ABSTRACT
OBJECTIVE: This clinical trial was conducted to investigate whether discontinuing methotrexate (MTX) for 1 week after seasonal influenza vaccination is noninferior to discontinuing for 2 weeks after vaccination in patients with rheumatoid arthritis (RA). METHODS: In this multicenter, prospective, randomized, parallel-group noninferiority trial, RA patients receiving a stable dose of MTX were randomly assigned at a ratio of 1:1 to discontinue MTX for 1 week or for 2 weeks after they received the quadrivalent 2021-2022 seasonal influenza vaccine containing H1N1, H3N2, B/Yamagata, and B/Victoria strains. The primary outcome measure was the proportion of patients with a satisfactory vaccine response, which was defined as ≥4-fold increase in antibody titers, as determined with the hemagglutination inhibition assay, against ≥2 of the 4 vaccine strains at 4 weeks after vaccination. RESULTS: The modified intent-to-treat population included 90 patients in the 1-week MTX hold group and 88 patients in the 2-week MTX hold group. The mean ± SD MTX doses were 12.6 ± 3.4 mg/week in the 1-week MTX hold group and 12.9 ± 3.3 mg/week in the 2-week MTX hold group. The proportion of satisfactory vaccine responses did not differ between the groups (68.9% versus 75.0%; P = 0.364). The rate of seroprotection and the fold increase in antibody titers for each of the 4 influenza antigens were similar between the groups. CONCLUSION: A temporary discontinuation of MTX for 1 week after vaccination was noninferior to a discontinuation of MTX for 2 weeks after vaccination, regarding induction of a satisfactory vaccine response to a seasonal influenza vaccine in patients with RA receiving a stable dose of MTX.
ABSTRACT
Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the sacroiliac joints. In this study, we develop a method for detecting bone marrow edema by magnetic resonance (MR) imaging of the sacroiliac joints and a deep-learning network. A total of 815 MR images of the sacroiliac joints were obtained from 60 patients diagnosed with axSpA and 19 healthy subjects. Gadolinium-enhanced fat-suppressed T1-weighted oblique coronal images were used for deep learning. Active sacroiliitis was defined as bone marrow edema, and the following processes were performed: setting the region of interest (ROI) and normalizing it to a size suitable for input to a deep-learning network, determining bone marrow edema using a convolutional-neural-network-based deep-learning network for individual MR images, and determining sacroiliac arthritis in subject examinations based on the classification results of individual MR images. About 70% of the patients and normal subjects were randomly selected for the training dataset, and the remaining 30% formed the test dataset. This process was repeated five times to calculate the average classification rate of the five-fold sets. The gradient-weighted class activation mapping method was used to validate the classification results. In the performance analysis of the ResNet18-based classification network for individual MR images, use of the ROI showed excellent detection performance of bone marrow edema with 93.55 ± 2.19% accuracy, 92.87 ± 1.27% recall, and 94.69 ± 3.03% precision. The overall performance was additionally improved using a median filter to reflect the context information. Finally, active sacroiliitis was diagnosed in individual subjects with 96.06 ± 2.83% accuracy, 100% recall, and 94.84 ± 3.73% precision. This is a pilot study to diagnose bone marrow edema by deep learning based on MR images, and the results suggest that MR analysis using deep learning can be a useful complementary means for clinicians to diagnose bone marrow edema.