Joint effusion at 6 months is a significant predictor of joint effusion 1 year after anterior cruciate ligament reconstruction

Can radiological findings detect neuropathic pain in patients with osteoarthritis of the knee undergoing surgery?

OBJECTIVES

The mechanism underlying neuropathic pain (NP) in osteoarthritis (OA) of the knee is not completely understood. This study aimed to investigate whether possible NP in patients with knee OA undergoing knee surgery is associated with specific radiological findings.

METHODS

This study included 197 patients who underwent knee surgery for symptomatic knee OA. Clinical evaluation was performed using the Central Sensitization Inventory (CSI), Western Ontario and McMaster Universities Arthritis Index (WOMAC), and PainDETECT questionnaire. Radiological evaluation was performed using the hip-knee-ankle (HKA) angle, posterior tibial slope (PTS), varus and valgus laxities, and magnetic resonance imaging OA Knee Score (MOAKS). Radiological findings were compared between patients with possible and unlikely NP. Logistic regression analysis was performed to identify the predictive factors for NP.

RESULTS

There were 163 and 34 patients with unlikely NP and with possible NP, respectively. The percentage of patients with CSI score ≥ 40 was significantly higher in the possible NP group than in the unlikely NP group (17.6% vs. 6.1%). Patients with possible NP had worse WOMAC scores than patients with unlikely NP. There were no significant positive associations between the possible NP and radiological findings in knee OA. Regression analysis showed no predictive factors for possible NP.

CONCLUSIONS

Possible NP is not associated with specific radiological findings in knee OA. Patients with possible NP may mediate CS and experience more severe symptoms, including decreased knee function and lower quality of life, than patients with unlikely NP.

Joint effusion at 3 months after anterior cruciate ligament reconstruction is associated with reinjury

PURPOSE

To evaluate whether joint effusion at 3 months after anterior cruciate ligament (ACL) reconstruction is associated with ACL reinjury.

METHODS

The medical records of 227 consecutive patients who underwent single-bundle ACL reconstruction between 2015 and 2018 were reviewed in this retrospective single-center study. Demographic data such as sex and age at surgery, as well as data on preinjury Tegner activity scale score, time from injury to surgery, presence of meniscus and cartilage injuries, and the occurrence of ACL reinjury within 2 years, were collected. Joint effusion was defined as grade 3 (range 0-3) according to the ACL Osteoarthritis Score by magnetic resonance imaging at 3 months postoperatively. Multivariate logistic regression analysis was performed to control for potential confounders.

RESULTS

A total of 176 patients (mean age 22.5 ± 9.9 years) were included. Among these patients, 18 (10.2%) had ACL reinjury. At the multivariate logistic regression analysis, higher Tegner activity scale (odds ratio [OR] 3.12; 95% confidence interval [CI] 1.61-6.04; p < 0.001) and presence of joint effusion (OR 34.5; 95% CI 6.63-179.7; p < 0.001) increased the odds of ACL reinjury, and older age (OR 0.68; 95% CI 0.51-0.92; p = 0.012) decreased the odds of ACL reinjury.

CONCLUSIONS

Joint effusion with a larger fluid volume at 3 months postoperatively was one of the risk factors for ACL reinjury independent of confounders, such as age and activity level. This result suggests the possibility of postoperative intervention for ACL reinjury.

LEVEL OF EVIDENCE

III.

Automatic estimation of knee effusion from limited MRI data

Knee effusion is a common comorbidity in osteoarthritis. To quantify the amount of effusion, semi quantitative assessment scales have been developed that classify fluid levels on an integer scale from 0 to 3. In this work, we investigated the use of a neural network (NN) that used MRI Osteoarthritis Knee Scores effusion-synovitis (MOAKS-ES) values to distinguish physiologic fluid levels from higher fluid levels in MR images of the knee. We evaluate its effectiveness on low-resolution images to examine its potential in low-field, low-cost MRI. We created a dense NN (dNN) for detecting effusion, defined as a nonzero MOAKS-ES score, from MRI scans. Both the training and performance evaluation of the network were conducted using public radiological data from the Osteoarthritis Initiative (OAI). The model was trained using sagittal turbo-spin-echo (TSE) MR images from 1628 knees. The accuracy was compared to VGG16, a commonly used convolutional classification network. Robustness of the dNN was assessed by adding zero-mean Gaussian noise to the test images with a standard deviation of 5-30% of the maximum test data intensity. Also, inference was performed on a test data set of 163 knees, which includes a smaller test set of 36 knees that was also assessed by a musculoskeletal radiologist and the performance of the dNN and the radiologist compared. For the larger test data set, the dNN performed with an average accuracy of 62%. In addition, the network proved robust to noise, classifying the noisy images with minimal degradation to accuracy. When given MRI scans with 5% Gaussian noise, the network performed similarly, with an average accuracy of 61%. For the smaller 36-knee test data set, assessed both by the dNN and by a radiologist, the network performed better than the radiologist on average. Classifying knee effusion from low-resolution images with a similar accuracy as a human radiologist using neural networks is feasible, suggesting automatic assessment of images from low-cost, low-field scanners as a potentially useful assessment tool.