Despite the success of total hip arthroplasty (THA), approximately 10%-15% of patients will be dissatisfied with their outcome. Identifying patients at risk of not achieving meaningful gains postoperatively is critical to pre-surgical counseling and clinical decision support. Machine learning has shown promise in creating predictive models. This study used a machine-learning model to identify patient-specific variables that predict the postoperative functional outcome in THA.
A prospective longitudinal cohort of 160 consecutive patients undergoing total hip replacement for the treatment of degenerative arthritis completed self-reported measures preoperatively and at 3 months postoperatively. Using four types of independent variables (patient demographics, patient-reported health, cognitive appraisal processes and surgical approach), a machine-learning model utilizing Least Absolute Shrinkage Selection Operator (LASSO) was constructed to predict postoperative Hip Disability and Osteoarthritis Outcome Score (HOOS) at 3 months.
The most predictive independent variables of postoperative HOOS were cognitive appraisal processes. Variables that predicted a worse HOOS consisted of frequent thoughts of work (β = -0.34), frequent comparison to healthier peers (β = -0.26), increased body mass index (β = -0.17), increased medical comorbidities (β = -0.19), and the anterior surgical approach (β = -0.15). Variables that predicted a better HOOS consisted of employment at the time of surgery (β = 0.17), and thoughts related to family interaction (β = 0.12), trying not to complain (β = 0.13), and helping others (β = 0.22).
Prognostic Level 1.
This clinical prediction model in THA revealed that the factors most predictive of outcome were cognitive appraisal processes, demonstrating their importance to outcome-based research.