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

Dorijn F. L. Hertroijs*, Arianne M. J. Elissen, Martijn C. G. J. Brouwers, Nicolaas C. Schaper, Sebastian Kohler, Mirela C. Popa, Stylianos Asteriadis, Steven H. Hendriks, Henk J. Bilo, Dirk Ruwaard

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    20 Citations (Scopus)
    237 Downloads (Pure)

    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=10528; 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.

    Original languageEnglish
    Pages (from-to)681-688
    Number of pages8
    JournalDiabetes obesity & metabolism
    Volume20
    Issue number3
    DOIs
    Publication statusPublished - Mar-2018

    Keywords

    • cohort study
    • database research
    • diabetes
    • glycaemic control
    • primary care
    • type 2
    • DISEASE MANAGEMENT
    • ALL-CAUSE
    • CARE
    • MIXTURE
    • PATIENT
    • TRAJECTORIES
    • MORTALITY
    • MEDICINE
    • MELLITUS
    • OUTCOMES

    Cite this