Prediction of People With Type 2 Diabetes Not Achieving HbA1c Target After Initiation of Fast-Acting Insulin Therapy: Using Machine Learning Framework on Clinical Trial Data
Authors' conclusion
The model was able to predict those who did not achieve HbA1c target with promising performance, potentially enabling early identification of people with type 2 diabetes who require additional support to reach glycemic control.
Study context
- Population
- People with T2D initiating fast-acting insulin in an RCT
- Participants
- 467 people
- Primary endpoint
- Prediction of HbA1c target (≤7%) nonachievers, assessed by AUROC
Methodological detailssecondary analysis of RCT data, logistic regression, forward feature selection, 20-repeated stratified 5-fold cross-validation
- Secondary analysis of RCT data
- Logistic regression
- Forward feature selection
- 20-repeated stratified 5-fold cross-validation
Primary result
As reported in the abstract
Model performance (AUROC) for predicting HbA1c target nonachievers
AUROC
0.745
95% CI = 0.734, 0.756
Did not achieve HbA1c target of ≤7%
98
21%
Original publication
Prediction of People With Type 2 Diabetes Not Achieving HbA1c Target After Initiation of Fast-Acting Insulin Therapy: Using Machine Learning Framework on Clinical Trial Data. J Diabetes Sci Technol. Published 20 September 2024. doi:10.
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