Prediction of time to insulin initiation in gestational diabetes mellitus: a secondary analysis of the EMERGE trial
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.
How strong is the evidence?
Evidence Hub reads this as a secondary / pooled analysis, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 413 participants or records.
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