Characterizing phenotypes and clinical and health utilization associations of young people with chronic pain: latent class analysis using the electronic Persistent Pain Outcomes Collaboration database.

Abstract: Using the Australiasian electronic Persistent Pain Outcomes Collaboration, a binational pain registry collecting standardized clinical data from paediatric ePPOC (PaedsePPOC) and adult pain services (AdultePPOC), we explored and characterized nationally representative chronic pain phenotypes and associations with clinical and sociodemographic factors, health care utilization, and medicine use of young people. Young people ≥15.0 and <25.0 years captured in PaedePPOC and AdultePPOC Australian data registry were included. Data from 68 adult and 12 paediatric pain services for a 5-year period January 2018 to December 2022 (first episode, including treatment information) were analysed. Unsupervised latent class analysis was applied to explore the existence of distinct pain phenotypes, with separate models for both services. A 3-phenotype model was selected from both paediatric and adult ePPOC data, with 693 and 3518 young people included, respectively (at least one valid indicator variable). Indicator variables for paediatric models were as follows: pain severity, functional disability (quasisurrogate "pain interference"), pain count, pain duration, pain-related worry (quasisurrogate "catastrophizing"), and emotional functioning; and, for adult models: pain severity, pain interference, pain catastrophizing, emotional functioning, and pain self-efficacy. From both services, 3 similar phenotypes emerged ("low," "moderate," "high"), characterized by an increasing symptom-severity gradient in multidimensional pain-related variables, showing meaningful differences across clinical and sociodemographic factors, health service utilization, and medicines use. Derived phenotypes point to the need for novel care models that differentially respond to the needs of distinct groups of young people, providing timely, targeted, age-appropriate care. To effectively scale such care, digital technologies can be leveraged to augment phenotype-informed clinical care.