In Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists
OBJECTIVE : Using supervised machine learning algorithms (SMLAs), we built models to predict the probability of type 1 diabetes mellitus (T1DM) patients on insulin pump therapy for meeting insulin pump self-management behavioral (IPSMB) criteria and achieving good glycemic response within six months.
METHODS : This was a single-center retrospective chart review of 100 adult T1DM patients on insulin pump therapy (>6 months). Three SMLAs were deployed: multivariable logistic regression (LR), random forest (RF), and K-nearest neighbor (k-NN); validated using repeated three-fold cross-validation. Performance metrics included AUC-ROC for discrimination and Brier scores for calibration.
RESULTS : Variables predictive of adherence with IPSMB criteria were baseline HbA1c, continuous glucose monitoring (CGM), and sex. The models had comparable discriminatory power (LR=0.74; RF=0.74; k-NN=0.72), with the random forest model showing better calibration (Brier=0.151). Predictors of the good glycemic response included baseline HbA1c, entering carbohydrates, and following the recommended bolus dose, with models comparable in discriminatory power (LR=0.81, RF=0.80, k-NN=0.78) but the random forest model being better calibrated (Brier=0.099).
CONCLUSION : These proof-of-concept analyses demonstrate the feasibility of using SMLAs to develop clinically relevant predictive models of adherence with IPSMB criteria and glycemic control within six months. Subject to further study, non-linear prediction models may perform better.
Kurdi Sawsan, Alamer Ahmad, Wali Haytham, Badr Aisha F, Pendergrass Merri L, Ahmed Nehad, Abraham Ivo, Fazel Maryam T
2023-Mar-08
Diabetes type 1, behaviors, insulin pump, machine learning, prediction models, self-care
