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Medial Patellar Plica Thickness as a Morphologic Predictor of the Medial Patellar Plica Syndrome.
- Source :
-
Journal of computer assisted tomography [J Comput Assist Tomogr] 2024 May-Jun 01; Vol. 48 (3), pp. 443-448. Date of Electronic Publication: 2024 Jan 22. - Publication Year :
- 2024
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Abstract
- Objective: The aim of this study was to evaluate the association between medial patellar plica (MPP) syndrome and the morphological features of the MPP, including length, width, and thickness, on knee magnetic resonance imaging (MRI).<br />Materials and Methods: From 2018 to 2022, 167 patients diagnosed with isolated MPP syndrome based on both MRI and arthroscopic findings were included in the "study group" and 226 patients without knee pathology on both MRI and physical examination were included in the "control group." Finally, 393 patients (mean age, 38.9 ± 5.7 years) with 405 knee MRI examinations were included. Morphological MR features of MPP were assessed, including width, length, and thickness. Multivariate regression and receiver operating characteristic analyses were performed to identify the factors associated with MPP syndrome.<br />Results: The mean thickness of MPP was significantly higher in the study group than control group (2.3 ± 0.5 mm vs 1.0 ± 0.8 mm, P < 0.001). Moreover, on multivariate analysis, MPP thickness was the only significant factor associated with MPP syndrome (odds ratio, 6.452; 95% confidence interval, 0.816-15.073; P = 0.002). On receiver operating characteristic analysis, thickness ≥1.8 mm was estimated as the optimal cutoff for predicting MPP syndrome with sensitivity of 75.9%, specificity of 65.4%, and area under the curve of 0.727 (95% confidence interval, 0.667-0.788; P < 0.001).<br />Conclusions: Measurement of MPP thickness on MRI could be a morphological predictor of MPP syndrome.<br />Competing Interests: The authors declare no conflict of interest.<br /> (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1532-3145
- Volume :
- 48
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- Journal of computer assisted tomography
- Publication Type :
- Academic Journal
- Accession number :
- 38271534
- Full Text :
- https://doi.org/10.1097/RCT.0000000000001581