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Abstract
<jats:p>Background Chronic postsurgical pain (CPSP) affects between 7-23% and 13-44% of patients after hip and knee arthroplasty, respectively. Standardised methods of pain assessment provide superior evaluation of pain, including the Oxford Joint Score Pain Subscale (OJS-PS). We aim to estimate the proportion of patients with a phenotype consistent with CPSP through a k-medoids clustering technique and identify a threshold on the OJS-PS to highlight such patients at a population level. Methods In this cross-sectional study Patient Reported Outcomes Measures data 6-months after hip and knee arthroplasty from 2017 to 2025 were examined. An adapted k-medoid clustering technique utilising subsampling, batch assignment and probabilistic consensus allocated clusters. A receiver operator characteristic analysis identified a threshold on the OJS-PS noting the lowest scoring cluster. Our categorisation was compared to self-reported severe or moderate pain; sensitivity, specificity and accuracy of this categorisation were calculated. Results We analysed 109,542 hip and 113,799 knee arthroplasty patients; three clusters were used in each analysis. After hip arthroplasty: 14.4% of patients were assigned to the cluster with the lowest median OJS-PS of 11 [IQR 8 – 13]. A threshold of 15.5 classified patients as severe or moderate pain with 60.6% sensitivity, 91.0% specificity and 85.7% accuracy. Similarly, after knee arthroplasty, 25.3% were assigned to the cluster with the lowest median OJS-PS of 14 [IQR 11 – 16]. A threshold of 18.5 on the OJS-PS had an 85.4% sensitivity, 88.4% specificity and 87.8% accuracy for classifying patients with self-reported severe or moderate pain. Conclusions This robust and scalable clustering technique on ordinal clinical data estimates the proportion of patients reporting a phenotype consistent with CPSP. On a population level the thresholds identified on the OJS-PS could aid screening for potential CPSP patients 6 months after hip and knee arthroplasties.</jats:p>