Evidence map›Paper›PMID 38604264›Full record

ArticleFertility and sterility2024

Predictive models of miscarriage on the basis of data from a preconception cohort study.

Jennifer J Yland, Zahra Zad, Tanran R Wang, Amelia K Wesselink, Tammy Jiang, Elizabeth E Hatch, Ioannis Ch Paschalidis, Lauren A Wise

Abstract read
In one paragraph

Article in Fertility and sterility, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

8 authors.

Jennifer J YlandDepartment of Epidemiology, Boston University School of Public Health, Boston, Massachusetts. Electronic address: yland@bu.edu.
Zahra ZadHariri Institute for Computing and Computational Science & Engineering, Boston University, Boston, Massachusetts; Division of Systems Engineering, Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts.
Tanran R WangDepartment of Epidemiology, Boston University School of Public Health, Boston, Massachusetts.
Amelia K WesselinkDepartment of Epidemiology, Boston University School of Public Health, Boston, Massachusetts.
Tammy JiangDepartment of Epidemiology, Boston University School of Public Health, Boston, Massachusetts.
Elizabeth E HatchDepartment of Epidemiology, Boston University School of Public Health, Boston, Massachusetts.
Ioannis Ch PaschalidisHariri Institute for Computing and Computational Science & Engineering, Boston University, Boston, Massachusetts; Division of Systems Engineering, Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts; Department of Biomedical Engineering, Boston University, Boston, Massachusetts.
Lauren A WiseDepartment of Epidemiology, Boston University School of Public Health, Boston, Massachusetts.

Funding

Project-005UL1TR001430 · NCATS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI BAIR-MERRITT, MEGAN H, CENTER, DAVID M. · 2015 to 2024
$52.3M
An internet-based preconception cohort study in North America and DenmarkR01HD086742 · NICHD · BOSTON UNIVERSITY MEDICAL CAMPUS · PI LAUREN A WISE · 2016 to 2026
$6.7M
Optimizing and Learning Strategies for Protein Docking R01GM135930 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI VAKILI, PIROOZ · 2019 to 2021
$561k
Feasibility of an internet-based study of time-to-pregnancy in the United StatesR21HD072326 · NICHD · BOSTON UNIVERSITY MEDICAL CAMPUS · PI WISE, LAUREN A · 2013 to 2014
$337k
NCATS NIH HHS UL1 TR001430NICHD NIH HHS R01 HD086742NICHD NIH HHS R21 HD072326NIGMS NIH HHS R01 GM135930
6 · The paper itself

Abstract

objectiveTo use self-reported preconception data to derive models that predict the risk of miscarriage.

designProspective preconception cohort study.

settingNot applicable. PATIENTS: Study participants were female, aged 21-45 years, residents of the United States or Canada, and attempting spontaneous pregnancy at enrollment during 2013-2022. Participants were followed for up to 12 months of pregnancy attempts; those who conceived were followed through pregnancy and postpartum. We restricted analyses to participants who conceived during the study period. EXPOSURE: On baseline and follow-up questionnaires completed every 8 weeks until pregnancy, we collected self-reported data on sociodemographic factors, reproductive history, lifestyle, anthropometrics, diet, medical history, and male partner characteristics. We included 160 potential predictor variables in our models.

main outcome measuresThe primary outcome was a miscarriage, defined as pregnancy loss before 20 weeks of gestation. We followed participants from their first positive pregnancy test until miscarriage or a censoring event (induced abortion, ectopic pregnancy, loss of follow-up, or 20 weeks of gestation), whichever occurred first. We fit both survival and static models using Cox proportional hazards models, logistic regression, support vector machines, gradient-boosted trees, and random forest algorithms. We evaluated model performance using the concordance index (survival models) and the weighted F1 score (static models).

resultsAmong the 8,720 participants who conceived, 20.4% reported miscarriage. In multivariable models, the strongest predictors of miscarriage were female age, history of miscarriage, and male partner age. The weighted F1 score ranged from 73%-89% for static models and the concordance index ranged from 53%-56% for survival models, indicating better discrimination for the static models compared with the survival models (i.e., the ability of the model to discriminate between individuals with and without miscarriage). No appreciable differences were observed across strata of miscarriage history or among models restricted to ≥8 weeks of gestation.

conclusionOur findings suggest that miscarriage is not easily predicted on the basis of preconception lifestyle characteristics and that advancing age and a history of miscarriage are the most important predictors of incident miscarriage.

Indexed as

Abortion, SpontaneousAdultCanadaCohort StudiesFemaleHumansMaleMiddle AgedPredictive Value of TestsPregnancyProspective StudiesRisk AssessmentRisk FactorsSelf ReportUnited StatesYoung Adultmachine learningMiscarriagepredictive modelingpregnancyspontaneous abortion

Identifiers

PMID38604264
PMCPMC11651128

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Registered trials

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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.