Person:

Toh, Sengwee Darren

Objective: To investigate whether pre-eclampsia is more common in first pregnancies solely because fewer affected women, who presumably have a higher risk of recurrence, go on to have subsequent pregnancies. Design: Prospective cohort study. Setting: Swedish Medical Birth Register. Participants: 763 795 primiparous mothers who had their first births in Sweden, 1987-2004. Main outcome measures: Pre-eclampsia. Results: The risk of pre-eclampsia was 4.1% in the first pregnancy and 1.7% in later pregnancies overall. However, the risk was 14.7% in the second pregnancy for women who had had pre-eclampsia in their first pregnancy and 31.9% for women who had had pre-eclampsia in the previous two pregnancies. The risk for multiparous women without a history of pre-eclampsia was around 1%. The incidence of pre-eclampsia associated with delivery before 34 weeks’ gestation was 0.42% in primiparous women, 0.11% in multiparous women without a history of pre-eclampsia, and 6.8% and 12.5% in women who had had one or two previous pregnancies affected, respectively. The proportion of women who went on to have a further pregnancy was 4-5% lower after having a pregnancy with any pre-eclampsia but over 10% lower if pre-eclampsia was associated with very preterm delivery. The estimated risk of pre-eclampsia in parous women did not change with standardisation for pregnancy rates. Conclusions: Having pre-eclampsia in one pregnancy is a poor predictor of subsequent pregnancy but a strong predictor for recurrence of pre-eclampsia in future gestations. The lower overall risk of pre-eclampsia among parous women was not explained by fewer conceptions among women who had had pre-eclampsia in a previous gestation. Early onset pre-eclampsia might be associated with a reduced likelihood of a future pregnancy and with more recurrences than late onset pre-eclampsia when there are further pregnancies. Findings are consistent with the existence of two distinct conditions: a severe recurrent early onset type affected by chronic factors, genetic or environmental, and a milder sporadic form affected by transient factors.

Network meta-analysis is increasingly used to allow comparison of multiple treatment alternatives simultaneously, some of which may not have been compared directly in primary research studies. The majority of network meta-analyses published to date have incorporated data from randomized controlled trials (RCTs) only; however, inclusion of non-randomized studies may sometimes be considered. Non-randomized studies can complement RCTs or address some of their limitations, such as short follow-up time, small sample size, highly selected population, high cost, and ethical restrictions. In this paper, we discuss the challenges and opportunities of incorporating both RCTs and non-randomized comparative cohort studies into network meta-analysis for assessing the safety and effectiveness of medical treatments. Non-randomized studies with inadequate control of biases such as confounding may threaten the validity of the entire network meta-analysis. Therefore, identification and inclusion of non-randomized studies must balance their strengths with their limitations. Inclusion of both RCTs and non-randomized studies in network meta-analysis will likely increase in the future due to the growing need to assess multiple treatments simultaneously, the availability of higher quality non-randomized data and more valid methods, and the increased use of progressive licensing and product listing agreements requiring collection of data over the life cycle of medical products. Inappropriate inclusion of non-randomized studies could perpetuate the biases that are unknown, unmeasured, or uncontrolled. However, thoughtful integration of randomized and non-randomized studies may offer opportunities to provide more timely, comprehensive, and generalizable evidence about the comparative safety and effectiveness of medical treatments.

Purpose of Review Administrative claims databases, which collect reimbursement-related information generated from healthcare encounters, are increasingly used to evaluate medication safety in pregnancy. We reviewed the strengths and limitations of claims-only databases and how other data sources may be used to improve the accuracy and completeness of information critical for studying medication safety in pregnancy. Recent Findings Research on medication safety in pregnancy requires information on pregnancy episodes, mother-infant linkage, medication exposure, gestational age, maternal and birth outcomes, confounding factors, and (in some studies) long-term follow-up data. Claims data reliably identifies live births and possibly other pregnancies. It allows mother-infant linkage and has prospectively collected prescription medication information. Its diagnosis and procedure information allows estimation of gestational age. It captures maternal medical conditions but generally has incomplete data on reproductive and lifestyle factors. It has information on certain, typically short-term maternal and infant outcomes that may require chart review confirmation. Other data sources including electronic health records and birth registries can augment claims data or be analyzed alone. Interviews, surveys, or biological samples provide additional information. Nationwide and regional birth and pregnancy registries, such as those in several European and North American countries, generally contain more complete information essential for pregnancy research compared to claims-only databases. Summary Claims data offers several advantages in medication safety in pregnancy research. Its limitations can be partially addressed by linking it with other data sources or supplementing with primary data collection. Rigorous assessment of data quality and completeness is recommended regardless of data sources.

Background High-grade cervical dysplasia or cervical intraepithelial neoplasia (CIN) grade 2 or worse has been widely used as a surrogate endpoint in cervical cancer screening or prevention trials.

Methods To identify high-grade cervical dysplasia and cervical cancer, we developed claims-based algorithms that incorporated a combination of diagnosis and procedure codes using the billing data in an electronic medical records (EMR) database and assessed the validity of the algorithms in an independent administrative claims database. We calculated the positive predictive value (PPV) with the 95% confidence interval (CI) of each algorithm, using new cytologic or pathologic diagnosis of CIN 2 or 3, carcinoma in situ, or cervical cancer as the gold standard.

Results Having ≥1 diagnosis code for high-grade cervical dysplasia or cervical cancer had a PPV of 57.1% (95%CI 54.7–59.5%). By requiring ≥2 diagnoses for high-grade cervical dysplasia or cervical cancer, separated by 7 to 30 days, the PPV increased to 60.2% (95%CI 53.9–66.1%). At least 2 diagnoses and a procedure code within a month from the first diagnosis date yielded a PPV of 80.7% (95%CI 73.6–86.2%). The algorithms had greater PPVs in identifying prevalent high-grade cervical dysplasia or cervical cancer. Overall, the PPVs of these algorithms were similar or slightly lower in the external claims data than in the sample used to derive the algorithms.

Conclusions Use of ≥ 2 diagnosis codes in combination with a procedure code appears to be a valid tool for studying high-grade cervical dysplasia and cervical cancer in both EMR and administrative claims databases.

Objective: To evaluate stroke risk among users of typical antipsychotics compared to users of atypical antipsychotics in a non-elderly and non-demented US population. Methods: New users of antipsychotics aged 18–64 years without dementia were identified via electronic health care data from 13 health plans participating in the Sentinel System from January 2001 to September 2015. The risk of hospitalized stroke events, identified via ICD-9-CM diagnostic criteria, was compared between typical and atypical antipsychotic users using 1:1 matching on propensity score. Adjusted hazard ratios (HRs) and 95% CIs during the entire follow-up period and during 1- to 15-day and 16- to 90-day risk windows were estimated. The risk associated with haloperidol use was estimated separately. Results: A total of 45,495 typical antipsychotic users were matched 1:1 to atypical antipsychotic users. While unmatched HRs suggest an increased stroke risk among typical antipsychotic users compared to atypical antipsychotic users, no increased risk was observed after matching during the entire follow-up period (HR = 0.87; 95% CI, 0.54–1.41), the 1- to 15-day risk window (HR = 1.16; 95% CI, 0.41–3.32), or the 16- to 90-day risk window (HR = 0.52; 95% CI, 0.20–1.36). The adjusted HR for haloperidol was 1.31 (95% CI, 0.54–3.21). Conclusion: These findings were not suggestive of an increased stroke risk in typical antipsychotic users compared to atypical antipsychotic users in a non-elderly and non-demented population.