Department of Health Services Research, Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
Department of Internal Medicine, Division of Endocrinology and Metabolic Diseases, Maastricht University Medical Centre, Maastricht, The Netherlands.
Diabetes Obes Metab. 2018 Mar;20(3):681-688. doi: 10.1111/dom.13148. Epub 2017 Nov 24.
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.
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.
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.
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.
在初级保健中识别、预测和验证新诊断 2 型糖尿病患者的不同血糖轨迹，作为实现更以患者为中心的护理的第一步。
我们在两个队列中进行了回顾性研究，使用来自两个大型荷兰糖尿病患者登记处的初级保健实践中常规收集的个体患者数据。参与者包括 2006 年 1 月至 2014 年 12 月期间新诊断为 2 型糖尿病的成年患者（发展队列，n=10528；验证队列，n=3777）。潜在增长混合模型确定了不同的血糖 5 年轨迹。使用日常临床实践中易于获得的患者特征构建机器学习模型来预测轨迹。
确定了三种不同的血糖轨迹：（1）稳定、适当的血糖控制（76.5%的患者）；（2）血糖控制改善（21.3%的患者）；和（3）血糖控制恶化（2.2%的患者）。在验证队列中也可以看出类似的轨迹。体重指数和糖化血红蛋白和甘油三酯水平是轨迹成员的最重要预测因素。在验证队列中，基于发展队列训练的预测模型的受试者工作特征曲线下面积为 0.96，表明准确性很高。
开发的模型可以有效地解释 2 型糖尿病患者未来血糖反应的异质性。因此，它可以在临床实践中用作提供量身定制的糖尿病护理的快速简便工具。
