The Journal of rheumatology · 2026
Uncovering Heterogeneous Health Burden in Axial Spondyloarthritis Through Latent Class Analysis of the Assessment of SpondyloArthritis International Society Health Index.
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Alonso Sara, Burger Stefanie, Pardo Estefanía, Braña Ignacio, Loredo Marta, Alvarez Paula, Alperi Mercedes, Queiro Rubén
Аннотация
OBJECTIVE: To identify and characterize unobserved subgroups of patients with axial spondyloarthritis (axSpA) based on response patterns to the Assessment of SpondyloArthritis international Society Health Index (ASAS HI) using latent class analysis (LCA).
METHODS: We conducted LCA on 17 dichotomous ASAS HI items in a cohort of 180 patients with axSpA. Models with 2 to 6 classes were evaluated using the Bayesian information criterion (BIC) and Akaike information criterion, with the optimal model selected based on the lowest BIC. Class profiles were defined by conditional item endorsement probabilities. Construct validity analyses included comparisons of Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI), Axial Spondyloarthritis Disease Activity Score (ASDAS), and total ASAS HI scores. Internal validation was performed using bootstrap resampling. Secondary multinomial logistic regression assessed the influence of disease indices on class membership.
RESULTS: A 5-class model showed distinct health impact profiles. Class 1 (n = 25 [13.9%]) exhibited the highest burden across physical, emotional, and motivational domains, whereas class 2 (n = 66 [36.7%]) showed minimal impact. The intermediate classes reflected predominantly physical (class 3; n = 35 [19.4%]), fatigue-related (class 4; n = 18 [10%]), or functional (class 5; n = 36 [20%]) impairment patterns. BASDAI, BASFI, ASDAS, and total ASAS HI scores differed significantly across classes (all P < 0.001). Combined multinomial regression models that included BASDAI, ASDAS, and BASFI showed a pseudo-R 2 of 0.33 for class allocation. Posterior classification probabilities were high (mean 0.88), and bootstrap analysis confirmed model stability.
CONCLUSION: LCA of ASAS HI responses identified 5 distinct and clinically meaningful health impact profiles in axSpA. These findings highlight the multidimensional burden of axSpA and support the potential role of patient-reported outcome measure-based stratification.
METHODS: We conducted LCA on 17 dichotomous ASAS HI items in a cohort of 180 patients with axSpA. Models with 2 to 6 classes were evaluated using the Bayesian information criterion (BIC) and Akaike information criterion, with the optimal model selected based on the lowest BIC. Class profiles were defined by conditional item endorsement probabilities. Construct validity analyses included comparisons of Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI), Axial Spondyloarthritis Disease Activity Score (ASDAS), and total ASAS HI scores. Internal validation was performed using bootstrap resampling. Secondary multinomial logistic regression assessed the influence of disease indices on class membership.
RESULTS: A 5-class model showed distinct health impact profiles. Class 1 (n = 25 [13.9%]) exhibited the highest burden across physical, emotional, and motivational domains, whereas class 2 (n = 66 [36.7%]) showed minimal impact. The intermediate classes reflected predominantly physical (class 3; n = 35 [19.4%]), fatigue-related (class 4; n = 18 [10%]), or functional (class 5; n = 36 [20%]) impairment patterns. BASDAI, BASFI, ASDAS, and total ASAS HI scores differed significantly across classes (all P < 0.001). Combined multinomial regression models that included BASDAI, ASDAS, and BASFI showed a pseudo-R 2 of 0.33 for class allocation. Posterior classification probabilities were high (mean 0.88), and bootstrap analysis confirmed model stability.
CONCLUSION: LCA of ASAS HI responses identified 5 distinct and clinically meaningful health impact profiles in axSpA. These findings highlight the multidimensional burden of axSpA and support the potential role of patient-reported outcome measure-based stratification.