Academic Journal
View in EDS

Predicting first-trimester pregnancy outcome in threatened miscarriage : a comparison of a multivariate logistic regression and machine learning models

Bibliographic Details
Title: Predicting first-trimester pregnancy outcome in threatened miscarriage : a comparison of a multivariate logistic regression and machine learning models
Authors: Sammut, Lara Maria, Bezzina, Paul, Gibbs, Vivien, Muscat Baron, Yves, Agius-Camenzuli, A., Calleja-Agius, Jean
Publisher Information: Elsevier Ltd.
Publication Year: 2025
Collection: University of Malta: OAR@UM / L-Università ta' Malta
Subject Terms: Miscarriage, Pregnancy -- Trimester, First, Multivariate analysis -- Data processing, Machine learning, Random graphs, Biochemical markers
Description: Introduction: Threatened miscarriage (TM), defined as first-trimester vaginal bleeding with a closed cervix and detectable fetal cardiac activity, affects up to 30 % of clinically recognised pregnancies and is linked to increased risk of adverse outcomes. This study evaluates the predictive value of first-trimester ultrasound (US) and biochemical (BC) markers in determining outcomes among women with TM symptoms. Methods: This prospective cohort study recruited 118 women with viable singleton pregnancies (5+0 to 12+6 weeks' gestation) from Malta's national public hospital between January 2023 and June 2024. Participants underwent US and BC assessment, along with collection of clinical and sociodemographic data. Pregnancy outcomes were followed to term and classified as live birth or loss. Univariate logistic regression identified individual predictors. Multivariate logistic regression (MLR) and random forest (RF) modelling assessed combined predictive performance. Results: Among 118 TM cases, 77 % resulted in live birth, 23 % in loss. MLR identified progesterone, cervical length, mean gestational sac diameter (MGSD), trophoblast thickness, sFlt-1:PlGF ratio, and maternal age as significant predictors. Higher progesterone, cervical length, MGSD, and sFlt-1:PlGF ratio reduced risk, while maternal age over 35 increased it. MLR achieved 82.7 % accuracy (AUC = 0.89). RF improved accuracy to 93.1 % (AUC = 0.97), confirming the combined predictive value of US and BC markers. Conclusion: US and BC markers hold predictive value in TM. Machine learning, particularly RF, may improve early clinical risk stratification. Implications for practice: This tool may support timely decision-making and personalised monitoring, intervention, and counselling for women with TM. ; peer-reviewed
Document Type: article in journal/newspaper
Language: English
Relation: https://www.um.edu.mt/library/oar/handle/123456789/138848
DOI: 10.1016/j.radi.2025.103159
Availability: https://www.um.edu.mt/library/oar/handle/123456789/138848
https://doi.org/10.1016/j.radi.2025.103159
Rights: info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.
Accession Number: edsbas.DF1304E1
Database: BASE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://www.um.edu.mt/library/oar/handle/123456789/138848#
    Name: EDS - BASE (ns324271)
    Category: fullText
    Text: View record from BASE
Header DbId: edsbas
DbLabel: BASE
An: edsbas.DF1304E1
RelevancyScore: 978
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 977.8095703125
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Predicting first-trimester pregnancy outcome in threatened miscarriage : a comparison of a multivariate logistic regression and machine learning models
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Sammut%2C+Lara+Maria%22">Sammut, Lara Maria</searchLink><br /><searchLink fieldCode="AR" term="%22Bezzina%2C+Paul%22">Bezzina, Paul</searchLink><br /><searchLink fieldCode="AR" term="%22Gibbs%2C+Vivien%22">Gibbs, Vivien</searchLink><br /><searchLink fieldCode="AR" term="%22Muscat+Baron%2C+Yves%22">Muscat Baron, Yves</searchLink><br /><searchLink fieldCode="AR" term="%22Agius-Camenzuli%2C+A%2E%22">Agius-Camenzuli, A.</searchLink><br /><searchLink fieldCode="AR" term="%22Calleja-Agius%2C+Jean%22">Calleja-Agius, Jean</searchLink>
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: Elsevier Ltd.
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2025
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: University of Malta: OAR@UM / L-Università ta' Malta
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Miscarriage%22">Miscarriage</searchLink><br /><searchLink fieldCode="DE" term="%22Pregnancy+--+Trimester%22">Pregnancy -- Trimester</searchLink><br /><searchLink fieldCode="DE" term="%22First%22">First</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis+--+Data+processing%22">Multivariate analysis -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Random+graphs%22">Random graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Biochemical+markers%22">Biochemical markers</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Introduction: Threatened miscarriage (TM), defined as first-trimester vaginal bleeding with a closed cervix and detectable fetal cardiac activity, affects up to 30 % of clinically recognised pregnancies and is linked to increased risk of adverse outcomes. This study evaluates the predictive value of first-trimester ultrasound (US) and biochemical (BC) markers in determining outcomes among women with TM symptoms. Methods: This prospective cohort study recruited 118 women with viable singleton pregnancies (5+0 to 12+6 weeks' gestation) from Malta's national public hospital between January 2023 and June 2024. Participants underwent US and BC assessment, along with collection of clinical and sociodemographic data. Pregnancy outcomes were followed to term and classified as live birth or loss. Univariate logistic regression identified individual predictors. Multivariate logistic regression (MLR) and random forest (RF) modelling assessed combined predictive performance. Results: Among 118 TM cases, 77 % resulted in live birth, 23 % in loss. MLR identified progesterone, cervical length, mean gestational sac diameter (MGSD), trophoblast thickness, sFlt-1:PlGF ratio, and maternal age as significant predictors. Higher progesterone, cervical length, MGSD, and sFlt-1:PlGF ratio reduced risk, while maternal age over 35 increased it. MLR achieved 82.7 % accuracy (AUC = 0.89). RF improved accuracy to 93.1 % (AUC = 0.97), confirming the combined predictive value of US and BC markers. Conclusion: US and BC markers hold predictive value in TM. Machine learning, particularly RF, may improve early clinical risk stratification. Implications for practice: This tool may support timely decision-making and personalised monitoring, intervention, and counselling for women with TM. ; peer-reviewed
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: article in journal/newspaper
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: https://www.um.edu.mt/library/oar/handle/123456789/138848
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1016/j.radi.2025.103159
– Name: URL
  Label: Availability
  Group: URL
  Data: https://www.um.edu.mt/library/oar/handle/123456789/138848<br />https://doi.org/10.1016/j.radi.2025.103159
– Name: Copyright
  Label: Rights
  Group: Cpyrght
  Data: info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsbas.DF1304E1
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.DF1304E1
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.radi.2025.103159
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Miscarriage
        Type: general
      – SubjectFull: Pregnancy -- Trimester
        Type: general
      – SubjectFull: First
        Type: general
      – SubjectFull: Multivariate analysis -- Data processing
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Random graphs
        Type: general
      – SubjectFull: Biochemical markers
        Type: general
    Titles:
      – TitleFull: Predicting first-trimester pregnancy outcome in threatened miscarriage : a comparison of a multivariate logistic regression and machine learning models
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Sammut, Lara Maria
      – PersonEntity:
          Name:
            NameFull: Bezzina, Paul
      – PersonEntity:
          Name:
            NameFull: Gibbs, Vivien
      – PersonEntity:
          Name:
            NameFull: Muscat Baron, Yves
      – PersonEntity:
          Name:
            NameFull: Agius-Camenzuli, A.
      – PersonEntity:
          Name:
            NameFull: Calleja-Agius, Jean
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-locals
              Value: edsbas
            – Type: issn-locals
              Value: edsbas.oa
ResultId 1