Evidence map›Paper›PMID 42597142›Full record

ArticlePregnancy (Hoboken, N.J.)2026

Development and validation of a prediction tool to optimize antenatal corticosteroid timing in patients at risk of spontaneous preterm birth.

Moti Gulersen, Adam Lin, Ansaf Salleb-Aouissi, Anita Raja, Robert M Silver, William A Grobman, Lynn M Yee, Judith H Chung, Hyagriv N Simhan, Ashley N Battarbee and 3 more

Abstract read
In one paragraph

Article in Pregnancy (Hoboken, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Moti GulersenDivision of Maternal-Fetal Medicine Department of Obstetrics and Gynecology Sidney Kimmel Medical College of Thomas Jefferson University Philadelphia Pennsylvania USA.ORCID https://orcid.org/0000-0002-1966-9578
Adam LinDepartment of Computer Science Columbia University New York New York USA.
Ansaf Salleb-AouissiDepartment of Computer Science Columbia University New York New York USA.
Anita RajaDepartment of Computer Science CUNY Hunter College New York New York USA.
Robert M SilverDivision of Maternal-Fetal Medicine Department of Obstetrics and Gynecology Spencer Fox Eccles School of Medicine at the University of Utah Salt Lake City Utah USA.ORCID https://orcid.org/0000-0002-5794-3152
William A GrobmanDivision of Maternal-Fetal Medicine Department of Obstetrics and Gynecology Warren Alpert Medical School of Brown University Providence Rhode Island USA.
Lynn M YeeDivision of Maternal-Fetal Medicine Department of Obstetrics and Gynecology Northwestern University Feinberg School of Medicine Chicago Illinois USA.
Judith H ChungDivision of Maternal-Fetal Medicine Department of Obstetrics and Gynecology University of California Irvine Orange California USA.
Hyagriv N SimhanDivision of Maternal-Fetal Medicine Department of Obstetrics Gynecology, and Reproductive Sciences University of Pittsburgh Pittsburgh Pennsylvania USA.
Ashley N BattarbeeDivision of Maternal-Fetal Medicine Department of Obstetrics and Gynecology University of Alabama Birmingham Alabama USA.
Cynthia Gyamfi-BannermanDivision of Maternal-Fetal Medicine Department of Obstetrics Gynecology, and Reproductive Sciences University of California at San Diego La Jolla California USA.
Vincenzo BerghellaDivision of Maternal-Fetal Medicine Department of Obstetrics and Gynecology Sidney Kimmel Medical College of Thomas Jefferson University Philadelphia Pennsylvania USA.
David M HaasDivision of Maternal-Fetal Medicine Department of Obstetrics and Gynecology Indiana University School of Medicine Indianapolis Indiana USA.ORCID https://orcid.org/0000-0002-8379-0743

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The objective of this study was to develop and validate a preterm birth (PTB) prediction model to optimize the timing of antenatal corticosteroid (ACS) administration in patients at risk of spontaneous PTB. Methods: Secondary analysis of the nuMoM2b (Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be) prospective cohort study including participants who received ACS due to increased risk for spontaneous PTB (i.e., development cohort). Nulliparous participants presenting between 23 0/7 and 36 6/7 weeks of gestation were eligible for inclusion and were characterized into two groups based on the time interval from the first dose of ACS administration to delivery: ≤7 versus >7 days. Multivariable logistic regression was utilized to assess clinical characteristics at the time of the first dose of ACS administration as candidate predictors for optimal ACS timing, defined as delivery within 7 days of ACS administration. We then externally validated the model using data from patients at risk for spontaneous PTB at two academic centers. The predictive performance of each model was assessed using area under the receiver operating curve (ROC AUC), precision, and recall scores with 95% confidence intervals (CIs). Results: Key predictors for optimal ACS timing were rupture of membranes, cervical dilation and effacement at admission, maternal age, and gravidity. In the development cohort, the model achieved an AUC of 0.84 (95% CI, 0.77-0.92). External validation of these models in two independent cohorts demonstrated consistent performances (AUC, 0.82; 95% CI, 0.75-0.88 and AUC, 0.81; 95% CI, 0.72-0.89). Conclusion: We successfully developed and externally validated a model that predicts whether nulliparas are expected to deliver within 7 days of ACS administration. Further studies can assess whether incorporating this model into clinical practice improves neonatal outcomes.

Indexed as

betamethasoneclinical prediction modelprematurityrisk estimationrisk stratification

Identifiers

PMID42597142
PMCPMC13344636

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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.