Clinical phenotyping in sarcoidosis using cluster analysis

• Published in: Respiratory research Vol. 23; no. 1; p. 88
• Main Authors: Lin, Nancy W, Arbet, Jaron, Mroz, Margaret M, Liao, Shu-Yi, Restrepo, Clara I, Mayer, Annyce S, Li, Li, Barkes, Briana Q, Schrock, Sarah, Hamzeh, Nabeel, Fingerlin, Tasha E, Carlson, Nichole E, Maier, Lisa A
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 09.04.2022; BioMed Central; BMC
• Subjects: Abnormalities; Biopsy; Cluster Analysis; Clustering; Disease; Disease severity; Genetic aspects; Health aspects; Health services; Humans; Lungs; Patient outcomes; Phenotype; Phenotypes; Phenotyping; Pulmonary; Respiratory function; Retrospective Studies; Sarcoidosis; Sarcoidosis - diagnosis; Sarcoidosis - epidemiology; Sarcoidosis - genetics; Severity of Illness Index; Spirometry; Steroids; Variables; Variance analysis; United States; Cluster analysis; Disease severity; Phenotypes; Sarcoidosis; Pulmonary
• ISSN: 1465-993X; 1465-9921; 1465-993X; 1465-9921
• DOI: 10.1186/s12931-022-01993-z

Most phenotyping paradigms in sarcoidosis are based on expert opinion; however, no paradigm has been widely adopted because of the subjectivity in classification. We hypothesized that cluster analysis could be performed on common clinical variables to define more objective sarcoidosis phenotypes. We performed a retrospective cohort study of 554 sarcoidosis cases to identify distinct phenotypes of sarcoidosis based on 29 clinical features. Model-based clustering was performed using the VarSelLCM R package and the Integrated Completed Likelihood (ICL) criteria were used to estimate number of clusters. To identify features associated with cluster membership, features were ranked based on variable importance scores from the VarSelLCM model, and additional univariate tests (Fisher's exact test and one-way ANOVA) were performed using q-values correcting for multiple testing. The Wasfi severity score was also compared between clusters. Cluster analysis resulted in 6 sarcoidosis phenotypes. Salient characteristics for each cluster are as follows: Phenotype (1) supranormal lung function and majority Scadding stage 2/3; phenotype (2) supranormal lung function and majority Scadding stage 0/1; phenotype (3) normal lung function and split Scadding stages between 0/1 and 2/3; phenotype (4) obstructive lung function and majority Scadding stage 2/3; phenotype (5) restrictive lung function and majority Scadding stage 2/3; phenotype (6) mixed obstructive and restrictive lung function and mostly Scadding stage 4. Although there were differences in the percentages, all Scadding stages were encompassed by all of the phenotypes, except for phenotype 1, in which none were Scadding stage 4. Clusters 4, 5, 6 were significantly more likely to have ever been on immunosuppressive treatment and had higher Wasfi disease severity scores. Cluster analysis produced 6 sarcoidosis phenotypes that demonstrated less severe and severe phenotypes. Phenotypes 1, 2, 3 have less lung function abnormalities, a lower percentage on immunosuppressive treatment and lower Wasfi severity scores. Phenotypes 4, 5, 6 were characterized by lung function abnormalities, more parenchymal abnormalities, an increased percentage on immunosuppressive treatment and higher Wasfi severity scores. These data support using cluster analysis as an objective and clinically useful way to phenotype sarcoidosis subjects and to empower clinicians to identify those with more severe disease versus those who have less severe disease, independent of Scadding stage.