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Abstract
Background Joints are affected in up to 95% of patients with systemic lupus erythematosus (SLE) and 11.7% of lupus patients develop permanent joint damage. Additionally, arthritis is present in 70-80% of lupus patients in clinical trials. There is continued debate as to whether tenderness in the absence of swelling constitutes active lupus arthritis and should be scored on activity instruments. The confusion over arthritis scoring impacts both clinical care and trials. There is an unmet need for a simple tool that can objectively assess lupus arthritis; near-infrared optical imaging has the potential to address this need. Over the last decade the technology has been optimized for use in brain imaging, breast cancer, and peripheral ischemia. Studies have also shown its utility in rheumatoid arthritis. Near-infrared light illuminates the tissues and transmitted and reflected light intensities are measured. Maps computed from these measurements show the spatial distribution of physiological parameters. Changes in optical properties caused by physiologic changes form the basis for its diagnostic capabilities. The current study evaluates the performance of frequency-domain optical imaging (FDOI) compared to a composite score that included tender and swollen joint counts (TSJc) and MSK ultrasound.
Methods Bilateral PIP joints from SLE patients with arthritis were assessed using TSJc, ultrasound, and frequency FDOI. Two independent assessors (both experienced rheumatologists) examined the PIPs and determined the TSJc. Subsequently, a trained technician performed ultrasonographic evaluation according to musculoskeletal US EULAR guidelines; data was obtained on Grey Scale (GS), Power Doppler (PD), and combined PDUS score as per EULAR-OMERACT recommendations. Finally, three independent and experienced clinicians reviewed the data and images in order to arrive at a consensus on a binary categorization of each individual PIP joint as either: 0 = no arthritis, or 1 = arthritis. All patients underwent FDOI assessment. The FDOI system consists of a laser diode projecting a 1-mm – 670 nm optical beam at 11 different positions along the PIP joints, 2mm apart, lengthwise. At each point, 16 images are acquired at different phases by a camera. The FDOI system is operated at a frequency of 300 MHz. Amplitudes and phases signals were generated using a demodulation algorithm. The fingers’ sizes (width and thickness) were measured using a caliper. An Optical Model Score of 0 (no arthritis) or 1 (arthritis) was generated for each PIP joint based on the FDOI assessment results. Binary scores from FDOI evaluation were compared to those from clinician consensus.
Results A total of 40 PIP joints (bilateral PIP 2-5, 8 joints per subject) from 5 SLE patients with arthritis were assessed. The Clinician Consensus Score (figure 1) and Optical Model Score (figure 2) are shown in heat-map formats, respectively. Each color block represents one individual PIP joint of a SLE arthritis patient. Binary score of 1 (arthritis) is represented by a black block, while a score of 0 (no arthritis) is represented by a white block; Clinician consensus ascertained 30 PIP joints (75%) as having arthritis while 10 (25%) as no arthritis. In comparison, FDOI evaluation judged 26 PIP joints (65%) as having arthritis while 14 (35%) PIP joints were considered to have no arthritis. An overlay between Clinician Consensus Score and Optical Model Score was shown in figure 3. Each grey block represents a mismatch between FDOI evaluation and clinician consensus in the correspondent PIP joint. The number of mismatch is 11 (27.5%) in all 40 joints. The Pearson’s correlation between the Clinician Consensus Score and Optical Model Score was 0.55, suggesting a moderate correlation.
Clinician consensus score
Optical model score
Overlay result
Conclusions These pilot data show that FDOI measurements have comparable potential to detect arthritis in PIP joints. If confirmed in a larger clinical study, FDOI could bring objectivity to the quantification of SLE arthritis. Compared to joint counts, US, and MR imaging, the advantages of FDOI are non-invasiveness, objectivity (eliminates inter-rater variability and operator dependency), low cost, and high speed of performance (~5 min).
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