Birth and Neonatal Outcomes following Opioid Use in Pregnancy: A Danish Population-Based Study

Background Few population-based data exist on birth outcomes in women who received opioid maintenance treatment during pregnancy. We therefore examined adverse birth outcomes in women exposed to methadone or buprenorphine during pregnancy and the risk of neonatal abstinence syndrome (NAS) among neonates exposed to buprenorphine, methadone, and/or heroin in utero. Patients and Methods This study included all female Danish residents with a live birth or a stillbirth from 1997 to 211. We identified the study population, use of opioids and opioid substitution treatment, birth outcomes, and NAS through medical registers. Birth outcomes included preterm birth (born before 38th gestational week), low-birth weight (LBW) (<2,500 g, restricted to term births), small for gestational age (SGA) (weight <2 standard deviations from the sex- and gestational-week-specific mean), congenital malformations, and stillbirths. We used log-binomial regression to estimate the prevalence ratio (PR) for birth outcomes. Results Among 95,172 pregnancies in a total of 571,823 women, we identified 557 pregnancies exposed to buprenorphine, methadone, and/or heroin (167 to buprenorphine, 197 to methadone, 28 to self-reported heroin, and 165 to combinations). Compared with nonexposed pregnancies, prenatal opioid use was associated with greater prevalence of preterm birth (PR of 2.8 (95% confidence interval (CI), 2.3–3.4)), LBW among infants born at term (PR of 4.3 (95% CI, 3.0–6.1)), and being SGA (PR of 2.7 (95% CI, 1.9–4.3)). Restricting the analyses to women who smoked slightly lowered these estimates. The prevalence of congenital malformations was 8.3% in opioid-exposed women compared with 4.2% in nonexposed women (PR of 2.0 (95% CI, 1.5–2.6)). The risk of NAS ranged from 7% in neonates exposed to buprenorphine only to 55% in neonates exposed to methadone only or to opioid combinations. Conclusion The maternal use of buprenorphine and methadone during pregnancy was associated with increased prevalence of adverse birth outcomes, and this increase could only be explained to a smaller extent by increased prevalence of smoking. The risk of NAS was eight-fold higher in methadone-exposed neonates than that in buprenorphine-exposed neonates, but this difference may at least partly be explained by differences in underlying indications (analgesic versus opioid maintenance treatment) between the two groups

Validation study in four health-care databases: Upper gastrointestinal bleeding misclassification affects precision but not magnitude of drug-related upper gastrointestinal bleeding risk

Objective: To evaluate the accuracy of disease codes and free text in identifying upper gastrointestinal bleeding (UGIB) from electronic health-care records (EHRs). Study design and setting: We conducted a validation study in four European electronic health-care record (EHR) databases such as Integrated Primary Care Information (IPCI), Health Search/CSD Patient Database (HSD), ARS, and Aarhus, in which we identified UGIB cases using free text or disease codes: (1) International Classification of Disease (ICD)-9 (HSD, ARS); (2) ICD-10 (Aarhus); and (3) International Classification of Primary Care (ICPC) (IPCI). From each database, we randomly selected and manually reviewed 200 cases to calculate positive predictive values (PPVs). We employed different case definitions to assess the effect of outcome misclassification on estimation of risk of drug-related UGIB. Results: PPV was 22% [95% confidence interval (CI): 16, 28] and 21% (95% CI: 16, 28) in IPCI for free text and ICPC codes, respectively. PPV was 91% (95% CI: 86, 95) for ICD-9 codes and 47% (95% CI: 35, 59) for free text in HSD. PPV for ICD-9 codes in ARS was 72% (95% CI: 65, 78) and 77% (95% CI: 69, 83) for ICD-10 codes (Aarhus). More specific definitions did not have significant impact on risk estimation of drug-related UGIB, except for wider CIs. Conclusions: ICD-9-CM and ICD-10 disease codes have good PPV in identifying UGIB from EHR; less granular terminology (ICPC) may require additional strategies. Use of more specific UGIB definitions affects precision, but not magnitude, of risk estimates