Predictors of quality of life, functional status, depression and fatigue in early arthritis: comparison between clinically suspect arthralgia, unclassified arthritis and rheumatoid arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is often preceded by symptomatic phases during which classification criteria are not fulfilled. The health burden of these "at-risk" stages is not well described. This study assessed health-related quality of life (HRQoL), function, fatigue and depression in newly presenting patients with clinically suspect arthralgia (CSA), unclassified arthritis (UA) or RA. METHODS: Cross-sectional analysis of baseline Patient-Reported Outcome Measures (PROMs) was conducted in patients from the Birmingham Early Arthritis Cohort. HRQoL, function, depression and fatigue at presentation were assessed using EQ-5D, HAQ-DI, PHQ-9 and FACIT-F. PROMs were compared across CSA, UA and RA and with population averages from the HSE with descriptive statistics. Multivariate linear regression assessed associations between PROMs and clinical and sociodemographic variables. RESULTS: Of 838 patients included in the analysis, 484 had RA, 200 had CSA and 154 had UA. Patients with RA reported worse outcomes for all PROMs than those with CSA or UA. However, "mean EQ-5D utilities were 0.65 (95%CI: 0.61 to 0.69) in CSA, 0.61 (0.56 to 0.66) in UA and 0.47 (0.44 to 0.50) in RA, which was lower than in general and older (≥ 65 years) background populations." In patients with CSA or UA, HRQoL was comparable to chronic conditions such as heart failure, severe COPD or mild angina. Higher BMI and older age (≥ 60 years) predicted worse depression (PHQ-9: -2.47 (-3.85 to -1.09), P < 0.001) and fatigue (FACIT-F: 5.05 (2.37 to 7.73), P < 0.001). Women were more likely to report worse function (HAQ-DI: 0.13 (0.03 to 0.21), P = 0.01) and fatigue (FACIT-F: -3.64 (-5.59 to -1.70), P < 0.001), and residents of more deprived areas experienced decreased function (HAQ-DI: 0.23 (0.10 to 0.36), P = 0.001), greater depression (PHQ-9: 1.89 (0.59 to 3.18), P = 0.004) and fatigue (FACIT-F: -2.60 (-5.11 to 0.09), P = 0.04). After adjustments for confounding factors, diagnostic category was not associated with PROMs, but disease activity and polypharmacy were associated with poorer performance across all PROMs. CONCLUSIONS: Patient-reported outcomes were associated with disease activity and sociodemographic characteristics. Patients presenting with RA reported a higher health burden than those with CSA or UA, however HRQoL in the pre-RA groups was significantly lower than population averages.

The value of ultrasound-defined tenosynovitis and synovitis in the prediction of persistent arthritis.

OBJECTIVES: The value of US-defined tenosynovitis in predicting the persistence of inflammatory arthritis is not well described. In particular, the predictive utility of US-defined tenosynovitis of larger tendons is yet to be reported. We assessed the value of US-defined tenosynovitis alongside US-defined synovitis and clinical and serological variables in predicting persistent arthritis in an inception cohort of DMARD-naïve patients with early arthritis. METHODS: One hundred and fifty DMARD-naïve patients with clinically apparent synovitis of one or more joints and a symptom duration of ≤3 months underwent baseline clinical, laboratory and US (of 19 bilateral joints and 16 bilateral tendon compartments) assessments. Outcomes were classified as persistent or resolving arthritis after 18 months' follow-up. The predictive value of US-defined tenosynovitis for persistent arthritis was compared with those of US-defined synovitis, and clinical and serological variables. RESULTS: At 18 months, 99 patients (66%) had developed persistent arthritis and 51 patients (34%) had resolving disease. Multivariate logistic regression analysis showed that US-detected digit flexor tenosynovitis [odds ratio (OR): 6.6, 95% CI: 2.0 , 22.1, P = 0.002] provided independent predictive data for persistence over and above the presence of US-detected joint synovitis and RF antibodies. In the RF/ACPA-negative subcohort, US-defined digit flexor tenosynovitis remained a significant predictive variable (OR: 4.7, 95% CI: 1.4, 15.8, P = 0.012), even after adjusting for US-defined joint synovitis. CONCLUSION: US-defined tenosynovitis provided independent predictive data for the development of persistent arthritis. The predictive role of US-defined digit flexor tenosynovitis should be further assessed; investigators should consider including this tendon site as a candidate variable when designing imaging-based predictive algorithms for persistent inflammatory arthritis development.

Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI.

Background Lumbar spine MRI studies are widely used for back pain assessment. Interpretation involves grading lumbar spinal stenosis, which is repetitive and time consuming. Deep learning (DL) could provide faster and more consistent interpretation. Purpose To assess the speed and interobserver agreement of radiologists for reporting lumbar spinal stenosis with and without DL assistance. Materials and Methods In this retrospective study, a DL model designed to assist radiologists in the interpretation of spinal canal, lateral recess, and neural foraminal stenoses on lumbar spine MRI scans was used. Randomly selected lumbar spine MRI studies obtained in patients with back pain who were 18 years and older over a 3-year period, from September 2015 to September 2018, were included in an internal test data set. Studies with instrumentation and scoliosis were excluded. Eight radiologists, each with 2-13 years of experience in spine MRI interpretation, reviewed studies with and without DL model assistance with a 1-month washout period. Time to diagnosis (in seconds) and interobserver agreement (using Gwet κ) were assessed for stenosis grading for each radiologist with and without the DL model and compared with test data set labels provided by an external musculoskeletal radiologist (with 32 years of experience) as the reference standard. Results Overall, 444 images in 25 patients (mean age, 51 years ± 20 [SD]; 14 women) were evaluated in a test data set. DL-assisted radiologists had a reduced interpretation time per spine MRI study, from a mean of 124-274 seconds (SD, 25-88 seconds) to 47-71 seconds (SD, 24-29 seconds) (P < .001). DL-assisted radiologists had either superior or equivalent interobserver agreement for all stenosis gradings compared with unassisted radiologists. DL-assisted general and in-training radiologists improved their interobserver agreement for four-class neural foraminal stenosis, with κ values of 0.71 and 0.70 (with DL) versus 0.39 and 0.39 (without DL), respectively (both P < .001). Conclusion Radiologists who were assisted by deep learning for interpretation of lumbar spinal stenosis on MRI scans showed a marked reduction in reporting time and superior or equivalent interobserver agreement for all stenosis gradings compared with radiologists who were unassisted by deep learning. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hayashi in this issue.

Single-Level Cervical Artificial Disc Replacement Compared with Cage Screw Implants: 2-Year Clinical and Radiological Outcomes Especially Adjacent Level Ossification.

STUDY DESIGN: Retrospective single-center, single-surgeon cohort study. PURPOSE: Our goal was to compare the 2-year clinical and radiological results of artificial disc replacement (ADR) and cage screw (CS) implants in patients with cervical degenerative disc disease (DDD). OVERVIEW OF LITERATURE: Anterior cervical discectomy and fusion with CS implants are an acceptable alternative to traditional cageplate construct due to perceived decreased complications of dysphagia. However, patients may experience adjacent segment disease because of increased motion and intradiscal pressure. ADR is an alternative to restore the physiological kinematics of the operated disc. Few studies directly compare ADR and CS construct for their efficacy. METHODS: Patients who received single-level ADR or CS between January 2008 and December 2018 were included. Data collected was preoperatively, intraoperatively, and postoperatively (6, 12, 24 months). Demographic information, surgical information, complications, follow-up surgery, and outcome ratings (Japanese Orthopaedic Association [JOA], Neck Disability Index [NDI], Visual Analog Scale [VAS] neck and arm, 36-item Short Form Health Survey [SF-36], EuroQoL-5 Dimension [EQ-5D]) were gathered. The radiological assessment included motion segment height, adjacent disc height, lordosis, cervical lordosis, T1 slope, the sagittal vertical axis C2-7, and adjacent level ossification development (ALOD). RESULTS: Fifty-eight patients were included (ADR: 37 and CS: 21). At 6 months, both groups' JOA, VAS, NDI, SF-36, and EQ-5D scores significantly improved, and the positive trends persisted at 2 years. Noted no significant difference in the enhancement of clinical scores except for the VAS arm (ADR: 5.95 vs. CS: 3.43, p =0.001). Radiological parameters were comparable except for the progression of ALOD of the subjacent disc (ADR: 29.7% vs. CS: 66.9%, p =0.02). No significant difference in adverse events or severe complications seen. CONCLUSIONS: ADR and CS obtain good clinical results for symptomatic single-level cervical DDD. ADR demonstrated a significant advantage over CS in the improvement of VAS arm and reduced progression of ALOD of the adjacent lower disc. No statistically significant difference of dysphonia or dysphagia between the two groups were seen, attributed to their comparable zero profile.

Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI.

Background: Metastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated MESCC classification on MRI could aid earlier diagnosis and referral. Purpose: To develop a DL model for automated classification of MESCC on MRI. Materials and Methods: Patients with known MESCC diagnosed on MRI between September 2007 and September 2017 were eligible. MRI studies with instrumentation, suboptimal image quality, and non-thoracic regions were excluded. Axial T2-weighted images were utilized. The internal dataset split was 82% and 18% for training/validation and test sets, respectively. External testing was also performed. Internal training/validation data were labeled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-year experience) and a neuroradiologist (5-year experience). These labels were used to train a DL model utilizing a prototypical convolutional neural network. Internal and external test sets were labeled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labeled independently by the neuroradiologist (5-year experience), a spine surgeon (5-year experience), and a radiation oncologist (11-year experience). Inter-rater agreement (Gwet's kappa) and sensitivity/specificity were calculated. Results: Overall, 215 MRI spine studies were analyzed [164 patients, mean age = 62 ± 12(SD)] with 177 (82%) for training/validation and 38 (18%) for internal testing. For internal testing, the DL model and specialists all showed almost perfect agreement (kappas = 0.92-0.98, p < 0.001) for dichotomous Bilsky classification (low versus high grade) compared to the reference standard. Similar performance was seen for external testing on a set of 32 MRI spines with the DL model and specialists all showing almost perfect agreement (kappas = 0.94-0.95, p < 0.001) compared to the reference standard. Conclusion: A DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.

Tophaceous gouty arthropathy of the lumbar spine.

Gout, classically affecting the first metatarsal joints, knees, fingers and ears, is seldom thrown out as a differential when a patient complains of lower back pain. Symptoms presented by patients with spinal gout may be non-specific and varied; at times, the only clue being that the patient has a previous history of gout. Prompt treatment with anti-inflammatory medication once diagnosis is reached helps to alleviate pain and improves the prognosis of the disease. Therefore, it is vital for the treating physician to keep an open mind and consider spinal gout as a diagnosis once other sinister causes such as osteomyelitis have been ruled out. This greatly reduces the morbidity associated with late treatment of spinal gout.

Cluster headache or giant cell arteritis?

We describe an elderly female patient with known polymyositis who presented with new onset temporal headache that was diagnosed as giant cell arteritis but subsequently had a typical clinical course of cluster headache. This case illustrates the potential for diagnostic confusion between giant cell arteritis (GCA) and cluster headache (CH) and the need to consider CH as a potential differential diagnosis in those newly presenting with headaches, even in the elderly, and relapsing headaches in subjects with a diagnosis of GCA. Importantly, this needs to include female patients, a group historically thought to be much less likely to develop CH.

Plant thorn tenosynovitis.

We report a case of plant thorn injury-induced tenosynovitis of the index finger of the left hand that was cured after surgical removal of the foreign body and partial synovectomy. We review current literature on this infrequently reported topic. Pathologically, thorn synovitis represents a foreign body reaction to retained plant material and is mostly aseptic. The presentation is delayed with the history of preceding injury often forgotten, and the diagnosis thus relies on careful elicitation of history. Imaging may be helpful to localize the plant material when the history is not clear. When possible, removal of the foreign material leads to complete remission, although some patients need partial or total synovectomy.