Data-driven long-term glycaemic control trajectories and their associated health and economic outcomes in Finnish patients with incident type 2 diabetes

Background Treatments should be customized to patients to improve patients' health outcomes and maximize the treatment benefits. We aimed to identify meaningful data-driven trajectories of incident type 2 diabetes patients with similarities in glycated haemoglobin (HbA1c) patterns since diagnosis and to examine their clinical and economic relevance. Materials and methods A cohort of 1540 patients diagnosed in 2011-2012 was retrieved from electronic health records covering primary and specialized healthcare in the North Karelia region, Finland. EHRs data were compiled with medication purchase data. Average HbA1c levels, use of medications, and incidence of micro- and macrovascular complications and deaths were measured annually for seven years since T2D diagnosis. Trajectories were identified applying latent class growth models. Differences in 4-year cumulative healthcare costs with 95% confidence intervals (CIs) were estimated with non-parametric bootstrapping. Results Four distinct trajectories of HbA1c development during 7 years after T2D diagnosis were extracted: patients with Stable, adequate (66.1%), Slowly deteriorating (24.3%), and Rapidly deteriorating glycaemic control (6.2%) as well as Late diagnosed patients (3.4%). During the same period, 2.2 (95% CI 1.9-2.6) deaths per 100 person-years occurred in the Stable, adequate trajectory increasing to 3.2 (2.4-4.0) in the Slowly deteriorating, 4.7 (3.1-6.9) in the Rapidly deteriorating and 5.2 (2.9-8.7) in the Late diagnosed trajectory. Similarly, 3.5 (95% CI 3.0-4.0) micro- and macrovascular complications per 100 person-years occurred in the Stable, adequate trajectory increasing to 5.1 (4.1-6.2) in the Slowly deteriorating, 5.5 (3.6-8.1) in the Rapidly deteriorating and 7.3 (4.3-11.8) in the Late diagnosed trajectory. Patients in the Stable, adequate trajectory had lower accumulated 4-year medication costs than other patients. Conclusions Data-driven patient trajectories have clinical and economic relevance and could be utilized as a step towards personalized medicine instead of the common one-fits-for-all treatment practices.

Automated summary

Customized treatments are crucial for improving health outcomes and maximizing treatment benefits for patients with diabetes. Identifying data-driven trajectories based on similarities in glycated haemoglobin patterns can provide insights into the clinical and economic relevance for different patient groups, enabling personalized medicine.