Development, validation and clinical utility of a risk prediction model for adverse pregnancy outcomes in women with gestational diabetes: The PeRSonal GDM model

Abstract

Background: The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with gestational diabetes mellitus (GDM) would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. We aimed to develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM.

Methods: A prediction model development and validation study was conducted on data from a observational cohort. Participants included all women with GDM from three metropolitan tertiary teaching hospitals in Melbourne, Australia. The development cohort comprised those who delivered between 1 July 2017 to 30 June 2018 and the validation cohort those who delivered between 1 July 2018 to 31 December 2018. The main outcome was a composite of critically important maternal and perinatal complications (hypertensive disorders of pregnancy, large-for-gestational age neonate, neonatal hypoglycaemia requiring intravenous therapy, shoulder dystocia, perinatal death, neonatal bone fracture and nerve palsy). Model performance was measured in terms of discrimination and calibration and clinical utility evaluated using decision curve analysis.

Findings: The final PeRSonal (Prediction for Risk Stratified care for women with GDM) model included body mass index, maternal age, fasting and 1-hour glucose values (75-g oral glucose tolerance test), gestational age at GDM diagnosis, Southern and Central Asian ethnicity, East Asian ethnicity, nulliparity, past delivery of an large-for-gestational age neonate, past pre-eclampsia, GWG until GDM diagnosis, and family history of diabetes. The composite adverse pregnancy outcome occurred in 27% (476/1747) of women in the development (1747 women) and in 26% (244/955) in the validation (955 women) cohorts. The model showed excellent calibration with slope of 0.99 (95% CI 0.75 to 1.23) and acceptable discrimination (c-statistic 0.68; 95% CI 0.64 to 0.72) when temporally validated. Decision curve analysis demonstrated that the model was useful across a range of predicted probability thresholds between 0.15 and 0.85 for adverse pregnancy outcomes compared to the alternatives of managing all women with GDM as if they will or will not have an adverse pregnancy outcome.

Interpretation: The PeRSonal GDM model comprising of routinely available clinical data shows compelling performance, is transportable across time, and has clinical utility across a range of predicted probabilities. Further external validation of the model to a more disparate population is now needed to assess the generalisability to different centres, community based care and low resource settings, other healthcare systems and to different GDM diagnostic criteria.

Funding: This work is supported by the Mothers and Gestational Diabetes in Australia 2 NHMRC funded project #1170847.

Keywords: Adverse pregnancy outcomes; Gestational diabetes mellitus (GDM); Large-for-gestational age (LGA); Neonatal hypoglycaemia; Pre-eclampsia; Prediction model; Pregnancy complications; Prognosis; Risk-stratification.

Figure 1

Calibration plot for the PeRSonal GDM model. The predicted probability of adverse pregnancy outcome (x-axis) is compared to observed frequency (y-axis) in women with gestational diabetes in the development cohort (top panel) and the validation cohort (bottom panel). The plot is grouped by deciles and quintiles of the predicted risk (green circles) with 95% confidence intervals (green lines) in the development and validation cohort respectively, and supplemented by a smoothed (Lowess) line. A spike plot of the distribution of events (adverse pregnancy outcome) and non-events (red). Perfect predictions should lie on the 45 reference (dashed).

Figure 2

Decision curve analysis using the PeRSonal GDM model to guide decisions at healthcare systems and individual shared decision making levels. As compared to a reference strategy where all women with GDM are managed as if they will have an adverse pregnancy outcome, the model offers no benefit at a threshold probability of < 0.15. Likewise, where all women with GDM are managed as if they will not have an adverse pregnancy outcome, the model offers no benefit at a threshold probability of > 0.85. Selection of a threshold probability between these thresholds, enables decisions on healthcare provision based on risk to be superior to the alternative reference strategies of managing all women as if they will or will not have an adverse pregnancy outcome.