Assessing the Validity of a New Prediction Model for Patient Satisfaction After Total Knee Arthroplasty: A Retrospective Cross-Sectional Study.

Abstract

PurposePreviously, a simplified model using statistically selected questionnaires from various patients reported outcome measures (PROMs) was proposed to predict patient satisfaction after total knee arthroplasty (TKA). However, this simple and useful model needs to be validated across ethnic and cultural differences. The objective of this study was to evaluate the utility of this predictive model in Japanese patients.Patients and MethodsOf all knees treated using primary TKA at our institution between August 2017 and June 2018, this study involved 50 knees of 48 patients (11 men, 37 women) to whom the predictive model was applied preoperatively and from whom PROMs were obtained at least 1 year postoperatively. To evaluate PROMs, patients completed the KSS and the Forgotten Joint Score-12. Correlations were analyzed between preoperatively predicted postoperative patient satisfaction and actual postoperative patient satisfaction, as well as each PROM.ResultsKSS satisfaction improved from 15.6 ± 6.1 preoperatively to 27.8 ± 8.3 postoperatively, with satisfaction reported for 41 knees (82%). The preoperatively predicted postoperative patient satisfaction score was 26.3 ± 4.6, with no significant correlation with actual postoperative score (r = 0.05, p = 0.72). The difference between preoperatively predicted patient satisfaction and actual postoperative patient satisfaction was positively correlated with the score for question 9 of the Pain Catastrophizing Scale, among other instruments constituting the predictive model.ConclusionOur data suggest that the predictive model had a low predictive value and that it had limited applicability to Japanese patients. The results also suggest that a tendency toward catastrophic thinking is associated with discrepancy between preoperatively predicted postoperative patient satisfaction and actual postoperative patient satisfaction. The predictive model has low utility and needs some modification.