Developing and refining methods to assess medical product utilization, safety, and effectiveness during pregnancy is a focus of FDA’s Sentinel System. The Sentinel Common Data Model (SCDM) includes a Mother-Infant Linkage (MIL) table that enables routine evaluation of the effects of medical product exposures during pregnancy on infant outcomes. Descriptions of efforts led by the Center for Drug Evaluation and Research are shown below. Please visit the links to learn more about each area of activity.
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:
- 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.
- 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.
- 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.
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.
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.
The 2020 Sentinel Public Training consisted of presentations on the Sentinel System’s distributed database and broad analytic capabilities. We discussed pregnancy-related analyses including how Sentinel links and uses mother and infant data, cohort identification approaches for assessing medical product use during pregnancy, and a case study that employs a new inferential analysis tool. The training was conducted by Sentinel epidemiologist investigators.
If you have any questions or concerns, please email info@sentinelsystem.org.
In this request we replicated the Hernandez-Diaz, et al. (1) study assessing risk of oral clefts with topiramate use during the first trimester of pregnancy. The replication was conducted to assess the performance of a newly developed inferential pregnancy tool for use in the Sentinel Distributed Database (SDD). The study period included data from January 1, 2000 to September 30, 2015. We distributed this request to six Sentinel Data Partners on June 19, 2020. These six Data Partners are a subset of the SDD and only include those that populate the Mother-Infant Linkage Table.
Report 1 does not require any duration of medical or drug coverage for mothers and infants after delivery.
Report 2 requires that mothers and infants have 90 days of medical and drug coverage after delivery.
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.
(1) Hernández-Díaz, S., et al. Topiramate use early in pregnancy and the risk of oral clefts: A pregnancy cohort study. Neurology. 2018; 90(4):e342-e351.
This analysis characterized the frequency of IV iron utilization by week relative to live birth and stillbirth deliveries. Information from this analysis contributed to a class-wide labeling update for parenteral iron products to add new safety information to the Use in Specific Populations, Pregnancy section of the label. This update describes the risk of severe adverse reactions to pregnant women and their fetus.
Dolutegravir (DTG) is an integrase strand transfer inhibitor indicated for the treatment of human immunodeficiency virus (HIV) type-1 infection. This study aims to describe the prevalence of exposure to DTG-containing regimens among women of child-bearing age living with HIV and among pregnant women in the Sentinel System. Using the Sentinel Distributed Database (SDD), the prevalence of DTG use was examined (using National Drug Codes) among women of childbearing age (15 to 49 years old) with an HIV diagnosis and among women aged 15 to 49 years old with live birth deliveries from August 2013 through March 2018.
The following presentation was presented at the 35th International Conference on Pharmacoepidemiology and Therapeutic Risk Management in 2019.