Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data

Abstract

Introduction Outcomes in type 2 diabetes mellitus (T2DM) could be optimized by identifying which treatments are likely to produce the greatest improvements in glycemic control for each patient. Objectives We aimed to identify patient characteristics associated with achieving and maintaining a target glycated hemoglobin (HbA1c) of ≤ 7% using machine learning methodology to analyze clinical trial data on combination therapy for T2DM. By applying a new machine learning methodology to an existing clinical dataset, the practical application of this approach was evaluated and the potential utility of this new approach to clinical decision making was assessed. Methods Data were pooled from two phase III, randomized, double-blind, parallel-group studies of empagliflozin/linagliptin single-pill combination therapy versus each monotherapy in patients who were treatment-naïve or receiving background metformin. Descriptive analysis was used to assess univariate associations between HbA1c target categories and each baseline characteristic. After the descriptive analysis results, a machine learning analysis was performed (classification tree and random forest methods) to estimate and predict target categories based on patient characteristics at baseline, without a priori selection. Results In the descriptive analysis, lower mean baseline HbA1c and fasting plasma glucose (FPG) were both associated with achieving and maintaining the HbA1c target. The machine learning analysis also identified HbA1c and FPG as the strongest predictors of attaining glycemic control. In contrast, covariates including body weight, waist circumference, blood pressure, or other variables did not contribute to the outcome. Conclusions Using both traditional and novel data analysis methodologies, this study identified baseline glycemic status as the strongest predictor of target glycemic control attainment. Machine learning algorithms provide an hypothesis-free, unbiased methodology, which can greatly enhance the search for predictors of therapeutic success in T2DM. The approach used in the present analysis provides an example of how a machine learning algorithm can be applied to a clinical dataset and used to develop predictions that can facilitate clinical decision making. Enhanced Digital Features To view enhanced digital features for this article go to https ://doi.org/10.6084/m9.figsh are.79994 78.

Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review

“…The output could be nominal (regression) or categorical (classification). Key among several advantages of the algorithm, as highlighted by Del Parigi et al [56],…”

“…Del Parigi et al [56] showed that ML (Random Forest) could be used to determine the predictors of the highest improvement in glycemic control for an individual patient from the data pooled from randomized controlled trials. In addition, Fujihara et al [81] have shown that ML (Neural Networks) can aid clinicians to decide, with high prediction accuracy, when to initiate an insulin regimen for their patients with Type 2 diabetes.…”

Predicting three-month fasting blood glucose and glycated hemoglobin of patients with type 2 diabetes based on multiple machine learning algorithms

“…From the feature screening results, this study is consistent with other studies, FBG and HbAlc are both the most important predictors. Del Parigi A et al [20] used several machine learning algorithms to nd predictors of glycemic control in diabetes and found that HbA1c and FPG were the strongest predictors of achieving glycemic control. This is consistent with our ndings.…”

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

“…To evaluate the treatment results of T2DM and to recognize the characteristics of patients associated with the achievement of a target HbA1c of ≤ 7%, several ML methods were applied [ 169 ]. The data was the effect of a clinical trial assessing the single-pill combination of the sodium-glucose cotransporter-2/dipeptidyl peptidase-4 (SGLT2/DPP-4) inhibitor empagliflozin/linagliptin with linagliptin or empagliflozin monotherapies to identify novel predictors of remedy success, defined as HbA1c decrease.…”