Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes

• Published in: Clinical Epidemiology
• Main Authors: Lavikainen,Piia, Chandra,Gunjan, Siirtola,Pekka, Tamminen,Satu, Ihalapathirana,Anusha T, Röning,Juha, Laatikainen,Tiina, Martikainen,Janne
• Format: Article in Journal/Newspaper
• Language: English
• Published: Dove Press 2023
• Subjects: Clinical Epidemiology; Pekka; Siirtola; karelia*
• Online Access: https://www.dovepress.com/data-driven-identification-of-long-term-glycemia-clusters-and-their-in-peer-reviewed-fulltext-article-CLEP

Piia Lavikainen,1,* Gunjan Chandra,2,* Pekka Siirtola,2 Satu Tamminen,2 Anusha T Ihalapathirana,2 Juha Röning,2 Tiina Laatikainen,3– 5 Janne Martikainen1 1School of Pharmacy, University of Eastern Finland, Kuopio, Finland; 2Biomimetics and Intelligent Systems Group, Faculty of ITEE, University of Oulu, Oulu, Finland; 3Joint Municipal Authority for North Karelia Social and Health Services (Siun Sote), Joensuu, Finland; 4Department of Public Health and Social Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland; 5Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland*These authors contributed equally to this workCorrespondence: Piia Lavikainen, School of Pharmacy C/O Faculty of Health Sciences, University of Eastern Finland, P.O. Box 1627, Kuopio, FI-70211, Finland, Tel [phone], Email [e-mail]: 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.Patients and Methods: 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.Results: 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 ...