Latent Class Analysis: A Novel Strategy for Identifying Chronic Pain Phenotypes in Youth (Motivated by the Study on Pain Subgroups and Healthcare Utilization by Slater et al.)

Latent Class Analysis (LCA) is a statistical method used to identify chronic pain phenotypes within heterogeneous populations based on observed patterns in categorical or continuous data. Unlike traditional, variable-centered methods, LCA employs a person-centered approach, effectively capturing complex phenotypic profiles. This paper illustrates the utility of LCA through the study by Zambelli et al. (2024), focusing on pediatric chronic pain phenotypes in youth. Using data from the electronic Persistent Pain Outcomes Collaboration (ePPOC), chronic pain phenotypes were identified based on demographic, psychological, and healthcare utilization variables.Key datasets required for robust LCA include comprehensive symptom data, healthcare utilization records, demographic covariates, and psychosocial factors. Longitudinal datasets further enhance the analysis by tracking phenotype stability over time. The Zambelli study demonstrated three pain subgroups among youth—high, moderate, and low pain intensity—with distinct healthcare utilization patterns, highlighting LCA’s potential for personalized intervention strategies.While LCA effectively classifies heterogeneous populations without predefined categories, challenges include potential model misclassification, subjective class-number selection, and large data requirements. Future research should integrate genetic, psychosocial, and environmental variables, leverage longitudinal data, and combine LCA with advanced machine learning techniques to refine pain classification methods and chronic pain management strategies in youth. These findings support the role of latent class analysis in pediatric chronic pain research and clinical classification. These findings support the role of latent class analysis in pediatric chronic pain research and clinical classification.