Objectives: Patients with chronic kidney disease (CKD) are at higher risk of being admitted to the hospital than the general population. Hospitalizations in patients with CKD are associated with higher medical costs and increased morbidity and mortality. Identification of patients with CKD who are at greatest risk of hospitalization may hold promise to improve clinical outcomes and enable judicious allocation of health care resources.
Study design: Retrospective, observational cohort study.
Methods: Medicare Part A and Part B claims from calendar years 2017 and 2018 from 50,000 unique patients with a diagnosis of stage 3 to 5 CKD were used for this study. Data were split into training (n = 40,000) and test (n = 10,000) sets. A variety of model types were built to predict all-cause hospitalization within 90 days.
Results: The final model was a gradient-boosting machine with 399 input terms. The model demonstrated good ability to discriminate (area under the curve [AUC] for the receiver operating characteristic curve = 0.73), which was stable when tested in the test set (AUC = 0.73). The positive predictive value in the test set was 0.306, 0.240, and 0.216 at the 10%, 20%, and 30% thresholds, respectively. The sensitivity in the test set was 0.288, 0.453, and 0.609 at the 10%, 20%, and 30% thresholds, respectively.
Conclusions: We developed an algorithm that uses medical claims to identify Medicare patients with CKD stages 3 to 5 who are at highest risk of being hospitalized in the near term. This algorithm could be used as a decision support tool for clinical programs focusing on management of patient populations with CKD.