Evidence map›Paper›PMID 39456023›Full record

ArticleBMC medicine2024

Accelerating adverse pregnancy outcomes research amidst rising medication use: parallel retrospective cohort analyses for signal prioritization.

Yeon Mi Hwang, Samantha N Piekos, Alison G Paquette, Qi Wei, Nathan D Price, Leroy Hood, Jennifer J Hadlock

Abstract read
In one paragraph

Article in BMC medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Yeon Mi HwangInstitute for Systems Biology, Seattle, WA, USA.
Samantha N PiekosInstitute for Systems Biology, Seattle, WA, USA.
Alison G PaquetteInstitute for Systems Biology, Seattle, WA, USA.
Qi WeiInstitute for Systems Biology, Seattle, WA, USA.
Nathan D PriceInstitute for Systems Biology, Seattle, WA, USA.
Leroy HoodInstitute for Systems Biology, Seattle, WA, USA.
Jennifer J HadlockInstitute for Systems Biology, Seattle, WA, USA. jhadlock@systemsbiology.org.

Funding

Harnessing "omics": A Systems Biology approach to discovery of biologicalpathways in placental development and parturitionR01HD091527 · NICHD · UNIVERSITY OF FLORIDA · PI BALONI, PRIYANKA, JONES, HELEN N · 2017 to 2021
$2.7M
Eunice Kennedy Shriver National Institute of Child Health and Human Development HD091527NICHD NIH HHS R01 HD091527
6 · The paper itself

Abstract

backgroundPregnant women are significantly underrepresented in clinical trials, yet most of them take medication during pregnancy despite the limited safety data. The objective of this study was to characterize medication use during pregnancy and apply propensity score matching method at scale on patient records to accelerate and prioritize the drug effect signal detection associated with the risk of preterm birth and other adverse pregnancy outcomes.

methodsThis was a retrospective study on continuously enrolled women who delivered live births between 2013/01/01 and 2022/12/31 (n = 365,075) at Providence St. Joseph Health. Our exposures of interest were all outpatient medications prescribed during pregnancy. We limited our analyses to medication that met the minimal sample size (n = 600). The primary outcome of interest was preterm birth. Secondary outcomes of interest were small for gestational age and low birth weight. We used propensity score matching at scale to evaluate the risk of these adverse pregnancy outcomes associated with drug exposure after adjusting for demographics, pregnancy characteristics, and comorbidities.

resultsThe total medication prescription rate increased from 58.5 to 75.3% (P < 0.0001) from 2013 to 2022. The prevalence rate of preterm birth was 7.7%. One hundred seventy-five out of 1329 prenatally prescribed outpatient medications met the minimum sample size. We identified 58 medications statistically significantly associated with the risk of preterm birth (P ≤ 0.1; decreased: 12, increased: 46).

conclusionsMost pregnant women are prescribed medication during pregnancy. This highlights the need to utilize existing real-world data to enhance our knowledge of the safety of medications in pregnancy. We narrowed down from 1329 to 58 medications that showed statistically significant association with the risk of preterm birth even after addressing numerous covariates through propensity score matching. This data-driven approach demonstrated that multiple testable hypotheses in pregnancy pharmacology can be prioritized at scale and lays the foundation for application in other pregnancy outcomes.

Indexed as

Pregnancy OutcomePremature BirthAdultDrug-Related Side Effects and Adverse ReactionsFemaleHumansInfant, NewbornPregnancyPregnancy ComplicationsPropensity ScoreRetrospective StudiesYoung AdultData miningDrug-related side effects and adverse reactionsPharmacoepidemiologyPregnancyPreterm birth

Identifiers

PMID39456023
PMCPMC11520034

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.