Session: (1787–1829) Metabolic and Crystal Arthropathies – Basic and Clinical Science Poster
1823: Unsupervised Cluster Analysis of Clinical and Ultrasound Features Reveals Unique Gout Subtypes: Results from the Egyptian College of Rheumatology (ECR)
Rheumatology Department, Faculty of Medicine, Assiut University, Assiut, Egypt PASADENA, CA, United States
Tamer A Gheita1, Ahmed M Elsaman2, Aly Bakhiet3, Mohamed Bakrey Mahmoud4, Faten Ismail5, Hanan El Saadany6, Rawhya R ElShereef5, Eman F Mohamed7, Mervat I Abd Elazeem8, Ayman Eid8, Fatma Ali5, Mona Hamdy5, Reem El Mallah9, Reem HA Mohammed1, Samar Tharwat10, Rania M Gamal11, Samar Fawzy1, Soha Senara12, Hanan M Fathi12, Adham Aboul Fotouh13 and Nevin Hammam14, 1Rheumatology Department, Faculty of Medicine, Cairo University, Cairo, Egypt, 2Rheumatology Department, Faculty of Medicine, Sohag University, Sohag, Egypt, 3Higher Institute for Computer Science and Information Systems, 6th of October City, Assiut, Egypt, 4Higher Institute for Computer Science and Information Systems, 6th of October City, Cairo, Egypt, 5Rheumatology Department, Faculty of Medicine, Minia University, Minia, Egypt, 6Rheumatology Department, Tanta University, Gharbia, Egypt, 7Internal Medicine Department, Rheumatology Unit, Faculty of Medicine (Girls), Al-Azhar University, Cairo, Egypt, 8Rheumatology Department, Faculty of Medicine, Beni-Suef University, Beni-Suef, Egypt, 9Rheumatology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt, 10Internal Medicine, Rheumatology Unit, Mansoura University, Dakahlia, Egypt, Dakahlia, Egypt, 11Rheumatology Department, Faculty of Medicine, Al-Azhar University, Assiut, Egypt, 12Rheumatology Department, Faculty of Medicine, Fayoum University, Fayoum, Egypt, 13Egyptian School for Musculoskeletal Ultrasonography (EgySMUS); Egyptian Society of Musculoskeletal and Neuromuscular Sonography (ESMNS), Cairo, Egypt, 14Rheumatology Department, Faculty of Medicine, Assiut University, Assiut, Egypt, PASADENA, CA
Background/Purpose: Gout comprises a heterogeneous group of disorders characterized by inflammatory arthritis associated with comorbidities leading to impaired quality of life, and an extensive burden on health care costs. Musculoskeletal ultrasound (MSUS) has shown great advantages in the diagnosis of gout; with its incorporation in the latest American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) gout classification criteria. Efforts to subtype the disease have been limited to the impact of comorbidities on gout patients, and no attempt has been made to describe how ultrasonographic features combined with clinical and laboratory characteristics categorize the population with gout into subtypes. Here, we applied unsupervised clustering analysis to identify clinically-relevant phenotypes in patients with gout on the basis of MSUS findings and to investigate differences across clusters.
Methods: This was a cross-sectional multicentre study of 425 gouty patients who met the ACR/EULAR classification criteria in the Egyptian College of Rheumatology (ECR)-MSUS Study Group, a nationally representative sample. Variables including demographic, lifestyle factors, clinical manifestations, comorbidities, and laboratory findings were selected for analysis. Musculoskeletal scans evaluated bilateral knee and 1st MTP joints for joint effusion, synovial proliferation, joint erosions, urate crystal deposits (double contour, aggregates), and power Doppler (PD) signal as a marker of local inflammation. We conducted a K-mean cluster analysis; the characteristics of each cluster were compared using the Chi-square test for categorical variables and one-way ANOVA for numeric outcomes.
Results: Overall, 425 patients, 267 (62.8%) male; mean (SD) age, 54.2 (10.3) years; and 364 (85.6%) had mono/oligoarticular involvement were included in the analysis. Three distinct clusters were identified in our dataset (Table 1). Cluster 1 (n=138, 32.5%) has the lowest overall disease burden with a lower frequency of MSUS features than other clusters, representing the benign disease subtype. Cluster 2 (n=140, 32.9%) was predominantly women and had the smallest number of smokers with a relatively low rate of patients receiving urate-lowering therapy (ULT) and an intermediate frequency of comorbidities. Cluster 3 (n=147, 34.6%), the most severe disease burden, with the highest proportion of patients with comorbidities, and were receiving ULT (Table 1). Significant MSUS differences between cluster 2 and 3 were observed for joint effusion (p< 0.0001; highest: Cluster 3); power Doppler signal (p< 0.0001; highest: Clusters 2); aggregates of crystals deposition (p< 0.0001; highest: Cluster 3); and presence of double contour (p=0.014; highest: Cluster 3) (Figure 1).
Conclusion: Cluster combined clinical and ultrasound findings from adults with gout identified three differentiated subgroups. Ultrasound may help in identifying different disease phenotypes and could support more tailoring of treatments at various stages of gout. Thus, we should pay attention to the musculoskeletal ultrasound findings when treating patients with gout.
Acknowledgment: We would like to thank Rasha Fawzy, and Doaa Mosaad.
SD: standard deviation; WBCs, PLT, ESR, SUA *Cluster 2 vs cluster 1, ** Cluster 3 vs cluster 1, *** Cluster 2 vs cluster 3. Definition: Obesity was defined as body mass index ≥30 kg/m2. Hypertension was defined as blood pressure≥130/90 mm Hg or current antihypertensive treatment. Diabetes was defined as a fasting glycemia>1.26 g/L or the use of antidiabetic drugs. Renal disorders were defined as glomerular filtration rate < 60 mL/min, proteinuria, and abnormal urinary sediment. The liver disorder was defined by the presence of hepatic steatosis or cirrhosis or abnormal liver enzyme levels. Dyslipidemia was defined by the presence of hypertriglyceridemia or hypercholesterolemia.
Disclosures: T. Gheita, None; A. M Elsaman, None; A. Bakhiet, None; M. Bakrey Mahmoud, None; F. Ismail, None; H. El Saadany, None; R. R ElShereef, None; E. F Mohamed, None; M. I Abd Elazeem, None; A. Eid, None; F. Ali, None; M. Hamdy, None; R. El Mallah, None; R. HA Mohammed, None; S. Tharwat, None; R. M Gamal, None; S. Fawzy, None; S. Senara, None; H. M Fathi, None; A. Aboul Fotouh, None; N. Hammam, None.
