Poor glycemic control predictions from AI
Poor glycemic control in patients with type 2 diabetes can be predicted from patient information systems using machine learning.
The risk of poor glycemic control in patients with type 2 diabetes can be predicted with certainty using machine learning methods, a new study from Finland shows. The main factors predicting glycemic control include previous glucose levels, the duration of type 2 diabetes and the patient’s existing antidiabetic drugs.
The researchers examined glycemic control in patients with type 2 diabetes in North Karelia, Finland, over a period of six years. The patients’ glycemic control was determined on the basis of long-term blood sugar, HbA1c. Three HbA1c trajectories were identified from the data, and based on these, patients were divided into two groups: patients with adequate glycemic control and patients with inadequate glycemic control. Using machine learning methods, the researchers examined the association of patients’ baseline characteristics, clinical and treatment-related factors, and socioeconomic status with glycemic control. The baseline characteristics included more than 200 different variables.
The results showed that by using data on the duration of type 2 diabetes, before HbA1c levels, fasting blood glucose, existing antidiabetic drugs and their number, it is possible to reliably identify patients at persistent risk of hyperglycaemia at any stage of their disease. In other words, inadequate glycemic control can be predicted from data routinely collected as part of diabetes monitoring and management.
The primary goal of treating type 2 diabetes is to maintain good glycemic control to prevent complications associated with the disease. According to the Finnish Current Care Guidelines for Diabetes, glycemic control should be followed up annually, which makes it possible to monitor the long-term course of the disease. Early identification of patients with poor glycemic control is of paramount importance to target treatment to those in need and intensify it at the right time. Delayed intensification of treatment increases the risk of complications, which is also reflected in higher costs of care.
The study used data from the electronic patient information system of the joint municipal authority for social and health services of North Karelia, Siun sote, from registers kept by the Finnish Social Insurance Institution, as well as from Statistics Finland’s open postcode database, Paavo. A total of 9,631 people with type 2 diabetes were selected for the study cohorts. The study was carried out in collaboration between the University of Eastern Finland and the University of Oulu, and it was funded by the Finnish Diabetes Association, the Strategic Research Council of the Academy of Finland, Kuopio University Hospital (VTR funding) and the HTx project funded by the EU’s Horizon 2020 program (https://www.htx-h2020.eu/).
Lavikainen P, Chandra G, Siirtola P, Tamminen S, Ihalapathirana A, Röning J, Laatikainen T, Martikainen J. Data-driven identification of long-term glycemia clusters and their individualized predictors in Finnish patients with type 2 diabetes. Clin Epidemiol. 2022. 10.2147/CLEP.S380828