Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation

Linda J. E. Meertens, Pim van Montfort*, Hubertina C. J. Scheepers, Sander M. J. van Kuijk, Robert Aardenburg, Josje Langenveld, Ivo M. A. van Dooren, Iris M. Zwaan, Marc E. A. Spaanderman, Luc J. M. Smits

*Corresponding author for this work

Research output: Contribution to journal(Systematic) Review article peer-review

28 Citations (Web of Science)

Abstract

IntroductionPrediction models may contribute to personalized risk-based management of women at high risk of spontaneous preterm delivery. Although prediction models are published frequently, often with promising results, external validation generally is lacking. We performed a systematic review of prediction models for the risk of spontaneous preterm birth based on routine clinical parameters. Additionally, we externally validated and evaluated the clinical potential of the models.

Material and methodsPrediction models based on routinely collected maternal parameters obtainable during first 16weeks of gestation were eligible for selection. Risk of bias was assessed according to the CHARMS guidelines. We validated the selected models in a Dutch multicenter prospective cohort study comprising 2614 unselected pregnant women. Information on predictors was obtained by a web-based questionnaire. Predictive performance of the models was quantified by the area under the receiver operating characteristic curve (AUC) and calibration plots for the outcomes spontaneous preterm birth

ResultsFour studies describing five prediction models fulfilled the eligibility criteria. Risk of bias assessment revealed a moderate to high risk of bias in three studies. The AUC of the models ranged from 0.54 to 0.67 and from 0.56 to 0.70 for the outcomes spontaneous preterm birth

ConclusionsThis review revealed several reporting and methodological shortcomings of published prediction models for spontaneous preterm birth. Our external validation study indicated that none of the models had the ability to predict spontaneous preterm birth adequately in our population. Further improvement of prediction models, using recent knowledge about both model development and potential risk factors, is necessary to provide an added value in personalized risk assessment of spontaneous preterm birth.

Original languageEnglish
Pages (from-to)907-920
Number of pages14
JournalActa Obstetricia et Gynecologica Scandinavica
Volume97
Issue number8
Early online date17 Apr 2018
DOIs
Publication statusPublished - Aug 2018

Keywords

  • Spontaneous preterm birth
  • spontaneous preterm delivery
  • prediction
  • risk assessment
  • external validation
  • decision curve analysis
  • systematic review
  • 1ST TRIMESTER
  • SHORT CERVIX
  • PREGNANCY
  • REGRESSION
  • DELIVERY
  • MANAGEMENT
  • MORTALITY

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