Exploring the optimal age for total knee arthroplasty to minimize risk of adverse outcomes: machine learning analysis of a statewide cohort

Abstract

BACKGROUND: Rates of total knee arthroplasty (TKA) in the United States have risen in patients of a wide age range. Although rates of postoperative TKA complications have decreased, they remain a significant concern. In this study, we aim to determine how the risk of adverse TKA outcomes changes dynamically with age and explore the optimal ages with the lowest risk for adverse outcomes. METHODS: This retrospective cohort study included patients who underwent elective primary TKA from 2012 to 2018 in the Pennsylvania Health Care Cost Containment Council Database. We trained (70% train:30% test) an explainable boosting machine (EBM), a modern generalized additive model, to predict risk for 90-day mortality, 90-day readmission, 1-year revision, and longer length of stay (LOS). This "glass box" model allowed us to measure and visualize feature importance using mean absolute scores and determine the role of age in the model. We then ran EBM models that allowed two-way interactions between age and patient-level covariates. RESULTS: In our cohort of 227,959 patients, 90-day readmission was observed in 7.5%, 90-day mortality in 0.2%, and 1-year revision in 0.8%. The median LOS was 2 days (IQR [2, 3]). Age was among the most important factors for predicting all outcomes, and these were nonlinear relationships. The risk for 90-day mortality increased substantially at 76.5 years, and for 90-day readmission and longer LOS at 73.5 years. Risk for 1-year revision was greater before 63.5 years. CONCLUSIONS: We determined that there is a nonlinear relationship between age and risk for adverse TKA outcomes, and it changes dramatically at specific time points. Our data suggests that the optimal age for lower risk of 90-day mortality, 90-day readmission, and longer LOS is below 73.5 years, and above 63.5 years for 1-year revision. These findings can help in decision-making when trying to quantify risks related to aging.

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