Early prediction of insulin requirement in gestational diabetes using a parsimonious LASSO model and a point-based risk score

Aims: This study developed and validated a practical prediction model to forecast insulin requirements in pregnant women with gestational diabetes mellitus (GDM). Methods: A retrospective single-center cohort of 490 consecutive women with GDM was analyzed. The primary outcome was insulin therapy initiation based on routine clinical decisions. Candidate predictors were collected at the first visit. LASSO logistic regression with 10-fold cross-validation was used for model development, excluding underweight women (BMI < 18.5 kg/m2) and incomplete cases. Coefficients were converted into a point score (scaling factor 0.25). Discrimination was assessed using the area under the receiver operating characteristic curve (AUC), and likelihood ratios defined clinically actionable categories. Results: Insulin therapy was initiated in 11.0% of women. The derivation sample included 403 women (43 receiving insulin). Five predictors were selected: BMI category, ethnic risk background, chronic hypertension, medically assisted reproduction, and number of abnormal oral glucose tolerance test values. The model showed good discrimination (AUC 0.7755, 95% CI 0.695–0.853), excellent calibration (slope 1.02), and low Brier score (0.082). Scores ≥ 14 identified high-risk patients (LR + 11.7). Conclusions: This LASSO-derived score using readily available variables accurately predicts insulin need in GDM, potentially facilitating risk-stratified management in community settings.