POS0995 VALIDATION OF THE SPARCC MRI-RETIC E-TOOL FOR INCREASING SCORING PROFICIENCY OF MRI LESIONS IN AXIAL SPONDYLOARTHRITIS

Abstract

BackgroundThe web-based Spondyloarthritis Research Consortium of Canada (SPARCC) real-time iterative calibration (RETIC) modules for scoring MRI lesions in axial spondyloarthritis (axSpA) have been created by SPARCC developers to enable remote training of readers to appropriately use the SPARCC MRI inflammation and structural damage instruments and to attain adequate scoring proficiency.ObjectivesWe aimed to test the performance of these modules in enhancing scoring proficiency in comparison to SPARCC developers.MethodsThe SPARCCRETIC SIJ inflammation and structural damage modules are each comprised of 50 DICOM axSpA cases with baseline and follow up scans and an online scoring interface based on SIJ quadrants. Continuous visual real-time feedback regarding concordance/discordance of scoring per SIJ quadrant with expert readers is provided by a color-coding scheme. Reliability is assessed in real-time by intra-class correlation coefficient (ICC), ICC data being provided every 10 cases, which are scored until proficiency targets for ICC are attained. In the present exercise, participants (n=15) from the EuroSpA Imaging project were randomized, stratified by reader expertise in scoring with SPARCC, to one of two reader training strategies (groups A and B) that each comprised 3 stages (25 patients per stage, 2 timepoints, blinded to chronology; independent assessment of Inflammatory and structural lesions): Group A. 1. Review of original SPARCC manuscript describing scoring method. 2. Review of PowerPoint summary of SPARCC method plus completion of SPARCCRETIC module. 3. Re-review of PowerPoint summary. Group B. Same 3-step strategy as A except SPARCCRETIC module completed at stage 3. The reliability of scoring was compared to an expert radiologist (SPARCC developer).ResultsVery good scoring proficiency for status and change scores was evident for SPARCC BME even by non-experienced readers with similar levels of reliability irrespective of prior expertise. The beneficial impact of the SPARCCRETIC module on scoring proficiency was most consistently evident for the scoring of structural lesions and for Strategy B, where the impact was evident for all structural lesions, level of reader expertise, and status as well as change scores (Table 1). Scoring proficiency improved the most for the least experienced readers (Figure 1).Table 1.Inter-rater reliability (Status/Change ICC) compared to radiologist SPARCC developerMRI LesionReader expertiseStrategy AStrategy BStage 1 cases (n=25)Stage 2 cases (n=25)Stage 3 cases (n=25)Stage 1 cases (n=25)Stage 2 cases (n=25)Stage 3 cases (n=25)BMENone (n=4)0.91 / 0.940.83/0.820.77/0.780.82/0.880.65/0.820.88/0.90Intermediate (n=6)0.88/0.880.90/0.900.85/0.900.93/0.940.78/0.800.83/0.80Experienced (n=5)0.92/0.940.90/0.880.92/0.930.83/0.880.84/0.900.89/0.89ANKYLOSISNone (n=4)0.86/0.660.83/0.280.86/0.780.66/0.410.69/0.340.88/0.80Intermediate (n=6)0.89/0.570.83/0.370.92/0.810.82/0.680.74/0.470.93/0.84Experienced (n=5)0.96/0.760.93/0.640.94/0.860.97/0.240.83/0.410.91/0.79BACKFILLNone (n=4)-0.08/-0.050.38/0.220.59/0.380.64/0.130.05/-0.090.47/0.27Intermediate (n=6)0.41/0.130.44/0.420.69/0.390.50/0.220.30/0.300.70/0.42Experienced (n=5)0.82/0.380.55/0.400.91/0.640.65/0.240.21/0.260.71/0.30EROSIONNone (n=4)0.13/-0.080.67/0.420.51/0.330.34/0.330.23/0.080.38/0.37Intermediate (n=6)0.42/0.180.56/0.120.51/0.440.33/0.270.45/0.180.53/0.39Experienced (n=5)0.61/0.330.64/0.340.64/0.420.51/0.270.58/0.110.62/0.31FAT METAPLASIANone (n=4)0.62/0.540.30/0.170.57/0.290.43/0.530.38/0.070.83/0.63Intermediate (n=6)0.49/0.380.59/0.300.79/0.510.57/0.780.50/0.420.81/0.47Experienced (n=5)0.75/0.620.81/0.340.91/0.700.84/0.900.56/0.130.78/0.37ConclusionAttaining scoring proficiency for MRI structural lesions in axSpA is difficult but can be consistently improved by using the SPARCCRETIC module, even for experienced readers.Figure 1.Disclosure of InterestsWalter P Maksymowych Speakers bureau: Abbvie, Janssen, Novartis, Pfizer, UCB, Consultant of: Abbvie, Boehringer Ingelheim, Celgene, Eli-Lilly, Galapagos, Novartis, Pfizer, UCB, Grant/research support from: Abbvie, Novartis, Pfizer, UCB, Anna Enevold Fløistrup Hadsbjerg Grant/research support from: Novartis, Mikkel Østergaard Consultant of: AbbVie, BMS, Boehringer-Ingelheim, Celgene, Eli Lilly and Company, Galapagos, Gilead, Hospira, Janssen, Merck, Novartis, Novo, Orion, Pfizer, Regeneron, Roche, Sandoz, Sanofi, UCB, Grant/research support from: AbbVie, BMS, Merck, Celgene, Novartis, Raphael Micheroli: None declared, Susanne Juhl Pedersen Grant/research support from: Novartis, Adrian Ciurea: None declared, Nora Vladimirova Grant/research support from: Novartis, Michael J Nissen Speakers bureau: Eli-Lilly, Janssen, Novartis, Consultant of: Abbvie, Celgene, Eli-Lilly, Janssen, Novartis, Pfizer, Kristyna Bubova: None declared, Stephanie Wichuk: None declared, Manouk de Hooge: None declared, Ashish Jacob Mathew Grant/research support from: Novartis, Karlo Pintaric: None declared, Monika Gregová: None declared, Ziga Snoj: None declared, Marie Wetterslev: None declared, Karel Gorican: None declared, Joel Paschke: None declared, Iris Eshed: None declared, Robert G Lambert Paid instructor for: Novartis