Jhase Sniderman1, Roland B Stark2, Carolyn E Schwartz3, Hajra Imam4, Joel A Finkelstein5, Markku T Nousiainen6. 1. Division of Orthopaedic Surgery, University of Toronto, Toronto, Ontario, Canada. 2. DeltaQuest Foundation, Inc, Concord, MA. 3. DeltaQuest Foundation, Inc, Concord, MA; Departments of Medicine and Orthopaedic Surgery, Tufts University School of Medicine, Boston, MA. 4. Division of Orthopaedic Surgery, Sunnybrook Holland Orthopaedic and Arthritic Center, Toronto, Ontario, Canada. 5. Division of Orthopaedic Surgery, University of Toronto, Toronto, Ontario, Canada; Division of Orthopedic Surgery, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; Division of Spine Surgery, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada. 6. Division of Orthopaedic Surgery, University of Toronto, Toronto, Ontario, Canada; Division of Orthopedic Surgery, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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. LEVEL OF EVIDENCE: Prognostic Level 1.
BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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. LEVEL OF EVIDENCE: Prognostic Level 1.
Authors: Katrin B Johannesdottir; Henrik Kehlet; Pelle B Petersen; Eske K Aasvang; Helge B D Sørensen; Christoffer C Jørgensen Journal: Acta Orthop Date: 2022-01-03 Impact factor: 3.717