Identifying Risk Groups in 401,846 Osteoarthritis Patients Undergoing Total Hip Arthroplasty: A Machine Learning...

Background/Objective: Osteoarthritis (OA) is the most common indication for total hip arthroplasty (THA), yet postoperative utilization and discharge outcomes vary substantially due to heterogeneous comorbidity burdens. This study applied unsupervised machine learning clustering to identify distinct comorbidity profiles among OA patients undergoing THA and to evaluate their association with non-routine discharge (NRD) and length of stay (LOS). Methods: The 2015-2021 National Inpatient Sample was queried using ICD-10 CM/PCS codes to identify patients with OA undergoing THA. Forty-nine comorbidities, complications, and in-hospital clinical covariates were incorporated into a k-modes clustering analysis. The Davies-Bouldin and Calinski-Harabasz indices were used to determine the optimal number of clusters. Multivariable logistic regression assessed adjusted odds of NRD across clusters, and Kruskal-Wallis H testing evaluated differences in LOS. Results: A total of 401,846 patients were included, and five distinct clusters were identified, ranging from 777 to 331,755 patients. Clusters with higher prevalence of renal dysfunction, cardiovascular disease, anemia, and heart failure demonstrated significantly increased risk of NRD (adjusted odds ratios up to 3.01, p < 0.001) and prolonged hospitalization, with median LOS up to 4 days. Lower-risk clusters exhibited shorter hospitalizations with median LOS of 2 days and higher rates of routine discharge. Kruskal-Wallis testing confirmed significant LOS differences across all clusters (p < 0.001). Conclusions: Machine learning clustering of OA patients undergoing THA identified clinically distinct subgroups with graded differences in postoperative hospital utilization. Patients with greater comorbidity burden experienced disproportionately higher risk of NRD and prolonged LOS. This data-driven framework highlights heterogeneity within the OA population and may inform future strategies for perioperative risk stratification and resource planning.