Use of existing electronic health care databases to evaluate medication safety in pregnancy: Triptan exposure in pregnancy as a case study

Characterizing Fit-for-Purpose Real-World Data: An Assessment of a Mother-Infant Linkage in the Japan Medical Data Center Claims Database

Purpose

Observational postapproval safety studies are needed to inform medication safety during pregnancy. Real-world databases can be valuable for supporting such research, but fitness for regulatory purpose must first be vetted. Here, we demonstrate a fit-for-purpose assessment of the Japan Medical Data Center (JMDC) claims database for pregnancy safety regulatory decision-making.

Patients and Methods

The Duke-Margolis framework considers a database's fitness for regulatory purpose based on relevancy (capacity to answer the research question based on variable availability and a sufficiently sized, representative population) and quality (ability to validly answer the research question based on data completeness and accuracy). To assess these considerations, we examined descriptive characteristics of infants and pregnancies among females ages 12-55 years in the JMDC between January 2005 and March 2022.

Results

For relevancy, we determined that critical data fields (maternal medications, infant major congenital malformations, covariates) are available. Family identification codes permitted linkage of 385,295 total mother-infant pairs, 57% of which were continuously enrolled during pregnancy. The prevalence of specific congenital malformation subcategories and maternal medical conditions were representative of the general population, but preterm births were below expectations (3.6% versus 5.6%) in this population. For quality, our methods are expected to accurately identify the complete set of mothers and infants with a shared health insurance plan. However, validity of gestational age information was limited given the high proportion (60%) of missing live birth delivery codes coupled with suppression of infant birth dates and inaccessibility of disease codes with gestational week information.

Conclusion

The JMDC may be well suited for descriptive studies of pregnant people in Japan (eg, comorbidities, medication usage). More work is needed to identify a method to assign pregnancy onset and delivery dates so that in utero medication exposure windows can be defined more precisely as needed for many regulatory postapproval pregnancy safety studies.

Association Between Periviable Delivery and New Onset of or Exacerbation of Existing Mental Health Disorders

OBJECTIVE

To evaluate whether there is an association between periviable delivery and new onset of or exacerbation of existing mental health disorders within 12 months postpartum.

METHODS

We conducted a retrospective cohort study of individuals with liveborn singleton neonates delivered at 22 or more weeks of gestation from 2008 to 2017 in the MarketScan Commercial Research Database. The exposure was periviable delivery , defined as delivery from 22 0/7 through 25 6/7 weeks of gestation. The primary outcome was a mental health morbidity composite of one or more of the following: emergency department encounter associated with depression, anxiety, psychosis, posttraumatic stress disorder, adjustment disorder, self-harm, or suicide; new psychotropic medication prescription; new behavioral therapy visit; and inpatient psychiatry admission in the 12 months postdelivery. Secondary outcomes included components of the primary composite. Those with and without periviable delivery were compared using multivariable logistic regression adjusted for clinically relevant covariates, with results reported as adjusted incident rate ratios (aIRRs). Effect modification by history of mental health diagnoses was assessed. Incidence of the primary outcome by 90-day intervals postdelivery was assessed.

RESULTS

Of 2,300,244 included deliveries, 16,275 (0.7%) were periviable. Individuals with periviable delivery were more likely to have a chronic health condition, to have undergone cesarean delivery, and to have experienced severe maternal morbidity. Periviable delivery was associated with a modestly increased risk of the primary composite outcome, occurring in 13.8% of individuals with periviable delivery and 11.0% of individuals without periviable delivery (aIRR 1.18, 95% CI 1.12-1.24). The highest-risk period for the composite primary outcome was the first 90 days in those with periviable delivery compared with those without periviable delivery (51.6% vs 42.4%; incident rate ratio 1.56, 95% CI 1.47-1.66).

CONCLUSION

Periviable delivery was associated with a modestly increased risk of mental health morbidity in the 12 months postpartum.

Pharmacovigilance: reporting requirements throughout a product's lifecycle

Comprehensive methods for evaluating safety are needed to objectively assess the full risk profile of a medication. The confidence of the prescribing provider in the safety and effectiveness of pharmaceuticals is extremely important. Pharmacovigilance is a key component of drug safety regulatory processes and is paramount for ensuring the safety profile of medications used to treat patients. All participants in the healthcare system, including healthcare providers and consumers, should understand and meaningfully engage in the pharmacovigilance process; healthcare providers should integrate pharmacovigilance into everyday practice, inviting feedback from patients. This narrative review aims to give an overview of the main topics underlying pharmacovigilance and drug safety in pharmaceutical research phase after the authorization of a drug in the United States. The US Food and Drug Administration guidance and post-approval regulatory actions are considered from an industry perspective.

Plain language summary

Regulatory processes that ensure the safety of drugs is monitored Government agencies regulate the safe use of medicinal products. By determining and enforcing pharmacovigilance, the monitoring of drugs for potential risks, they safeguard the welfare of consumers of medicines. Comprehensive, documented methods for evaluating the safety of a drug during its development and its subsequent use allow identification of any risks associated with the drug's use throughout its lifetime. The comprehensive identification of safety issues associated with a drug is improved when all parties involved in the development and use of drugs participate in the pharmacovigilance process. For example, clinicians should regularly ask their patients if they are experiencing any issues with their treatment, and patients should be encouraged to report problems they encounter with a particular medication to their healthcare provider. This narrative review provides an overview of the main topics underlying pharmacovigilance and drug safety after approval of a drug in the United States. Guidelines and actions from the US Food and Drug Administration are considered from an industry perspective.

Development and evaluation of MADDIE: Method to Acquire Delivery Date Information from Electronic health records

OBJECTIVE

To develop an algorithm that infers patient delivery dates (PDDs) and delivery-specific details from Electronic Health Records (EHRs) with high accuracy; enabling pregnancy-level outcome studies in women's health.

MATERIALS AND METHODS

We obtained EHR data from 1,060,100 female patients treated at Penn Medicine hospitals or outpatient clinics between 2010-2017. We developed an algorithm called MADDIE: Method to Acquire Delivery Date Information from Electronic Health Records that infers a PDD for distinct deliveries based on EHR encounter dates assigned a delivery code, the frequency of code usage, and the time differential between code assignments. We validated MADDIE's PDDs against a birth log independently maintained by the Department of Obstetrics and Gynecology.

RESULTS

MADDIE identified 50,560 patients having 63,334 distinct deliveries. MADDIE was 98.6 % accurate (F₁-score 92.1 %) when compared to the birth log. The PDD was on average 0.68 days earlier than the true delivery date for patients with only one delivery (± 1.43 days) and 0.52 days earlier for patients with more than one delivery episode (± 1.11 days).

DISCUSSION

MADDIE is the first algorithm to successfully infer PDD information using only structured delivery codes and identify multiple deliveries per patient. MADDIE is also the first to validate the accuracy of the PDD using an external gold standard of known delivery dates as opposed to manual chart review of a sample.

CONCLUSION

MADDIE augments the EHR with delivery-specific details extracted with high accuracy and relies only on structured EHR elements while harnessing temporal information and the frequency of code usage to identify accurate PDDs.

Machine learning on drug-specific data to predict small molecule teratogenicity

Pregnant women are an especially vulnerable population, given the sensitivity of a developing fetus to chemical exposures. However, prescribing behavior for the gravid patient is guided on limited human data and conflicting cases of adverse outcomes due to the exclusion of pregnant populations from randomized, controlled trials. These factors increase risk for adverse drug outcomes and reduce quality of care for pregnant populations. Herein, we propose the application of artificial intelligence to systematically predict the teratogenicity of a prescriptible small molecule from information inherent to the drug. Using unsupervised and supervised machine learning, our model probes all small molecules with known structure and teratogenicity data published in research-amenable formats to identify patterns among structural, meta-structural, and in vitro bioactivity data for each drug and its teratogenicity score. With this workflow, we discovered three chemical functionalities that predispose a drug towards increased teratogenicity and two moieties with potentially protective effects. Our models predict three clinically-relevant classes of teratogenicity with AUC = 0.8 and nearly double the predictive accuracy of a blind control for the same task, suggesting successful modeling. We also present extensive barriers to translational research that restrict data-driven studies in pregnancy and therapeutically "orphan" pregnant populations. Collectively, this work represents a first-in-kind platform for the application of computing to study and predict teratogenicity.

Use of existing electronic health care databases to evaluate medication safety in pregnancy: Triptan exposure in pregnancy as a case study

Purpose

The recent expansion of electronic health and medical record systems may present an opportunity to generate robust post‐approval safety data and obviate the limitations of prospective pregnancy exposure registries. We examined and compared, over the same time frame, the outcomes of triptan exposure in pregnancy using (1) a retrospective claims database and (2) a previously completed pregnancy registry.

Methods

Using the Marketscan database, the risk of major birth defects was ascertained in live‐born infants whose birth mothers were exposed to sumatriptan, naratriptan, or sumatriptan/naproxen during pregnancy. The frequencies of outcomes observed were compared with the findings of the 16‐year sumatriptan, naratripan, and sumatriptan/naproxen prospective pregnancy registry.

Results

About 5120 pregnancies were identified in the retrospective claims cohort in contrast to 617 included in the prospective registry during the same time frame. The proportion of major birth defects among first‐semester sumatriptan exposures was 4.0%, which is exactly the same as the proportion of major birth defects reported for first‐semester sumatriptan exposures in the registry. There were very few non‐livebirth outcomes in both the claims analyses and registry.

Conclusions

These results confirm broad agreement between the database analysis and the registry regarding the safety of triptans during pregnancy. Of note, the number of triptan‐exposed pregnancies identified in this large US database was about 7‐fold that included in the prospective registry over the same time frame. The findings of this study support an approach of using existing health care database (s) in the post‐approval assessment of medication exposure in pregnancy.