A Machine Learning Approach to Determine the Optimal Age for Total Hip Arthroplasty: When Is Risk for Adverse Outcomes Lowest?

INTRODUCTION: With evolving lifestyles and improvements in surgical techniques, the utilization of total hip arthroplasty (THA) is growing across patients of all ages. Although complication rates are low, they are still a key consideration in THA planning. In this study, we explore the optimal ages for THA with the lowest risk of adverse outcomes. METHODS: Patients who underwent primary elective THA were retrospectively identified from the Pennsylvania Health Care Cost Containment Council Database, 2012 to 2018. We trained a supervised model learning model-explainable boosting machines-with a 70% train to 30% test split to predict risk for 90-day readmission, 90-day mortality, 1-year revision, and longer length of stay (LOS). Using this "glass box" model, we assessed risk for these adverse outcomes as they change with age. We then visualized two-way interactions between age and other patient-level covariates in the models to further understand risks. RESULTS: Our cohort included 105,336 patients with rates of 8.0% readmission, 0.3% mortality, and 1.5% revision. The median LOS was 2 days (interquartile range [1, 3]). We demonstrated a nonlinear relationship between age and THA outcomes, and identified age as one of the most important factors in prediction of adverse outcome. Risk for 90-day readmission increased between 48.5 and 71.5 years, and 90-day mortality risk increased after 79.5 years. Risk for 1-year revision increased before 46.5 years, and risk for longer LOS decreased between 52.5 and 74.5 years. Multiple factors influenced the relationship between age and THA outcomes, such as discharge location and comorbidities. CONCLUSIONS: Risk for adverse THA outcomes changes in a nonlinear manner with age. Our models suggest that the optimal age for lowest risk of adverse outcomes after THA is between 52.5 and 71.5 years. This study may provide nuanced risk quantification when considering age in THA, and inform planning and election of THA.