ArticleCureus2025
Preoperative Prediction of Chronic Post-surgical Pain Risk After Total Knee Arthroplasty: Insights From a Single-Center Case-Control Study.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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