Risk of Progression and Costs of Care for Patients with Type 2 Diabetes and Chronic Kidney Disease

Chronic kidney disease (CKD) progression is associated with a significant incremental economic burden. Previous work has demonstrated high accuracy of the laboratory-based machine learning model, Klinrisk, in predicting the risk of CKD progression. We sought to use the Klinrisk model to evaluate the association of risk of CKD progression with healthcare resource utilization (HRU) and costs of care in adults with type 2 diabetes and CKD.

This retrospective observational study included 413,177 eligible patients from Optum's electronic health records database (1/1/2007-9/30/2022). Patients were classified into low-, medium-, and high-risk groups based on their 2-year risk of CKD progression as predicted by the Klinrisk model. All-cause HRU and medical costs during the 1 year after CKD were estimated for each group.

Of the 413,177 patients included, 110,399 (26.7%) were classified as low-risk of CKD progression, 253,188 (61.3%) as medium-risk, and 49,590 (12.0%) as high-risk. The observed risk of CKD progression at 2 years, 5 years, and 10 years was 18.6%, 36.5%, and 54.1% for high-risk patients, 3.7%, 11.7%, and 26.4% for medium-risk patients, and 1.5%, 5.7%, and 15.8% for low-risk patients, which were similar to the predicted risks of CKD progression. High-risk patients had higher HRU and more than 2-3 times higher costs than lower-risk patients. Inpatient costs were the major cost driver for high-risk patients.

The Klinrisk model accurately identified patients with type 2 diabetes and CKD requiring the most healthcare resources. Such tools can support the identification and targeting of high-risk patients for interventions that may lead to a more cost-effective model of care.