Evaluating prediction of short-term tolerability of five type 2 diabetes drug classes using routine clinical features: UK population-based study
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Introduction
A precision medicine approach in type 2 diabetes (T2D) needs to consider potential treatment risks alongside established benefits for glycaemic and cardiometabolic outcomes. Considering five major T2D drug classes, we aimed to describe variation in short-term discontinuation (a proxy of overall tolerability) by drug and patient routine clinical features and determine whether combining features in a model to predict drug class-specific tolerability has clinical utility.
Methods
UK routine clinical data (Clinical Practice Research Datalink, 2014–2020) of people with T2D initiating glucagon-like peptide-1 receptor agonists (GLP-1RA); dipeptidyl peptidase-4 inhibitors (DPP4i); sodium-glucose co-transporter-2 inhibitors (SGLT2i); thiazolidinediones (TZD) and sulfonylureas (SU) in primary care were studied. We first described the proportions of short-term (3-month) discontinuation by drug class across subgroups stratified by routine clinical features. We then assessed the performance of combining features to predict discontinuation by drug class using a flexible machine learning algorithm (Bayesian Additive Regression Trees).
Results
Amongst 182,194 treatment initiations, discontinuation varied modestly by clinical features. Higher discontinuation on SGLT2i and GLP-1RA was seen for older patients and those with longer diabetes duration. For most other features, discontinuation differences were similar by drug class, with higher discontinuation for patients who had previously discontinued metformin, females, and people of South-Asian and Black ethnicities. Lower discontinuation was seen for patients currently taking statins and blood pressure medication. The model combining all sociodemographic and clinical features had a low ability to predict discontinuation (AUC = 0.61).
Conclusion
A model-based approach to predict drug-specific discontinuation for individual patients with T2D has low clinical utility. Instead of likely tolerability, prescribing decisions in T2D should focus on drug-specific side-effect risks and differences in the glycaemic and cardiometabolic benefits of available medication classes.
Key message
Routine clinical features are not sufficient to predict individuals likely to discontinue (maybe say tolerate due to the proxy stuff) T2D glucose treatment.
Why did we undertake this study?
Precision medicine studies aiming to guide the choice of type 2 diabetes treatment have mainly evaluated treatment benefits, meaning there is little evidence to inform targeting based on potential treatment risks.
What is the specific question(s) we wanted to answer?
Can routine clinical features be used to predict individual patients likely to discontinue therapy short-term (a proxy of overall drug tolerability), considering five major type 2 diabetes glucose-lowering drug classes.
What did we find?
Routine clinical features are associated with differences in short-term tolerability of the five drug classes, but a model combining features to predict likely tolerability has low predictive utility.
What are the implications of our findings?
Overall, type 2 diabetes drug tolerability cannot be accurately predicted for individual patients using routine clinical features. Prescribing decisions in type 2 diabetes should focus on drug-specific side-effect risks and differences in glycaemic and cardiometabolic benefits.
