Identifying Risk Groups in 73,000 Patients with Diabetes Receiving Total Hip Replacement: A Machine Learning Clustering Analysis.

ABSTRACT: Background/Objective: Diabetes mellitus (DM) is a highly prevalent condition that contributes to adverse outcomes in patients undergoing total hip arthroplasty (THA). This study applied machine learning clustering algorithms to identify comorbidity profiles among diabetic THA patients and evaluate their association with postoperative outcomes. Methods: The 2015-2021 National Inpatient Sample was queried using ICD-10 CM/PCS codes to identify DM patients undergoing THA. Forty-nine comorbidities, complications, and clinical covariates were incorporated into clustering analysis. The Davies-Bouldin and Calinski-Harabasz indices determined the optimal number of clusters. Multivariate logistic regression assessed risk of non-routine discharge (NRD), and Kruskal-Wallis H testing eval

SUBMITTER: Ahmadi A 

PROVIDER: S-EPMC12653386 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature