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

• Published in: Diabetes, obesity & metabolism Vol. 20; no. 3; pp. 681 - 688
• Main Authors: Hertroijs, Dorijn F. L., Elissen, Arianne M. J., Brouwers, Martijn C. G. J., Schaper, Nicolaas C., Köhler, Sebastian, Popa, Mirela C., Asteriadis, Stylianos, Hendriks, Steven H., Bilo, Henk J., Ruwaard, Dirk
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.03.2018; Wiley Subscription Services, Inc
• Subjects: Blood Glucose - metabolism; Body Mass Index; Clinical medicine; Cohort analysis; cohort study; database research; Diabetes; Diabetes mellitus; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - blood; Diabetes Mellitus, Type 2 - prevention & control; Female; glycaemic control; Glycated Hemoglobin A - metabolism; Hemoglobin; Humans; Hypoglycemic Agents - therapeutic use; Learning algorithms; Male; Middle Aged; Netherlands; Original; Patient-centered care; Patients; Primary care; Retrospective Studies; Risk Assessment - methods; Treatment Outcome; Triglycerides; Triglycerides - metabolism; type 2; primary care; glycaemic control; cohort study; type 2; diabetes; database research
• ISSN: 1462-8902; 1463-1326
• DOI: 10.1111/dom.13148

Aim To 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. Methods We 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. Results Three 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. Conclusions The 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.