Early antenatal prediction of gestational diabetes in obese women: development of prediction tools for targeted intervention

All obese women are categorised as being of equally high risk of gestational diabetes (GDM) whereas the majority do not develop the disorder. Lifestyle and pharmacological interventions in unselected obese pregnant women have been unsuccessful in preventing GDM. Our aim was to develop a prediction tool for early identification of obese women at high risk of GDM to facilitate targeted interventions in those most likely to benefit. Clinical and anthropometric data and non-fasting blood samples were obtained at 15+0–18+6 weeks’ gestation in 1303 obese pregnant women from UPBEAT, a randomised controlled trial of a behavioural intervention. Twenty one candidate biomarkers associated with insulin resistance, and a targeted nuclear magnetic resonance (NMR) metabolome were measured. Prediction models were constructed using stepwise logistic regression. Twenty six percent of women (n = 337) developed GDM (International Association of Diabetes and Pregnancy Study Groups criteria). A model based on clinical and anthropometric variables (age, previous GDM, family history of type 2 diabetes, systolic blood pressure, sum of skinfold thicknesses, waist:height and neck:thigh ratios) provided an area under the curve of 0.71 (95%CI 0.68–0.74). This increased to 0.77 (95%CI 0.73–0.80) with addition of candidate biomarkers (random glucose, haemoglobin A1c (HbA1c), fructosamine, adiponectin, sex hormone binding globulin, triglycerides), but was not improved by addition of NMR metabolites (0.77; 95%CI 0.74–0.81). Clinically translatable models for GDM prediction including readily measurable variables e.g. mid-arm circumference, age, systolic blood pressure, HbA1c and adiponectin are described. Using a ≥35% risk threshold, all models identified a group of high risk obese women of whom approximately 50% (positive predictive value) later developed GDM, with a negative predictive value of 80%. Tools for early pregnancy identification of obese women at risk of GDM are described which could enable targeted interventions for GDM prevention in women who will benefit the most

Impact of an Electronic Clinical Decision Support Tool on Early Maternal Glucose Screening in Pregnancy

abstract: Gestational diabetes mellitus (GDM), diabetes diagnosed in the second or third trimester of pregnancy that is not clearly overt diabetes, has become more common as the rates of obesity in women of childbearing age have increased. Undiagnosed, uncontrolled diabetes in pregnancy can lead to maternal and infant health comorbidities as well as have adverse long-term effects for mother or baby. Although routine screening for gestational diabetes mellitus (GDM) occurs between 24 and 28 weeks gestation, the American Congress of Obstetricians and Gynecologists (ACOG) recommends screening earlier in pregnancy for women at risk for undiagnosed type 2 diabetes. Risk factors include previous history of GDM, known impaired glucose metabolism, or obesity (BMI > 30). The purpose of this project is to implement the clinical practice guideline for early maternal glucose screening during pregnancy in women with risk factors through the integration of a clinical decision support (CDS) tool in an electronic health record (EHR). CDS tools can be utilized as a point of care strategy to remind providers of the clinical practice guidelines and to assist providers in decision-making related to screening. Participating providers (n=18) utilized the CDS tool during the initial obstetrical visit for at risk women without a prepregnancy diabetes diagnosis and entering prenatal care prior to 24 weeks. The impact of implantation of the CDS tool shows that an increase in screening was statistically significant (p<.001)

Patient-oriented computerized clinical guidelines for mobile decision support in gestational diabetes

The risks associated with gestational diabetes (GD) can be reduced with an active treatment able to improve glycemic control. Advances in mobile health can provide new patient-centric models for GD to create personalized health care services, increase patient independence and improve patients’ self-management capabilities, and potentially improve their treatment compliance. In these models, decision-support functions play an essential role. The telemedicine system MobiGuide provides personalized medical decision support for GD patients that is based on computerized clinical guidelines and adapted to a mobile environment. The patient’s access to the system is supported by a smartphone-based application that enhances the efficiency and ease of use of the system. We formalized the GD guideline into a computer-interpretable guideline (CIG). We identified several workflows that provide decision-support functionalities to patients and 4 types of personalized advice to be delivered through a mobile application at home, which is a preliminary step to providing decision-support tools in a telemedicine system: (1) therapy, to help patients to comply with medical prescriptions; (2) monitoring, to help patients to comply with monitoring instructions; (3) clinical assessment, to inform patients about their health conditions; and (4) upcoming events, to deal with patients’ personal context or special events. The whole process to specify patient-oriented decision support functionalities ensures that it is based on the knowledge contained in the GD clinical guideline and thus follows evidence-based recommendations but at the same time is patient-oriented, which could enhance clinical outcomes and patients’ acceptance of the whole system