医学
骨关节炎
髌下脂肪垫
磁共振成像
滑膜炎
灌注
体质指数
组内相关
膝关节
内科学
核医学
放射科
关节炎
外科
病理
临床心理学
替代医学
心理测量学
作者
Christine Ballegaard,R. Riis,Henning Bliddal,Robin Christensen,Marius Henriksen,Else Marie Bartels,L. Stefan Lohmander,David J. Hunter,R. Bouert,Mikael Boesen
标识
DOI:10.1016/j.joca.2014.04.018
摘要
ObjectiveTo investigate the association between knee pain and signs of inflammation in the infrapatellar fat pad (IPFP) in obese patients with knee osteoarthritis (KOA).DesignIn a cross-sectional setting, 3-T conventional contrast-enhanced (CE) magnetic resonance imaging (MRI) and dynamic contrast-enhanced (DCE)-MRI of KOA were analysed to quantify the extent of inflammation in the IPFP, and correlated (Spearman's rank correlation) to pain and other symptoms assessed via the Knee injury and Osteoarthritis Outcome Score (KOOS) (100 = no pain, 0 = extreme pain). The extent of inflammation in the IPFP was assessed according to the MRI Osteoarthritis Knee Score (MOAKS) using CE-MRI and by DCE-MRI perfusion variables. The perfusion variable, “Inflammation”, was chosen as primary perfusion variable in the analysis. Intraclass correlation coefficients for the perfusion variables ranged from 0.81 to 0.99.ResultsMRI and clinical data were obtained in 95 patients. The typical patient was a woman (82%) with an average age of 65 years (SD 6.5) and a body mass index (BMI) of 32 kg/m2 (SD 3.7). The bivariate association between KOOS pain and the DCE-MRI perfusion variable “Inflammation” showed a statistically significant correlation (r = −0.42, P < 0.0001). A statistically significant correlation was also found between KOOS pain and MOAKS Hoffa-synovitis (r = −0.21, P = 0.046).ConclusionsPerfusion variables on DCE-MRI reflecting the severity of inflammation in the IPFP and MOAKS Hoffa-synovitis were associated with the severity of pain in KOA. These results suggest that severe inflammation in the IPFP is associated with severe pain in KOA and that DCE-MRI is a promising method to study the impact of inflammation in KOA.
科研通智能强力驱动
Strongly Powered by AbleSci AI