Evaluating the Impact of a Preoperative Risk Management Program on Outcomes Following Total Joint Arthroplasty: A Retrospective Cohort Study

Background: Total joint arthroplasty is an effective intervention for end-stage joint disease, but carries elevated risks for patients with comorbidities and those from historically marginalized populations. Preoperative risk management programs are designed to mitigate these risks by optimizing patient health prior to surgery. This study evaluated the impact of such a program on postoperative outcomes at a single academic institution, with attention to racial and ethnic minorities and patients with public insurance. Methods: We conducted a retrospective cohort study of 2748 patients who underwent total hip or knee arthroplasty between 2019 and 2021 at a single academic institution. Of these, 1548 patients received preoperative optimization targeting modifiable risk factors such as diabetes, obesity, and anemia, while 1200 followed standard preoperative protocols. Outcomes assessed included length of stay, prosthetic joint infection, 30- and 90-day readmissions, and emergency department (ED) visits. Patients were stratified by race/ethnicity, insurance type, and American Society of Anesthesiology physical classification. Results: = .01). There were no significant differences between groups in length of stay, readmissions, or overall ED visit rates. Black patients experienced higher ED utilization within 90 days postoperatively, regardless of optimization status. Medicaid patients with severe systemic disease had the highest rates of prosthetic joint infection (3.8%), and optimization was not associated with improved outcomes in this group. Patients with American Society of Anesthesiology physical classification ≥3 had increased ED visits postoperatively despite optimization. Conclusions: Preoperative risk management did not consistently improve outcomes, particularly among patients with greater comorbidity burdens or those facing socioeconomic disadvantage. These findings support the need for tailored optimization strategies that address both clinical risk and social determinants of health.