Construction and validation of nomogram model for chronic postsurgical pain in patients after total knee arthroplasty: A retrospective study

Abstract

Objective: Chronic postsurgical pain (CPSP) after total knee arthroplasty (TKA) is the most common postoperative complication in orthopedics. This study aims to explore the risk factors for CPSP after TKA and construct a nomogram model.

Methods: This retrospective study included clinical records of 430 patients who received TKA treatment at Wuhan Fourth Hospital between January 2020 to January 2024. Patients were randomly divided into a training cohort (n=301) and a validation cohort (n=129) in a 7:3 ratios. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and logistic regression analysis were used to identify the independent risk factors, and a predictive nomogram model was established based on the identified risk factors. The concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis were used to assess the predictive accuracy and clinical application value of the nomogram model.

Results: Six risk factors for predicting CPSP were identified, including preoperative anxiety, preoperative depression, preoperative pain, duration of tourniquet use, pain upon discharge, and postoperative C-reactive protein levels. The nomogram model demonstrated sufficient predictive accuracy, with the area under the curve (AUC) values of 0.761 (95% CI: 0.689-0.833) and 0.806 (95% CI: 0.700-0.911) in the training cohort and validation cohort, respectively. The C-index of the training cohort and validation cohort were 0.733 and 0.761, respectively. The calibration curve shows good consistency between the predicted risk of the model and the actual risk of CPSP. Decision curve analysis (DCA) demonstrated the clinical applicability of the model.

Conclusions: The nomogram model established in this study for predicting CPSP after TKA has good predictive value and may be used in clinical practice to identify patients at high risk of developing CPSP after TKA.

Keywords: Chronic postsurgical pain; Nomogram model; Orthopedics; Risk factors; Total knee arthroplasty.

Fig.1

Participant screening process diagram.

Fig.2

LASSO coefficient curve of CPSP in TKA patients. A: Each curve in the graph represents the coefficient variation of each variable; The vertical axis represents the coefficient values, the lower horizontal axis represents log (λ), and the upper horizontal axis represents the number of non-zero coefficients in the model at each time point. B: 10-fold cross-validation fitting.

Fig.3

Nomogram of CPSP after TKA. Each level of the predictor variable represents a specific score. The total score is generated by summarizing the scores of each predictor variable. The total score corresponds to the CPSP probability.

Fig.4

ROC curve and AUC of the predictive model; ROC: receiver operating characteristic; AUC: area under the curve.

Fig.5

Calibration diagram of the prediction model. A. Calibration chart of the training cohort. B. Calibration chart in the internal validation cohort. The x-axis represents the predicted probability of CPSP. The y-axis represents the observed CPSP. The diagonal dashed line represents the perfect prediction of the ideal model. The solid line represents the performance of the nomogram. It indicates that solid lines are closer to diagonal dashed lines for better prediction, indicating that the model has good predictive ability.

Fig.6

DCA of the nomogram. DCA: decision curve analysis.