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

Autor: Hertroijs DFL; Department of Health Services Research, Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands., Elissen AMJ; Department of Health Services Research, Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands., Brouwers MCGJ; Department of Internal Medicine, Division of Endocrinology and Metabolic Diseases, Maastricht University Medical Centre, Maastricht, The Netherlands., Schaper NC; Department of Internal Medicine, Division of Endocrinology and Metabolic Diseases, Maastricht University Medical Centre, Maastricht, The Netherlands., Köhler S; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands., Popa MC; Department of Data Science and Knowledge Engineering, Faculty of Humanities and Sciences, Maastricht University, Maastricht, The Netherlands., Asteriadis S; Department of Data Science and Knowledge Engineering, Faculty of Humanities and Sciences, Maastricht University, Maastricht, The Netherlands., Hendriks SH; Diabetes Centre, Isala, Zwolle, The Netherlands., Bilo HJ; Diabetes Centre, Isala, Zwolle, The Netherlands.; Department of Internal Medicine, University Medical Centre Groningen and University of Groningen, Groningen, The Netherlands., Ruwaard D; Department of Health Services Research, Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
Jazyk: angličtina
Zdroj: Diabetes, obesity & metabolism [Diabetes Obes Metab] 2018 Mar; Vol. 20 (3), pp. 681-688. Date of Electronic Publication: 2017 Nov 24.
DOI: 10.1111/dom.13148
Abstrakt: 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.
(© 2017 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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