Preoperative Prediction of Chronic Post-surgical Pain Risk After Total Knee Arthroplasty: Insights From a...

Background Chronic post-surgical pain (CPSP) is a common and distressing complication following total knee arthroplasty (TKA). Despite advancements, CPSP remains a significant challenge, necessitating the development of a predictive model to identify at-risk patients. Materials and methods This level III case-control study analyzed 869 patients who underwent TKA between 2019 and 2021 at a single center. Data on demographics, comorbidities, mental health conditions, pre-existing pain, opioid use, substance abuse, and surgical factors were collected. Univariate and multivariate logistic regression analyses were conducted to identify significant preoperative risk factors. Independent predictors from multivariate analysis were assigned weighted scores proportional to their odds ratios, which were then adjusted according to previous literature and investigator consensus to create a clinically usable tool. Receiver operating characteristic (ROC) curve analysis was performed, and cutoffs were identified to optimize sensitivity and specificity. Results Out of 869 patients who underwent TKA, 15.7% (n = 136) developed CPSP. Out of these 136 patients, 22% (29) were referred to specialist pain management clinics. Univariate analysis identified seven significant predictors: diabetes mellitus, coronary artery disease (CAD), heart failure, mental health conditions, pre-existing pain, and preoperative opioid use. The multivariate analysis excluded heart failure (p = 0.098). The strongest association was found with CAD (OR = 2.98). A scoring system was developed, and an ROC analysis yielded an area under the curve of 0.616 (95% CI: 0.562-0.671, p = 0.001). Conclusions Identifying preoperative risk factors and using this score as a predictive model, pending further prospective validation, can help stratify patients and enable targeted interventions, potentially minimizing the impact and financial burden of CPSP after TKA.