Pregnancy

This presentation provides an overview of TreeScan signal identification methods and discusses how such methods can be used for surveillance of potential adverse infant events following maternal medication exposure during pregnancy. Methods and results from a simulation analysis to assess the performance of TreeScan under known conditions are reviewed, as well as a case study which aimed to demonstrate the use of TreeScan in real-world data, in a cohort of pregnant women linked to their live-born infants.

It was presented at the Canadian Network for Observational Drug Effect Studies (CNODES) Virtual Seminar, held on March 9, 2022.

In this report we describe exposure to multiple sclerosis (MS) drugs before, during and after pregnancies resulting in a live-born delivery among women in the Sentinel Distributed Database (SDD). We also describe MS drug use among matched women during episodes without evidence of a live-birth delivery.

The study period includes data from January 1, 2001 to December 31, 2020. We distributed this request to six Sentinel Data Partners on June 25, 2021.

This is a joint agency project involving the Food and Drug Administration (FDA) and National Institutes of Health/ National Library of Medicine (NIH/NLM) that addresses the Patient-Centered Outcomes Research (PCOR) priority to expand data capacity or data infrastructure for conducting research that informs decisions about the effectiveness of health interventions used in the Medicaid and Children's Health Insurance Programs.

This project aims to develop and publish open source code to format T-MSIS Research Identifiable File (RIFs) to the Sentinel CDM in tandem with NLM’s formatting of the T-MSIS RIFs to the OMOP CDM. Data quality will be characterized using a harmonized Data Quality Assessment Framework for electronic healthcare data that was developed in a previously funded OS-PCORTF project. To illustrate the benefits of CDM transformation, open source tools will be used to conduct several studies of public health importance, such as studies of maternal mortality and adherence to clinical guidelines for perinatal testing. Additionally, the project will assess the feasibility of using Fast Healthcare Interoperability Resources (FHIR) APIs (Application Programming Interfaces) to link Electronic Health Record (EHR) data to T-MSIS claims data. Lastly, the project will ensure stakeholder engagement and sustainability via a Technical Expert Panel (TEP) to provide guidance on the data quality metrics, the PCOR demonstration projects, and development of a series of training materials. The goal of the training materials is to ensure findings are optimally disseminated to the Medicaid research community to lead to greater utilization of the CDM translation code and use of T-MSIS for patient-centered outcomes research in Sentinel and OMOP ecosystems.

This project covers several major tasks:

Task 1: Develop open source code to format the T-MSIS data into the Sentinel and Observational Medical Outcomes Partnership (OMOP) Common Data Models (CDMs)

Task 2: Leverage the ASPE funded data quality metrics model to characterize each CDM-formatted version

Task 3: Develop open-source code to create a mother-infant linkage in the Sentinel CDM to support several Sentinel analyses on maternal health

Task 4: Conduct one or more demonstration studies assessing the harms and benefits of an aspect of maternal health using the transformed T-MSIS dataset and Sentinel analytic tools. The FDA-led workgroup will include participation from CDC/NCHHSTP, CDC/NCBDDD, NIH/NICHD and HRSA

Task 5: Evaluate the feasibility of implementing FHIR APIs to link T-MSIS data with EHR data

Task 6: Engage stakeholders and create sustainability by:

• Establishing a TEP with relevant expertise in the Medicaid program, T-MSIS data, or patient-centered outcomes research to provide non-binding guidance from an end-user’s perspective on the data quality metrics selected in Task 2,  potential PCOR demonstration projects using the new T-MSIS dataset in Task 4, and the structure of the training materials
• Developing a webinar series to train Medicaid researchers on the new data transformation tools and disseminate major project findings

This study aims to describe the feasibility and generalizability of a mobile app for capturing data on health conditions, medication use, and other relevant exposures during pregnancy and linking those data to the patient’s medical record research database.

The following presentation was presented at the 37th International Conference on Pharmacoepidemiology and Therapeutic Risk Management in 2021.

The goals of this methods project are to evaluate the performance of TreeScan to assess maternal and infant outcomes, test signal identification methods in a pregnancy setting, and evaluate methods performance using older drugs with relatively well-characterized safety profiles.

A Propensity Score-Based TreeScan approach will be the primary analysis for this project. There will be two analyses: 1) empirical (real) data set and a 2) simulated data set with investigator-inserted risks. Two protocols will be written for Aims 2 and 3, respectively, and posted for public comment.

Aim 1:

• a) Develop a mother-infant linkage programming module, to be executed in Merative™ MarketScan® Research Databases, that produces an SCDM-compliant mother-infant linkage table using a published method.
• b) Develop a general propensity score for pregnant women.
• c) Develop tree of infant outcomes in ICD-10-CM and develop tree of maternal outcomes in ICD-10-CM.  

Aim 2:

• a) Assess the performance of TreeScan to detect infant outcomes using empirical data with the following outcomes: congenital malformations as the primary objective and a selection of additional outcomes including small for gestation age, low birth weight, and increased rate of premature birth as the secondary objectives.
• b) Perform a simulation study with investigator-injected risks to develop data on the power to detect risk under ideal circumstances, using empirical data to develop background rates. 

Aim 3:

• a) Assess the performance of TreeScan to detect maternal outcomes using empirical data among pregnant women with live birth outcomes.
• b) Perform a simulation study with investigator-injected risks to develop data on the power to detect risk under ideal circumstances, using empirical data to develop background rates.

This infant outcomes protocol was posted for public comment from June 19, 2020 through July 6, 2020. The public comment period is now closed. A revised version was approved for implementation by FDA on August 17, 2020. A second revised version was approved for implementation by FDA on February 9, 2021. A log of changes is included in the revised protocol (v3).

This maternal outcomes protocol was posted for public comment from June 29, 2021 through July 13, 2021. The public comment period is now closed. A revised version was approved for implementation by FDA on August 17, 2022. A log of changes is included in the revised protocol (v2).

This request examines pregnancy among women with heart failure in the Sentinel Distributed Database (SDD). This analysis includes two reports:

• Report 1: This report examines counts of live birth or stillbirth delivery among women with heart failure. We distributed this request to six Data Partners on October 7, 2020.
• Report 2: This report examines the clinical characteristics in three cohorts including pregnant women with and without evidence of heart failure (HF) and non-pregnant women with HF, characterizes the use of oral HF-related therapies before, during, and after pregnancy in these cohorts, and examines maternal and fetal outcomes among pregnant women with and without HF. We distributed this request to four Data Partners on December 4, 2020.

The study period includes data from January 1, 2010 to February 29, 2020.

The Sentinel System can be used to address numerous questions of importance to FDA during the COVID-19 pandemic. One population of significant interest for study is pregnant people. Evidence suggests that pregnant people are more likely to experience severe illness related to respiratory infections, including COVID-19, than nonpregnant people (1,2). Little information is available to support understanding the natural history of COVID-19 disease in pregnant people, or the impact of COVID-19 treatment upon pregnant people or the developing fetus.

To address vulnerable populations, including pregnant people, Sentinel published a COVID-19 Natural History Master Protocol, designed to identify multiple COVID-19 cohorts to support a variety of on-demand queries and subsequent descriptive and inferential studies. The European Medicines Agency (EMA) has also funded a project, called “COVID-19 infectiOn aNd medicineS In pregnancy” (CONSIGN) which has drafted a protocol to study the natural history of COVID-19 disease in pregnant people. The CONSIGN study was implemented in varied data sources across eight European countries.

This project implemented the following (three) aims of the CONSIGN protocol:

1. To estimate the prevalence of medicines used and compare this among pregnant people with COVID‐19, pregnant people without COVID‐19, and nonpregnant people with COVID‐19.
2. To describe severity and clinical outcomes of COVID‐19 disease in pregnant people with COVID‐19, according to treatments received during pregnancy, and compare these data with those of nonpregnant people of reproductive age with COVID‐19.
3. To assess and compare the rates of adverse maternal and neonatal outcomes in cohorts of pregnant people with COVID-19 diagnosis in the first, second, or third trimester during pregnancy and pregnant people without COVID-19.

(1) Allotey J, Stallings E, Bonet M, et al. Clinical manifestations, risk factors, and maternal and perinatal outcomes of coronavirus disease 2019 in pregnancy: living systematic review and meta-analysis. BMJ. Published online September 1, 2020:m3320. doi:10.1136/bmj.m3320

(2) Ellington S. Characteristics of Women of Reproductive Age with Laboratory-Confirmed SARS-CoV-2 Infection by Pregnancy Status — United States, January 22–June 7, 2020. MMWR Morb Mortal Wkly Rep. 2020;69. doi:10.15585/mmwr.mm6925a1

This study aimed to develop a claims-based algorithm using diagnosis and procedure codes for routine prenatal tests and fertility procedures to classify the timing of pregnancy start in a live-birth delivery cohort.

In this request we examined characteristics of pregnant individuals using modafinil or armodafinil, amphetamines, methylphenidate, or who were unexposed to those medical products in the Sentinel Distributed Database (SDD). We distributed this request to six Sentinel Data Partners on March 30, 2020. The study period included data from January 1, 2000 to December 31, 2018.

This analysis executed the Cohort Identification and Descriptive Analysis (CIDA) Pregnancy Episodes tool to assess risk of cardiac congenital malformations following armodafinil or modafinil use during the first trimester of pregnancy in the Sentinel Distributed Database (SDD). We distributed this request to six Sentinel Data Partners on June 20, 2020. The study period included data from January 1, 2000 to December 31, 2019.

The analytic package associated with this analysis can be found externally in Sentinel's Git Repository located here. The Git Repository serves as Sentinel's version control tracking system for analytic packages and technical documentation.