Lavikainen Piia, Chandra Gunjan, Siirtola Pekka, Tamminen Satu, Ihalapathirana Anusha T, Röning Juha, Laatikainen Tiina, Martikainen Janne
School of Pharmacy, University of Eastern Finland, Kuopio, Finland.
Biomimetics and Intelligent Systems Group, Faculty of ITEE, University of Oulu, Oulu, Finland.
Clin Epidemiol. 2023 Jan 5;15:13-29. doi: 10.2147/CLEP.S380828. eCollection 2023.
To gain an understanding of the heterogeneous group of type 2 diabetes (T2D) patients, we aimed to identify patients with the homogenous long-term HbA1c trajectories and to predict the trajectory membership for each patient using explainable machine learning methods and different clinical-, treatment-, and socio-economic-related predictors.
Electronic health records data covering primary and specialized healthcare on 9631 patients having T2D diagnosis were extracted from the North Karelia region, Finland. Six-year HbA1c trajectories were examined with growth mixture models. Linear discriminant analysis and neural networks were applied to predict the trajectory membership individually.
Three HbA1c trajectories were distinguished over six years: "stable, adequate" (86.5%), "improving, but inadequate" (7.3%), and "fluctuating, inadequate" (6.2%) glycemic control. Prior glucose levels, duration of T2D, use of insulin only, use of insulin together with some oral antidiabetic medications, and use of only metformin were the most important predictors for the long-term treatment balance. The prediction model had a balanced accuracy of 85% and a receiving operating characteristic area under the curve of 91%, indicating high performance. Moreover, the results based on SHAP (Shapley additive explanations) values show that it is possible to explain the outcomes of machine learning methods at the population and individual levels.
Heterogeneity in long-term glycemic control can be predicted with confidence by utilizing information from previous HbA1c levels, fasting plasma glucose, duration of T2D, and use of antidiabetic medications. In future, the expected development of HbA1c could be predicted based on the patient's unique risk factors offering a practical tool for clinicians to support treatment planning.
为了解2型糖尿病（T2D）患者的异质性群体，我们旨在识别具有同质长期糖化血红蛋白（HbA1c）轨迹的患者，并使用可解释的机器学习方法以及不同的临床、治疗和社会经济相关预测因素来预测每位患者的轨迹归属。
从芬兰北卡累利阿地区提取了9631例确诊为T2D患者的初级和专科医疗电子健康记录数据。采用生长混合模型检查了六年的HbA1c轨迹。分别应用线性判别分析和神经网络来预测轨迹归属。
在六年期间区分出三种HbA1c轨迹：“稳定且达标”（86.5%）、“改善但未达标”（7.3%）以及“波动且未达标”（6.2%）的血糖控制情况。既往血糖水平、T2D病程、仅使用胰岛素、胰岛素与某些口服降糖药联合使用以及仅使用二甲双胍是长期治疗平衡的最重要预测因素。该预测模型的平衡准确率为85%，曲线下面积为91%，表明性能良好。此外，基于SHAP（Shapley值相加解释）值的结果表明，在群体和个体层面都有可能解释机器学习方法的结果。
利用既往HbA1c水平、空腹血糖、T2D病程和降糖药使用情况等信息，可以可靠地预测长期血糖控制的异质性。未来，基于患者独特的风险因素可以预测HbA1c的预期变化，为临床医生提供一个支持治疗规划的实用工具。
