医学
内科学
痹症科
痛风
星团(航天器)
横断面研究
物理疗法
病理
计算机科学
程序设计语言
作者
Nevin Hammam,Samar Tharwat,Ahmed M. Elsaman,Ali Bakhiet,Mohamed Mahmoud,Faten Ismail,Hanan M. El‐Saadany,Rawhya R. El-Shereef,Eman F Mohamed,Mervat I. Abd Elazeem,Ayman Eid,Fatma Ali,Mona Hamdy,Reem El Mallah,Reem Hamdy A Mohammed,Rania M. Gamal,Samar M. Fawzy,Soha Senara,Osman Hammam,Hanan M. Fathi,Adham Aboul Fotouh,Tamer A. Gheita
标识
DOI:10.1016/j.dsx.2023.102897
摘要
Gout comprises a heterogeneous group of disorders; however, comorbidities have been the focus of most efforts to classify disease subgroups. We applied cluster analysis using musculoskeletal ultrasound (MSUS) combined with clinical and laboratory findings in patients with gout to identify disease phenotypes, and differences across clusters were investigated. Patients with gout who complied with the ACR/EULAR classification criteria were enrolled in the Egyptian College of Rheumatology (ECR)-MSUS Study Group, a multicenter study. Selected variables included demographic, clinical, and laboratory findings. MSUS scans assessed the bilateral knee and first metatarsophalangeal joints. We performed a K-mean cluster analysis and compared the features of each cluster. Results: 425 patients, 267 (62.8 %) males, mean age 54.2 ± 10.3 years were included. Three distinct clusters were identified. Cluster 1 (n = 138, 32.5 %) has the lowest burden of the disease and a lower frequency of MSUS characteristics than the other clusters. Cluster 2 (n = 140, 32.9 %) was mostly women, with a low rate of urate-lowering treatment (ULT). Cluster 3 (n = 147, 34.6 %) has the highest disease burden and the greatest proportion of comorbidities. Significant MSUS variations were found between clusters 2 and 3: joint effusion (p < 0.0001; highest: cluster 3), power Doppler signal (p < 0.0001; highest: clusters 2), and aggregates of crystal deposition (p < 0.0001; highest: cluster 3). Cluster analysis using MSUS findings identified three gout subgroups. People with more MSUS features were more likely to receive ULT. Treatment should be tailored according to the cluster and MSUS features.
科研通智能强力驱动
Strongly Powered by AbleSci AI