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

TL;DR

This study creates a predictive model to identify patients at high risk of chronic pain after knee replacement surgery.

Contribution

A novel nomogram model is developed and validated for predicting chronic postsurgical pain after total knee arthroplasty.

Findings

Six risk factors for chronic pain after knee surgery were identified, including preoperative anxiety and depression.

The nomogram model showed good predictive accuracy with AUC values of 0.761 in training and 0.806 in validation cohorts.

The model's calibration and decision curve analysis confirmed its clinical applicability for risk prediction.

Abstract

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.

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.

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.

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.

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