# LASSO regression-derived first-trimester (9–14+6 weeks) risk stratification model for gestational diabetes mellitus: development, validation, and open-access web tool in a retrospective Chinese cohort

## TL;DR

This study creates a simple and accurate model to predict gestational diabetes in early pregnancy using common clinical data and provides a free online tool for risk assessment.

## Contribution

A novel, resource-efficient GDM prediction model using first-trimester clinical indicators and an open-access web tool for risk stratification.

## Key findings

- The LASSO–1SE model achieved an AUC of 0.717 with high sensitivity and positive predictive value.
- The model demonstrated robust calibration and clinical utility across risk thresholds.
- An open-access web calculator was developed for practical clinical use.

## Abstract

Early identification of gestational diabetes mellitus (GDM) is critical for mitigating adverse maternal and neonatal outcomes. Existing prediction models face limitations in clinical utility due to inconsistent variable selection and reliance on impractical biomarkers. This study aimed to develop and validate a resource-efficient GDM prediction model using routinely available first-trimester clinical indicators and deploy it as an open-access web tool.

A retrospective cohort of 1818 pregnancies from a Shanghai tertiary hospital (2023) was randomly divided into training (70%) and validation (30%) sets. Three predictor screening strategies (traditional logistic regression, least absolute shrinkage and selection operator (LASSO) regression with 1SE rule, and LASSO–MIN rule) were compared. The model performance was assessed by the area under the receiver operating characteristic (ROC) curves (AUC), the calibration curve, the clinical decision curve (DCA) and the clinical impact curve (CIC). The optimal model was visualized as a nomogram and deployed as an open access web calculator.

The LASSO–1SE model achieved the best balance of accuracy and simplicity, with an AUC of 0.717 (95% CI 0.681–0.753), sensitivity 69.7%, specificity 64.9%, and high positive predictive value (PPV = 92.3%).The model showed robust calibration (Hosmer–Lemeshow P > 0.3) and clinical utility across risk thresholds in DCA and CIC. A nomogram and an open-access web calculator (https://wangxiao0922.shinyapps.io/20250309/) were developed for risk stratification.

This resource-efficient tool enables early GDM risk stratification using routine clinical variables, supporting timely intervention in diverse healthcare settings.