Prediction of time to insulin initiation in gestational diabetes mellitus: a secondary analysis of the EMERGE trial
Year: 2025
Type: article
Abstract: AIMS: Gestational diabetes mellitus (GDM) is a global health problem. Insulin therapy is recommended when lifestyle management fails to control blood glucose. We aim to predict the time to insulin initiation for women with GDM. METHODS: A random survival forest (RSF) model was developed to predict the time to insulin initiation among 413 women from the EMERGE trial, analysed separately for placebo and metformin groups. Maternal characteristics and early glucose data (collected during the two weeks after randomisation) were used as predictors. Decision curve analysis was performed to assess the net benefit of the model compared with default strategies. RESULTS: The RSF model had a concordance index (C-index) of 0.71 (95 % CI: 0.64-0.77), time-dependent AUC ≥0.70 and Brier score ≤0.2 for the placebo group, and a C-index of 0.72 (95 % CI: 0.64-0.80), time-dependent AUC ≥0.75 and Brier score ≤0.2 for the metformin group. The decision curve analysis showed the RSF provided a higher net benefit for both groups compared to default strategies across clinically relevant threshold probabilities. CONCLUSIONS: The RSF model effectively identified women at high risk of requiring insulin. The decision curve analysis could help clinicians to balance insulin initiation decisions. However, prospective validation is needed to confirm the generalisability of the model.
Authors: Yueyun Zhu, Alberto Alvarez-Iglesias, Aoife M. Egan, Andrew Smyth, Christine Newman, David Aguilar Macias, Declan Devane, Fidelma Dunne, Martin O’Donnell, Paula O’Shea, Paddy Gillespie, Andrew J. Simpkin
Language: English
Subfield: Obstetrics and Gynecology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,410
OpenAlex ID: W4417537490