A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes

Abstract

AimTo identify, predict and validate distinct glycaemic trajectories among patients with newly diagnosed type 2 diabetes treated in primary care, as a first step towards more effective patient‐centred care.MethodsWe conducted a retrospective study in two cohorts, using routinely collected individual patient data from primary care practices obtained from two large Dutch diabetes patient registries. Participants included adult patients newly diagnosed with type 2 diabetes between January 2006 and December 2014 (development cohort, n = 10 528; validation cohort, n = 3777). Latent growth mixture modelling identified distinct glycaemic 5‐year trajectories. Machine learning models were built to predict the trajectories using easily obtainable patient characteristics in daily clinical practice.ResultsThree different glycaemic trajectories were identified: (1) stable, adequate glycaemic control (76.5% of patients); (2) improved glycaemic control (21.3% of patients); and (3) deteriorated glycaemic control (2.2% of patients). Similar trajectories could be discerned in the validation cohort. Body mass index and glycated haemoglobin and triglyceride levels were the most important predictors of trajectory membership. The predictive model, trained on the development cohort, had a receiver‐operating characteristic area under the curve of 0.96 in the validation cohort, indicating excellent accuracy.ConclusionsThe developed model can effectively explain heterogeneity in future glycaemic response of patients with type 2 diabetes. It can therefore be used in clinical practice as a quick and easy tool to provide tailored diabetes care.

Modeling the Research Landscapes of Artificial Intelligence Applications in Diabetes (GAPRESEARCH)

“…This developed model can be used to estimate the patient's journey with diabetes and determine the follow-up period based on their risk score. Patients with higher risk scores should be followed up more closely compared to those who have lower risk scores to optimize their health outcomes, particularly in diabetes [5].…”

Predicting short‐ and long‐term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine‐learning algorithms

“…Prediction models based on such traditional regression analyses, however, have limited utility for clinical decision‐making because they do not personalize the prediction to the individual. Similarly, studies that identify clinical characteristics of patients that are associated with distinct HbA1c trajectories in patients with T2DM over the course of insulin treatment via unsupervised clustering algorithms cannot sufficiently guide clinical decision‐making . Such studies lead to inconsistent groupings of patients, suggesting that cluster analysis driven by outcome patterns is not helpful in predicting HbA1c responses using clinical variables at baseline.…”

“…Similarly, studies that identify clinical characteristics of patients that are associated with distinct HbA1c trajectories in patients with T2DM over the course of insulin treatment via unsupervised clustering algorithms cannot sufficiently guide clinical decision-making. [14][15][16][17][18] Such studies lead to inconsistent groupings of patients, suggesting that cluster analysis driven by outcome patterns is not helpful in predicting HbA1c responses using clinical variables at baseline. There is a need for a more individualized approach to support clinical decision-making and to guide personalized treatment for patients with T2DM in need of additional treatment.…”

Durability of glycaemic control in patients with type 2 diabetes after metformin failure: Prognostic model derivation and validation using the DISCOVER study

“…Along with early diagnosis, people with T2D now have a longer life expectancy compared with just a few decades ago, which emphasizes the need for a continued effort to ensure optimal glycaemic control to prevent diabetes‐related complications. By applying simulations and machine learning methods, trajectories of HbA1c have been predicted, but these models have been developed and validated using RCT data or country‐specific cohorts, thereby limiting their generalizability and application in a real‐world setting.…”

“…An increasing number of diagnostic and prognostic prediction models have been proposed in the area of diabetes medicine; most of them have focused on diabetes prevention and diagnosis or prediction of diabetes‐related complications . Few models have been developed for the prediction of glycaemic control in subjects with diabetes, and while some apply to people with type 1 diabetes (T1D), those for people with T2D focused on the trajectory of HbA1c predicted through simulation, or by using machine learning methods; these studies, however, used data from RCTs or a single‐country cohort, thus limiting the generalizability of the findings. Moreover, to our knowledge, no models have been developed to predict the durability of glycaemic control in people with T2D after metformin failure, thereby leaving the individual decision to the single healthcare professional.…”