Construction and validation of a predictive model for acute pain after total knee arthroplasty

The aim was to investigate the independent risk factors for acute pain after total knee arthroplasty, and to build a nomogram prediction model accordingly. Data were collected from total knee replacement patients in our hospital from June 2022 to December 2023, and independent risk factors for acute pain after total knee replacement were identified using univariate and multivariate logistic regression analyses, and the corresponding nomograms were established. The performance of the model was evaluated by plotting the working characteristic curves of the subjects and calculating the area under the curve, and the model performance was evaluated by using calibration curves and decision curve analyses in order to further enhance the reliability of the validation results. To further improve the reliability of the validation results, internal validation was performed using Bootstrap with 10-fold cross-validation rows, and the clinical utility of the model was assessed using calibration curve and decision curve analysis. A total of 486 total knee replacement patients were enrolled in the study, and 149 patients with acute pain after total knee replacement, with an incidence rate of 30.66%. After univariate and multivariate logistic regression analyses, a total of 5 variables were identified as independent risk factors for acute pain after total knee arthroplasty: body mass index > 24 kg/m2 (odds ratio [OR]: 1.930; 95% confidence interval [CI]: 1.032-3.917), diabetes (OR: 3.256; 95% CI: 1.106-7.961), placement of drainage tube (OR: 5.327; 95% CI: 1.236-10.237), operative time >2 hours (OR: 4.378; 95% CI: 1.237-9.372), and moderate-to-severe pain fear (OR: 7.665; 95% CI: 1.155-13.442). The nomogram constructed in this study for acute pain after total knee arthroplasty has good predictive accuracy and helps physicians to intervene in advance for patients at high risk of acute pain after total knee arthroplasty.