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MethodsThis study aimed to develop and validate a nomogram for predicting the risk of severe pain in patients with knee osteoarthritis. A total of 150 patients with knee osteoarthritis were enrolled from our hospital, and nomogram was established through a validation cohort (n = 150). An internal validation cohort (n = 64) was applied to validate the model.ResultsEight important variables were identified using the Least absolute shrinkage and selection operator (LASSO) and then a nomogram was developed by Logistics regression analysis. The accuracy of the nomogram was determined based on the C-index, calibration plots, and Receiver Operating Characteristic (ROC) curves. Decision curves were plotted to assess the benefits of the nomogram in clinical decision-making. Several variables were employed to predict severe pain in knee osteoarthritis, including sex, age, height, body mass index (BMI), affected side, Kellgren—Lawrance (K–L) degree, pain during walking, pain going up and down stairs, pain sitting or lying down, pain standing, pain sleeping, cartilage score, Bone marrow lesion (BML) score, synovitis score, patellofemoral synovitis, bone wear score, patellofemoral bone wear, and bone wear scores. The LASSO regression results showed that BMI, affected side, duration of knee osteoarthritis, meniscus score, meniscus displacement, BML score, synovitis score, and bone wear score were the most significant risk factors predicting severe pain.ConclusionsBased on the eight factors, a nomogram model was developed. The C-index of the model was 0.892 (95% CI: 0.839–0.945), and the C-index of the internal validation was 0.822 (95% CI: 0.722–0.922). Analysis of the ROC curve of the nomogram showed that the nomogram had high accuracy in predicting the occurrence of severe pain [Area Under the Curve (AUC) = 0.892] in patients with knee osteoarthritis (KOA). The calibration curves showed that the prediction model was highly consistent. Decision curve analysis (DCA) showed a higher net benefit for decision-making using the developed nomogram, especially in the >0.1 and

Methods: This study aimed to develop and validate a nomogram for predicting the risk of severe pain in patients with knee osteoarthritis. A total of 150 patients with knee osteoarthritis were enrolled from our hospital, and nomogram was established through a validation cohort ( n  = 150). An internal validation cohort ( n  = 64) was applied to validate the model., Results: Eight important variables were identified using the Least absolute shrinkage and selection operator (LASSO) and then a nomogram was developed by Logistics regression analysis. The accuracy of the nomogram was determined based on the C-index, calibration plots, and Receiver Operating Characteristic (ROC) curves. Decision curves were plotted to assess the benefits of the nomogram in clinical decision-making. Several variables were employed to predict severe pain in knee osteoarthritis, including sex, age, height, body mass index (BMI), affected side, Kellgren-Lawrance (K-L) degree, pain during walking, pain going up and down stairs, pain sitting or lying down, pain standing, pain sleeping, cartilage score, Bone marrow lesion (BML) score, synovitis score, patellofemoral synovitis, bone wear score, patellofemoral bone wear, and bone wear scores. The LASSO regression results showed that BMI, affected side, duration of knee osteoarthritis, meniscus score, meniscus displacement, BML score, synovitis score, and bone wear score were the most significant risk factors predicting severe pain., Conclusions: Based on the eight factors, a nomogram model was developed. The C-index of the model was 0.892 (95% CI: 0.839-0.945), and the C-index of the internal validation was 0.822 (95% CI: 0.722-0.922). Analysis of the ROC curve of the nomogram showed that the nomogram had high accuracy in predicting the occurrence of severe pain [Area Under the Curve (AUC) = 0.892] in patients with knee osteoarthritis (KOA). The calibration curves showed that the prediction model was highly consistent. Decision curve analysis (DCA) showed a higher net benefit for decision-making using the developed nomogram, especially in the >0.1 and <0.86 threshold probability intervals. These findings demonstrate that the nomogram can predict patient prognosis and guide personalized treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Shao, Liang, Hu and Bi.)

Objective: To assess the relation between knee osteoarthritis (KOA)-specific quality of life (QOL) and intra-articular cartilage volume (CV) in participants treated with prolotherapy. KOA is characterized by CV loss and multifactorial pain. Prolotherapy is an injection therapy reported to improve KOA-related QOL to a greater extent than blinded saline injections and at-home exercise, but its mechanism of action is unclear., Design: Two-arm (prolotherapy, control), partially blinded, controlled trial., Setting: Outpatient., Participants: Adults with ≥3 months of symptomatic KOA (N=37)., Interventions: Prolotherapy: 5 monthly injection sessions;, Control: blinded saline injections or at-home exercise., Main Outcome Measures: Primary: KOA-specific QOL scores (baseline, 5, 9, 12, 26, and 52wk; Western Ontario and McMaster University Osteoarthritis Index). Secondary: KOA-specific pain, stiffness, function (Western Ontario McMaster University Osteoarthritis Index subscales), and magnetic resonance imaging-assessed CV (baseline, 52wk)., Results: Knee-specific QOL improvement among prolotherapy participants exceeded that among controls (17.6±3.2 points vs 8.6±5.0 points; P=.05) at 52 weeks. Both groups lost CV over time (P<.05); no between-group differences were noted (P=.98). While prolotherapy participants lost CV at varying rates, those who lost the least CV ("stable CV") had the greatest improvement in pain scores. Among prolotherapy participants, but not control participants, the change in CV and the change in pain (but not stiffness or function) scores were correlated; each 1% CV loss was associated with 2.7% less improvement in pain score (P<.05)., Conclusions: Prolotherapy resulted in safe, substantial improvement in KOA-specific QOL compared with control over 52 weeks. Among prolotherapy participants, but not controls, magnetic resonance imaging-assessed CV change (CV stability) predicted pain severity score change, suggesting that prolotherapy may have a pain-specific disease-modifying effect. Further research is warranted., (Copyright © 2013 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.)