Evidence map›Paper›PMID 38940904›Full record

ReviewDrug safety2024

Use of the Sentinel System to Examine Medical Product Use and Outcomes During Pregnancy.

Jennifer G Lyons, Mayura U Shinde, Judith C Maro, Andrew Petrone, Austin Cosgrove, Maria E Kempner, Susan E Andrade, Jamila Mwidau, Danijela Stojanovic, José J Hernández-Muñoz and 1 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Drug safety, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jennifer G LyonsDivision of Therapeutics Research and Infectious Disease Epidemiology, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, MA, 02215, USA. Jennifer_Lyons@harvardpilgrim.org.ORCID http://orcid.org/0000-0002-5224-0298
Mayura U ShindeDivision of Therapeutics Research and Infectious Disease Epidemiology, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, MA, 02215, USA.
Judith C MaroDivision of Therapeutics Research and Infectious Disease Epidemiology, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, MA, 02215, USA.ORCID http://orcid.org/0000-0001-9900-2142
Andrew PetroneDivision of Therapeutics Research and Infectious Disease Epidemiology, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, MA, 02215, USA.ORCID http://orcid.org/0000-0001-8413-6236
Austin CosgroveDivision of Therapeutics Research and Infectious Disease Epidemiology, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, MA, 02215, USA.
Maria E KempnerDivision of Therapeutics Research and Infectious Disease Epidemiology, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, MA, 02215, USA.
Susan E AndradeDivision of Therapeutics Research and Infectious Disease Epidemiology, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, MA, 02215, USA.
Jamila MwidauOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.
Danijela StojanovicOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.
José J Hernández-MuñozOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.
Sengwee TohDivision of Therapeutics Research and Infectious Disease Epidemiology, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, MA, 02215, USA.ORCID http://orcid.org/0000-0002-5160-0810

Funding

U.S. Food and Drug Administration 75F40119D10037
6 · The paper itself

Abstract

While many pregnant individuals use prescription medications, evidence supporting product safety during pregnancy is often inadequate. Existing electronic healthcare data sources provide large, diverse samples of health plan members to allow for the study of medical product utilization during pregnancy, as well as pregnancy, maternal, and infant outcomes. The Sentinel System is a national medical product surveillance system that includes administrative claims and electronic health record databases from large national and regional health insurers. In addition to these data sources, Sentinel develops and maintains a sizeable selection of analytic tools to facilitate epidemiologic analyses in a way that protects patient privacy and health system autonomy. In this article, we provide an overview of Sentinel System infrastructure, including the Mother-Infant Linkage Table, parameterizable analytic tools, and algorithms to estimate gestational age and identify pregnancy outcomes. We also describe past and future Sentinel work that contributes to our understanding of the way medical products are used and the safety of these products during pregnancy.

Indexed as

Pregnancy OutcomeDatabases, FactualDrug-Related Side Effects and Adverse ReactionsElectronic Health RecordsFemaleHumansPregnancyProduct Surveillance, PostmarketingSentinel SurveillanceUnited States

Identifiers

PMID38940904

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.