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.