Predicting the Need for Insulin Treatment: A Risk-Based Approach to the Management of Women with Gestational Diabetes Mellitus

Abstract

Gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes including large for gestational age infants. Individualizing the management of women with GDM based on the likelihood of needing insulin may improve pregnancy outcomes. The aim of this study is to identify characteristics associated with a need for insulin in women with GDM, and to develop a predictive model for insulin requirement. A historical cohort study was conducted among all women with GDM in a singleton pregnancy at Aarhus University Hospital from 2012 to 2017. Variables associated with insulin treatment were identified through multivariable logistic regression. The variables were dichotomized and included in a point scoring system aiming to predict the likelihood of needing insulin. Seven variables were associated with needing insulin: family history of diabetes, current smoker, multiparity, prepregnancy body mass index, gestational age at the oral glucose tolerance test (OGTT), 2-h glucose value at the OGTT and hemoglobin A1c at diagnosis. A risk score was calculated assigning one point to each variable. On ROC analysis, a cut-off value of ≥3 points optimally predicted a requirement for insulin. This prediction model may be clinically useful to predict requirement for insulin treatment after further validation.