Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes

Abstract

Objective: The aim of the present study was to identify the factors associated with non-attendance of immediate postpartum glucose test using a machine learning algorithm following gestational diabetes mellitus (GDM) pregnancy.

Method: A retrospective cohort study of all GDM women (n = 607) for postpartum glucose test due between January 2016 and December 2019 at the George Eliot Hospital NHS Trust, UK.

Results: Sixty-five percent of women attended postpartum glucose test. Type 2 diabetes was diagnosed in 2.8% and 21.6% had persistent dysglycaemia at 6-13 weeks post-delivery. Those who did not attend postpartum glucose test seem to be younger, multiparous, obese, and continued to smoke during pregnancy. They also had higher fasting glucose at antenatal oral glucose tolerance test. Our machine learning algorithm predicted postpartum glucose non-attendance with an area under the receiver operating characteristic curve of 0.72. The model could achieve a sensitivity of 70% with 66% specificity at a risk score threshold of 0.46. A total of 233 (38.4%) women attended subsequent glucose test at least once within the first two years of delivery and 24% had dysglycaemia. Compared to women who attended postpartum glucose test, those who did not attend had higher conversion rate to type 2 diabetes (2.5% vs 11.4%; p = 0.005).

Conclusion: Postpartum screening following GDM is still poor. Women who did not attend postpartum screening appear to have higher metabolic risk and higher conversion to type 2 diabetes by two years post-delivery. Machine learning model can predict women who are unlikely to attend postpartum glucose test using simple antenatal factors. Enhanced, personalised education of these women may improve postpartum glucose screening.

Fig 1. Consort diagram of immediate postpartum glucose testing attendance and follow-up for 24-months post-delivery.

The flow chart displayed the proportion of GDM women attended vs not attended ppGT. The diagnosis of dysglycaemia/impaired glucose regulation and Type 2 diabetes was performed as, Normal glycaemia: FPG <5.6 and 2-hr glucose <7.8 at postpartum OGTT or HbA1c <39 mmol/mol (<5.7%); Impaired glucose regulation: IFG, IGT and/or Prediabetes [Impaired Fasting glucose: FPG ≥5.6mmol/L; Impaired Glucose tolerance: 2-hr glucose ≥7.8 mmol/L; Prediabetes: HbA1c ≥39 to <48 mmol/mol (≥5.7 and <6.4%)]; Type 2 diabetes: FPG ≥7.0mmol/L and/or 2-hr ≥11.1mmol/L post 75g OGTT or HbA1c ≥48mmol/mol (≥6.5%).

Fig 2. AUROC for prediction of non-attendance at ppGT.

AUROC was used to evaluate the performance of our machine learning based algorithm using logistic regression model on the validation cohort, n = 607 by aggregating the predictions from the 5 test folds of CV1. The area under ROC was 0.72. The dotted line indicates optimal threshold. The grey line indicates ‘target none’ approach and black line indicates ‘target all’ approach. The blue line indicates the net benefit of the proposed ML prediction model.

Fig 3. Decision curve analysis for the standardized net benefit obtained from the proposed ML model.

The DCA (Decision curve analysis) showed the net benefit obtained from the ML (blue line) prediction model compared to the target all (solid black line) or target none (solid grey line). Net benefit by implementing our model in a clinical setting is larger when compared to the follow-up of all GDM women for ppGT. DCA was derived from the equation, Net benefit = $\frac{1}{N}\left[TP-FP\left(\frac{p_t}{1-p_t}\right)\right]$, where TP and FP are the true positives and false positives respectively, p_t is the probability threshold, and N is the total number of participants in the validation cohort, N = 607.