Applying latent profile analysis to identify adolescents and young adults with chronic conditions at risk for poor health-related quality of life.

Abstract

The impact of chronic diseases on health-related quality of life (HRQOL) in adolescents and young adults (AYAs) is understudied. Latent profile analysis (LPA) can identify profiles of AYAs based on their HRQOL scores reflecting physical, mental, and social well-being. This paper will (1) demonstrate how to use LPA to identify profiles of AYAs based on their scores on multiple HRQOL indicators; (2) explore associations of demographic and clinical factors with LPA-identified HRQOL profiles of AYAs; and (3) provide guidance on the selection of adult or pediatric versions of Patient-Reported Outcomes Measurement Information System® (PROMIS®) in AYAs. A total of 872 AYAs with chronic conditions completed the adult and pediatric versions of PROMIS measures of anger, anxiety, depression, fatigue, pain interference, social health, and physical function. The optimal number of LPA profiles was determined by model fit statistics and clinical interpretability. Multinomial regression models examined clinical and demographic factors associated with profile membership. As a result of the LPA, AYAs were categorized into 3 profiles: Minimal, Moderate, and Severe HRQOL Impact profiles. Comparing LPA results using either the pediatric or adult PROMIS T-scores found approximately 71% of patients were placed in the same HRQOL profiles. AYAs who were female, had hypertension, mental health conditions, chronic pain, and those on medication were more likely to be placed in the Severe HRQOL Impact Profile. Our findings may facilitate clinicians to screen AYAs who may have low HRQOL due to diseases or treatments with the identified risk factors without implementing the HRQOL assessment.