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 evaluated length-of-stay (LOS) differences. Results: A total of 73,606 patients were included. Six clusters were identified, ranging from 107 to 61,505 patients. Cluster 6, enriched for urinary tract infection and sepsis, had the highest risk of NRD (OR 7.83, p < 0.001) and the longest median LOS (9.0 days). Clusters 1–4 had shorter recoveries with median LOS of 2.0 days and narrow variability, while Cluster 5 showed intermediate outcomes. Kruskal–Wallis and post hoc testing confirmed significant differences across clusters (p < 0.001). Conclusions: Machine learning clustering of diabetic THA patients revealed six distinct groups with varied comorbidity profiles. Infection-driven clusters carried the highest risk for non-routine discharge and prolonged hospitalization. This approach provides a novel framework for risk stratification and may inform targeted perioperative management strategies.

Background: Down syndrome (DS) is a genetic disorder characterized by an extra copy of chromosome 21, often leading to intellectual disabilities, developmental delays, and an increased risk of various comorbidities, including thyroid dysfunction and mental health disorders. The relationship between thyroid dysfunction and mood disorders, particularly depression in DS populations, requires further investigation. Objective: This study aims to investigate the presence of a correlative relationship between hypothyroidism and depression in 178,840 individuals with DS, utilizing data from the National Inpatient Sample (NIS) to determine if those with comorbid hypothyroidism exhibit higher rates of depression compared to their counterparts without hypothyroidism. Methods: A retrospective analysis of the 2016–2019 NIS dataset was conducted, focusing on patients with DS, hypothyroidism, and depression diagnoses. The diagnoses were determined and labeled based on ICD-10 codes associated with NIS datapoints. Survey-weighted linear regression analyses were employed to assess the association between hypothyroidism and depression within the DS cohort, adjusting for demographic factors such as age, gender, and race. Results: This study found that individuals with DS exhibit a significantly higher prevalence of hypothyroidism (29.88%) compared to the general population (10.28%). Additionally, individuals with DS and comorbid hypothyroidism demonstrated a higher prevalence of depression (8.67%) compared to those without hypothyroidism (3.00%). These findings suggest a significant association between hypothyroidism and increased depression risk among individuals with DS. However, the overall prevalence of depression in DS (4.69%) remains substantially lower than in the general population (12.27%). Conclusions: This study highlights the importance of considering hypothyroidism as a potential contributor to depression in individuals with DS. Further research is needed to explore the underlying mechanisms of this association and potential screening and management strategies to address thyroid dysfunction and its potential psychiatric implications in DS.

Total hip arthroplasty (THA) is one of the most common and cost-effective orthopaedic procedures in the United States, yet disparities in access, complication rates, and discharge disposition persist across socioeconomic groups. Although racial disparities after THA are well described, the influence of socioeconomic status (SES)-particularly income and insurance type-on in-hospital outcomes during the index admission remains less clearly defined. Clarifying these relationships is essential for improving equity in joint arthroplasty care. A retrospective cohort study was conducted using a large national database to identify adults undergoing elective primary THA from 2016 to 2021. Approximately 2.28 million weighted THA cases were included in the analysis. The SES indicators included ZIP-code-based income quartiles and primary payer category (e.g., private, Medicare, Medicaid, self-pay, and other). Survey-weighted descriptive statistics characterized demographic and clinical patterns. Multivariable logistic regression, adjusted for age, sex, race, comorbidity burden, and admission type, evaluated associations between SES and prolonged length of stay (LOS), discharge disposition, in-hospital complications, and mortality. Routine home discharge declined from 38.6% in the lowest income quartile (Q1) to 33.6% in the highest (Q4), with Q1 patients demonstrating higher adjusted odds of home discharge (odds ratio (OR) 1.42, 95% confidence interval (CI) 1.33 to 1.51, P < 0.001). Medicare (OR 0.84, P < 0.001) and Medicaid (OR 0.89, P < 0.001) coverage were associated with reduced odds of home discharge compared with private insurance. Income quartile did not significantly predict perioperative complications; however, Medicaid (OR 1.36, P < 0.001) and self-pay (OR 1.50, P < 0.01) patients had increased risk. In-hospital mortality was rare (< 0.2%), with elevated odds in lower-income groups largely attributable to comorbidity severity. Lower SES and public insurance coverage were also associated with modest increases in LOS. Socioeconomic disadvantage influences inpatient outcomes following THA. Lower-income and publicly insured patients experience longer hospitalizations, higher complication risk, and distinct discharge patterns. Incorporating social and payer-based risk factors into perioperative planning and value-based payment models may improve equity in joint arthroplasty care.

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.