Revised Gestational Diabetes Prediction Tool Shows Improved Performance

Via Peters

The study covered in this summary was published in medRxiv.org as a preprint and has not yet been peer-reviewed.

Key Takeaways

  • An external validation study provided guidance into needed revisions to an earlier model used to predict a pregnant woman’s risk for developing gestational diabetes mellitus (GDM) early during pregnancy across many settings and revised ethnicity categories.

  • The revision applied best practices using large validation and updating datasets derived from an ethnically diverse population with GDM diagnosis based on contemporary criteria and a universal screening strategy, with a GDM prevalence of 18.0%.

  • The report demonstrates the potential value of working from an existing validated model and performing an update to sustain predictive performance over time rather than starting from scratch.

Why This Matters

  • Growing evidence supports integration of the revised GDM prediction model into routine practice to expedite and improve risk-stratified care to women at risk for GDM.

  • Both the original and newly revised models use variables that are routinely collected and recorded in clinical practice, thereby avoiding the barriers and costs for collecting additional information.

Study Design

  • The researchers used routinely collected health data for 26,474 singleton pregnancies resulting in a birth from January 2016 to December 2018 at Monash Health, Australia’s largest health service that includes three maternity hospitals and serves an ethnically diverse population.

  • They defined diagnosis of GDM using the criteria of the International Association of Diabetes and Pregnancy Study Groups.

  • The investigators updated the ethnicity classification system to reflect international ethnicity categories and self-reported ethnicity designations. In contrast, the original model relied on extrapolating ethnicity from country of birth.

  • They selected the best model using predictive performance measures and a closed testing procedure. C-statistics (the area under the receiver operating characteristic curve) were used to assess and compare the models they developed.

Key Results

  • The original model produced a C-statistic of 0.698, which shows “reasonable” discrimination.

  • Model C2 emerged as the preferred model because of the comparable calibration plot in the high prevalence region, a superior C-statistic of 0.732, its use of more generalizable ethnicity categories, and because it showed a significantly better fit during closed testing.

  • The new model uses the variables of age, body mass index, family history of diabetes, history of GDM, history of poor obstetric outcome, and ethnicity.

Limitations

  • The revised model handles the continuous variables of body mass index and age as categorical variables, an approach that can reduce predictive power and may be superseded by electronic risk calculators. Reestimating the relationship between body mass index and age as continuous variables and the diagnosis of GDM would produce a completely new prediction model, extending beyond the scope of validation and updating.

Disclosures

  • The preprint currently contains no information on funding nor authors disclosures.

  • A previously published description of the design of the study indicated that it received no commercial funding and that none of the authors had commercial disclosures.

This is a summary of a preprint research study External validation and updating of a prediction model for the diagnosis of gestational diabetes mellitus, written by researchers primarily based at Monash University, Clayton, Australia, on medRxiv provided to you by Medscape. This study has not yet been peer-reviewed. The full text of the study can be found on medRxiv.org.

Mitchel L. Zoler is a reporter for Medscape and MDedge based in the Philadelphia area. @mitchelzoler

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https://www.medscape.com/viewarticle/965699

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