Clinical and experimental rheumatology · 2024
Clinical phenotypes of sarcoidosis using cluster analysis: a Spanish population-based cohort study.
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Fernández-Ramón Raúl, Gaitán-Valdizán Jorge Javier, Martín-Varillas José Luis, Demetrio-Pablo Rosalía, Ferraz-Amaro Iván, Castañeda Santos, Blanco Ricardo
Аннотация
OBJECTIVES: Sarcoidosis is a clinically heterogenous disease. The objective of this study is the identification of clinical phenotypes using cluster analysis.
METHODS: A model-based clustering relaying on 19 clinical variables was performed in a retrospective cohort of 342 sarcoidosis patients, diagnosed and followed-up from 1999 to 2019 in a tertiary hospital at Northern Spain. Chi-square test and ANOVA were used to compare categorical and continuous variables among groups. Two-sample t-tests and the partition of Pearson's chi-square statistic were used in pairwise comparisons. The Wasfi severity score was calculated and compared among clusters.
RESULTS: Cluster analysis identified five groups: C1 (16.1%), C2 (14.3%), C3 (24.3%), C4 (5.0%), and C5 (40.4%). Lung involvement was predominant, ranging from 55.1% (C2) to 100% (C1 and C4). Extrapulmonary involvement was significantly higher in C2 (96.4%) and C3 (98.0%). A significant lower FEV1 percent predicted was detected in C5 (90.5±21.8) versus C1 (102.0±22.9), C3 (102.3±17.6) and C4 (105.8±20.8). The cluster 5 had a lower FVC percent predicted (96.6±18.9) than others, ranging from 108.1±18.0 (C3) to 111.5±21.7 (C4). The prescription of systemic glucocorticoids and non-corticosteroid immunosuppressants was higher in the clusters 1, 3 and 5. Chronicity rates were higher in C3 (31.3%) and C5 (32.6%) compared to C1 (9.1%) and C4 (0%), as well as the Wasfi severity score values.
CONCLUSIONS: Five phenotypes with different clinical and prognostic characteristics are proposed in our study. Cluster analysis can be a useful tool for identifying clinical patterns in a disease as heterogeneous as sarcoidosis and optimising its management.
METHODS: A model-based clustering relaying on 19 clinical variables was performed in a retrospective cohort of 342 sarcoidosis patients, diagnosed and followed-up from 1999 to 2019 in a tertiary hospital at Northern Spain. Chi-square test and ANOVA were used to compare categorical and continuous variables among groups. Two-sample t-tests and the partition of Pearson's chi-square statistic were used in pairwise comparisons. The Wasfi severity score was calculated and compared among clusters.
RESULTS: Cluster analysis identified five groups: C1 (16.1%), C2 (14.3%), C3 (24.3%), C4 (5.0%), and C5 (40.4%). Lung involvement was predominant, ranging from 55.1% (C2) to 100% (C1 and C4). Extrapulmonary involvement was significantly higher in C2 (96.4%) and C3 (98.0%). A significant lower FEV1 percent predicted was detected in C5 (90.5±21.8) versus C1 (102.0±22.9), C3 (102.3±17.6) and C4 (105.8±20.8). The cluster 5 had a lower FVC percent predicted (96.6±18.9) than others, ranging from 108.1±18.0 (C3) to 111.5±21.7 (C4). The prescription of systemic glucocorticoids and non-corticosteroid immunosuppressants was higher in the clusters 1, 3 and 5. Chronicity rates were higher in C3 (31.3%) and C5 (32.6%) compared to C1 (9.1%) and C4 (0%), as well as the Wasfi severity score values.
CONCLUSIONS: Five phenotypes with different clinical and prognostic characteristics are proposed in our study. Cluster analysis can be a useful tool for identifying clinical patterns in a disease as heterogeneous as sarcoidosis and optimising its management.