Patients suffering from chronic kidney disease (CKD) show a reduction in kidney functionality. Uncontrolled diabetes is among the causes of CKD. As patients with diabetes attend periodic visits, relevant amounts of data end up being available within EHR systems. This information could be exploited to extract insights into disease progression and provide clinicians with tools to better understand the expected disease course. In this work, we applied multi-trajectory group-based trajectory models (GBTM) to identify and characterize groups of patients with similar progression patterns of CKD and diabetes. Specifically, we studied a population of 7,000 patients with diabetes and an initial diagnosis of CKD stage III followed at diabetes outpatient clinics spread across the Veneto Region (Italy). GBTM analysis led to the identification of 6 unique groups of patients with differing CKD and diabetes progression trajectories. Our results suggest that multi-trajectory modeling via GBTM can shed light on the progression of CKD and its interaction with glycemic control, as well as provide clinicians with tools to preemptively identify patients expected to experience significant CKD worsening.
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| http://dx.doi.org/10.1109/EMBC53108.2024.10781792 | DOI Listing |
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