Construction,Internal Validation and Comparison of Satisfaction Prediction Models for Patient in the Early Stage After Primary TKA
Objective To construct a predictive model of satisfaction in the early stage after primary total knee arthroplasty(TKA)and conduct internal validation.In addition,to compare the effectiveness of Van Onsem,Kunze model and this prediction model.Methods A total of 277 patients with knee osteoarthritis who underwent primary TKA in the Joint Surgery Department of Tianjin Hospital from January 2021 to June 2021 were prospectively collected.The preoperative information and KSS satisfaction score at 6 months after operation were collected.KSS satisfaction at 6 months after TKA was used as the outcome index,and preoperative factors of TKA were used as predictors.Univariate regression analysis and Lasso regression were used to screen variables,and multivariate logistic regression was used to construct a prediction model of early satisfaction after TKA.The AUC,C index,Calibration curve and DCA curve of the prediction model were calculated.The Kunze and Van Onsem models were externally validated using the data set of our center,and compared with the model constructed in this paper.Results Of the 277 patients,232 patients(83.75％)were satisfied and 45 patients(16.25％)were dissatisfied at 6 months after operation.Based on WOMAC score,KSS function score,age,BMI,GAD-7,OARSI lateral joint space classification and OARSI medial femoral osteophyte classification,a satisfaction prediction model after TKA was constructed,and the AUC of the prediction model was 0.77,the corrected AUC was 0.83(95％CI:0.763-0.833),the AUC of the Kunze model was 0.61(95％CI:0.52-0.70),and the AUC of the Van Onsem model was 0.71(95CI:0.63-0.79).Conclusion The prediction model of early satisfaction after TKA constructed in this study has good accuracy and discrimination in the northern Chinese population,and has high clinical practical value.In addition,Van Onsem and Kunze's prediction model does not apply to this study group.
Total knee arthroplastySatisfactionPrediction modelClinical decision tools
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