Author: Juan C Sarmiento-Monroy
Brikman et al. (OP0003) developed a machine learning-based prediction model for identifying hyperuricemic participants at risk of developing gout. The model demonstrated relatively good performance and high NPV, suggesting effective prediction of which hyperuricemic patients will not develop gout. This has potential implications for clinical decisions regarding gout prevention therapy.
Keenan et al. (POS0268) evaluated the long-term effect of AR882 (a novel and selective URAT1 inhibitor) on the resolution of target subcutaneous tophi and the reduction in urate crystal deposition volume using DECT in gout patients with subcutaneous tophi. The 12-month treatment of AR882 in patients with tophaceous gout demonstrated effective and safe lowering of sUA levels, continued tophus resolution, and total crystal volume dissolution.
Andrés et al. (POS0125) described the inflammatory profile of patients with asymptomatic hyperuricemia (AH) with subclinical monosodium urate (MSU) crystal deposits detected by ultrasound and compare them with AH without deposits, intercritical gout, and normouricemia. Hyperuricemic groups exhibited a significant pro-inflammatory state and signs of active pyroptosis. The use of different ultrasound definitions for AH with deposits showed similar inflammatory profiles, with notable differences in calprotectin levels that warrant further investigation.
Gouze et al. (POS0568) estimated the prevalence of OMERACT-defined ultrasound (US) lesions in gout patients across different phases of the disease and compared clinical, biological, and US findings between symptomatic (Sy) and asymptomatic (Asy) patients. This study demonstrated that US abnormalities, including SH and gout-specific lesions, were more prevalent in symptomatic patients. The US proved to be a sensitive tool for detecting and monitoring gout lesions, even in asymptomatic patients and those with controlled uricemia.
Hügle et al. (OP0112) developed a deep learning approach to automatically and reliably detect CPPD on hand radiographs, focusing on the triangular fibrocartilage complex and the MCP-2/3 joints. This study demonstrates the potential of an automated model for CPPD detection on hand radiographs, particularly in combined and TFCC-alone models. The algorithm could screen large databases or Electronic Medical Records for CPPD cases.
ABOUT THE AUTHOR
Juan C Sarmiento-Monroy
Juan C. is a Rheumatologist and Clinical Research Fellow at the Hospital Clinic of Barcelona. His main research interests include the validation of biomarkers in RA-ILD and the development of digital tools for patients with SLE.
Juan C. is a member of the EMEUNET Education Sub-committee.