients aged 65 years and older without psychiatric disorders who were prescribed an oral antipsychotic drug after major surgery from 2009 to 2018. INTERVENTIONS: Haloperidol (≤4 mg on the day of initiation), olanzapine (≤10 mg), quetiapine (≤150 mg), and risperidone (≤4 mg). MEASUREMENTS: The risk ratios (RRs) for in-hospital death, cardiac arrhythmia events, pneumonia, and stroke or transient ischemic attack (TIA) were estimated after propensity score overlap weighting. RESULTS: The weighted population had a mean age of 79.6 years, was 60.5% female, and had in-hospital death of 3.1%. Among the 4 antipsychotics, quetiapine was the most prescribed (53.0% of total exposure). There was no statistically significant difference in the risk for in-hospital death among patients treated with haloperidol (3.7%, reference group), olanzapine (2.8%; RR, 0.74 [95% CI, 0.42 to 1.27]), quetiapine (2.6%; RR, 0.70 [CI, 0.47 to 1.04]), and risperidone (3.3%; RR, 0.90 [CI, 0.53 to 1.41]). The risk for nonfatal clinical events ranged from 2.0% to 2.6% for a cardiac arrhythmia event, 4.2% to 4.6% for pneumonia, and 0.6% to 1.2% for stroke or TIA, with no statistically significant differences by treatment group. LIMITATION: Residual confounding by delirium severity; lack of untreated group; restriction to oral low-to-moderate dose treatment. CONCLUSION: These results suggest that atypical antipsychotics and haloperidol have similar rates of in-hospital adverse clinical events in older patients with postoperative delirium who receive an oral low-to-moderate dose antipsychotic drug. PRIMARY FUNDING SOURCE: National Institute on Aging.

Comparative Safety of In Utero Exposure to Buprenorphine Combined With Naloxone vs Buprenorphine Alone.

Importance: Buprenorphine combined with naloxone is commonly used to treat opioid use disorders outside of pregnancy. In pregnancy, buprenorphine alone is generally recommended because of limited perinatal safety data on the combination product. Objective: To compare perinatal outcomes following prenatal exposure to buprenorphine with naloxone vs buprenorphine alone. Design, Settings, and Participants: Population-based cohort study using health care utilization data from Medicaid-insured beneficiaries in the US from 2000 to 2018. The cohort was restricted to pregnant individuals linked to their liveborn infants, with maternal Medicaid enrollment from 3 months before pregnancy to 1 month after delivery and infant enrollment for the first 3 months after birth, unless they died sooner. Exposure: Use of buprenorphine with naloxone vs buprenorphine alone during the first trimester based on outpatient dispensings. Main Outcomes and Measures: Outcomes included major congenital malformations, low birth weight, neonatal abstinence syndrome, neonatal intensive care unit admission, preterm birth, respiratory symptoms, small for gestational age, cesarean delivery, and maternal morbidity. Confounder-adjusted risk ratios were calculated using propensity score overlap weights. Results: This study identified 3369 pregnant individuals exposed to buprenorphine with naloxone during the first trimester (mean [SD] age, 28.8 [4.6] years) and 5326 exposed to buprenorphine alone or who switched from the combination to buprenorphine alone by the end of the first trimester (mean [SD] age, 28.3 [4.5] years). When comparing buprenorphine combined with naloxone with buprenorphine alone, a lower risk for neonatal abstinence syndrome (absolute risk, 37.4% vs 55.8%; weighted relative risk, 0.77 [95% CI, 0.70-0.84]) and a modestly lower risk for neonatal intensive care unit admission (absolute risk, 30.6% vs 34.9%; weighted relative risk, 0.91 [95% CI, 0.85-0.98]) and small for gestational age (absolute risk, 10.0% vs 12.4%; weighted relative risk, 0.86 [95% CI, 0.75-0.98]) was observed. For maternal morbidity, the comparative rates were 2.6% vs 2.9%, respectively, and the weighted relative risk was 0.90 (95% CI, 0.68-1.19). No differences were observed with respect to major congenital malformations overall, low birth weight, preterm birth, respiratory symptoms, or cesarean delivery. Results were consistent across sensitivity analyses. Conclusions and Relevance: There were similar and, in some instances, more favorable neonatal and maternal outcomes for pregnancies exposed to buprenorphine combined with naloxone compared with buprenorphine alone. For the outcomes assessed, compared with buprenorphine alone, buprenorphine with naloxone during pregnancy appears to be a safe treatment option. This supports the view that both formulations are reasonable options for the treatment of opioid use disorder in pregnancy, affirming flexibility in collaborative treatment decision-making.

Surgeon Postoperative Opioid Prescribing Intensity and Risk of Persistent Opioid Use Among Opioid-naive Adult Patients: A Population-based Cohort Study.

OBJECTIVE: The aim of this study was to determine the relationship between surgeon opioid prescribing intensity and subsequent persistent opioid use among patients undergoing surgery. SUMMARY BACKGROUND DATA: The extent to which different postoperative prescribing practices lead to persistent opioid use among surgical patients is poorly understood. METHODS: Retrospective population-based cohort study assessing opioid-naive adults who underwent 1 of 4 common surgeries. For each surgical procedure, the surgeons' opioid prescribing intensity was categorized into quartiles based on the median daily dose of morphine equivalents of opioids dispensed within 7 days of the surgical visit for all the surgeons' patients. The primary outcome was persistent opioid use in the year after surgery, defined as 180 days or more of opioids supplied within the year after the index date excluding prescriptions filled within 30 days of the index date. Secondary outcomes included a refill for an opioid within 30 days and emergency department visits and hospitalizations within 1 year. RESULTS: Among 112,744 surgical patients, patients with surgeons in the highest intensity quartile (Q4) were more likely to fill an opioid prescription within 7 days after surgery compared with those in the lowest quartile (Q1) (83.3% Q4 vs 65.4% Q1). In the primary analysis, the incidence of persistent opioid use in the year after surgery was rare in both highest and lowest quartiles (0.3% Q4 vs 0.3% Q1), adjusted odds ratio (AOR) of 1.18, 95% CI 0.83-1.66). However, multiple analyses using stricter definitions of persistent use that included the requirement of a prescription filled within 7 days of discharge after surgery showed a significant association with surgeon quartile (up to an AOR 1.36, 95% CI 1.25, 1.47). Patients in Q4 were more likely to refill a prescription within 30 days (4.8% Q4 vs 4.0% Q1, AOR 1.14, 95% CI 1.04-1.24). CONCLUSIONS: Surgeons' overall prescribing practices may contribute to persistent opioid use and represent a target for quality improvement. However, the association was highly sensitive to the definition of persistent use used.

Emulating a Target Trial of Interventions Initiated During Pregnancy with Healthcare Databases: The Example of COVID-19 Vaccination.

BACKGROUND: Observational studies are often the only option to estimate effects of interventions during pregnancy. Causal inference from observational data can be conceptualized as an attempt to emulate a hypothetical pragmatic randomized trial: the target trial. OBJECTIVE: To provide a step-by-step description of how to use healthcare databases to estimate the effects of interventions initiated during pregnancy. As an example, we describe how to specify and emulate a target trial of COVID-19 vaccination during pregnancy, but the framework can be generally applied to point and sustained strategies involving both pharmacologic and non-pharmacologic interventions. METHODS: First, we specify the protocol of a target trial to evaluate the safety and effectiveness of vaccination during pregnancy. Second, we describe how to use observational data to emulate each component of the protocol of the target trial. We propose different target trials for different gestational periods because the outcomes of interest vary by gestational age at exposure. We identify challenges that affect (i) the target trial and thus its observational emulation (censoring and competing events), and (ii) mostly the observational emulation (confounding, immortal time, and measurement biases). CONCLUSION: Some biases may be unavoidable in observational emulations, but others are avoidable. For instance, immortal time bias can be avoided by aligning the start of follow-up with the gestational age at the time of the intervention, as we would do in the target trial. Explicitly emulating target trials at different gestational ages can help reduce bias and improve the interpretability of effect estimates for interventions during pregnancy.

Development and Validation of Algorithms to Estimate Live Birth Gestational Age in Medicaid Analytic eXtract Data.

BACKGROUND: While healthcare utilization data are useful for postmarketing surveillance of drug safety in pregnancy, the start of pregnancy and gestational age at birth are often incompletely recorded or missing. Our objective was to develop and validate a claims-based live birth gestational age algorithm. METHODS: Using the Medicaid Analytic eXtract (MAX) linked to birth certificates in three states, we developed four candidate algorithms based on: preterm codes; preterm or postterm codes; timing of prenatal care; and prediction models - using conventional regression and machine-learning approaches with a broad range of prespecified and empirically selected predictors. We assessed algorithm performance based on mean squared error (MSE) and proportion of pregnancies with estimated gestational age within 1 and 2 weeks of the gold standard, defined as the clinical or obstetric estimate of gestation on the birth certificate. We validated the best-performing algorithms against medical records in a nationwide sample. We quantified misclassification of select drug exposure scenarios due to estimated gestational age as positive predictive value (PPV), sensitivity, and specificity. RESULTS: Among 114,117 eligible pregnancies, the random forest model with all predictors emerged as the best performing algorithm: MSE 1.5; 84.8% within 1 week and 96.3% within 2 weeks, with similar performance in the nationwide validation cohort. For all exposure scenarios, PPVs were >93.8%, sensitivities >94.3%, and specificities >99.4%. CONCLUSIONS: We developed a highly accurate algorithm for estimating gestational age among live births in the nationwide MAX data, further supporting the value of these data for drug safety surveillance in pregnancy. See video abstract at, http://links.lww.com/EDE/B989 .

Nonopioid Analgesic Prescriptions Filled after Surgery among Older Adults in Ontario, Canada: A Population-based Cohort Study.

BACKGROUND: The objective was to assess changes over time in prescriptions filled for nonopioid analgesics for older postoperative patients in the immediate postdischarge period. The authors hypothesized that the number of patients who filled a nonopioid analgesic prescription increased during the study period. METHODS: The authors performed a population-based cohort study using linked health administrative data of 278,366 admissions aged 66 yr or older undergoing surgery between fiscal year 2013 and 2019 in Ontario, Canada. The primary outcome was the percentage of patients with new filled prescriptions for nonopioid analgesics within 7 days of discharge, and the secondary outcome was the analgesic class. The authors assessed whether patients filled prescriptions for a nonopioid only, an opioid only, both opioid and nonopioid prescriptions, or a combination opioid/nonopioid. RESULTS: Overall, 22% (n = 60,181) of patients filled no opioid prescription, 2% (n = 5,534) filled a nonopioid only, 21% (n = 59,608) filled an opioid only, and 55% (n = 153,043) filled some combination of opioid and nonopioid. The percentage of patients who filled a nonopioid prescription within 7 days postoperatively increased from 9% (n = 2,119) in 2013 to 28% (n = 13,090) in 2019, with the greatest increase for acetaminophen: 3% (n = 701) to 20% (n = 9,559). The percentage of patients who filled a combination analgesic prescription decreased from 53% (n = 12,939) in 2013 to 28% (n = 13,453) in 2019. However, the percentage who filled both an opioid and nonopioid prescription increased: 4% (n = 938) to 21% (n = 9,880) so that the overall percentage of patients who received both an opioid and a nonopioid remained constant over time 76% (n = 18,642) in 2013 to 75% (n = 35,391) in 2019. CONCLUSIONS: The proportion of postoperative patients who fill prescriptions for nonopioid analgesics has increased. However, rather than a move to use of nonopioids alone for analgesia, this represents a shift away from combination medications toward separate prescriptions for opioids and nonopioids.

Achieving comparability in glycemic control between antidiabetic treatment strategies in pregnancy when using real world data.

PURPOSE: Healthcare utilization databases often lack information on glycemic control, a key confounder when studying the safety of antidiabetic treatments, since patients with worse control are channeled to second-line agents, in particular insulin, versus first-line agents such as metformin. We evaluated whether adjustment for measured characteristics attains balance in glycemic control when comparing antidiabetic treatment strategies in pregnant women with pregestational type 2 diabetes (T2DM). METHODS: In a US insurance claims database, we identified 3360 women with T2DM pregnant between 2004 and 2015, of whom a subset of 996 had data on hemoglobin A1c (HbA1c ) levels. We selected insulin only as the comparator group and used propensity score (PS)-matching on comorbidities and proxies of diabetes severity, but not on HbA1c , to adjust for confounding. We used standardized differences (st.diff) to assess balance in claims-based covariates and mean HbA1c (% ± SD) in the subset. RESULTS: There were imbalances in claims-based covariates before PS-matching, with smaller differences when both treatment strategies included insulin. After PS-matching, balance was achieved in most claims-based covariates (st.diff <0.1). Mean HbA1c was similar before and after PS-matching when both treatments included insulin (e.g., 7.1 ± 1.5 vs. 7.7 ± 1.8 and 7.1 ± 1.5 vs. 7.5 ± 1.7, respectively, for metformin + insulin vs. insulin only). Differences in mean HbA1c remained after PS-matching when non-insulin treatments were compared to treatments including insulin (e.g., 6.3 ± 1.1 vs. 7.6 ± 1.7 for metformin only vs. insulin only). CONCLUSIONS: Balance in both claims-based characteristics and glycemic control was attained after restricting the population to women with T2DM and comparing treatment strategies indicated for patients with similar diabetes severity. When comparing treatment strategies with versus without insulin, differences in glycemic control persisted after PS-matching even when balance was attained for other measured characteristics.