Predicting the Need for Transition from Pediatric to Adult Pain Services: A Retrospective, Longitudinal Study Using the Electronic Persistent Pain Outcome Collaboration (ePPOC) Databases.
Joel ChampionMatthew CrawfordTiina JaanistePublished in: Children (Basel, Switzerland) (2023)
A proportion of youth with chronic pain do not respond to interdisciplinary pain management and may require transition to adult pain services. This study sought to characterize a cohort of patients referred to pediatric pain services who subsequently required referral to an adult pain service. We compared this transition group with pediatric patients eligible by age to transition but who did not transition to adult services. We sought to identify factors predicting the need to transition to adult pain services. This retrospective study utilized linkage data from the adult electronic Persistent Pain Outcomes Collaboration (ePPOC) and the pediatric (PaedePPOC) data repositories. The transition group experienced significantly higher pain intensity and disability, lower quality of life, and higher health care utilization relative to the comparison group. Parents of the transition group reported greater distress, catastrophizing, and helplessness relative to parents in the comparison group. Three factors significantly predicted transition: compensation status (OR = 4.21 (1.185-15)), daily anti-inflammatory medication use (OR = 2 (1.028-3.9)), and older age at referral (OR = 1.6 (1.3-2.17)). This study demonstrated that patients referred to pediatric pain services who subsequently need transition to adult services are a uniquely disabled and vulnerable group beyond comparative peers. Clinical applications for transition-specific care are discussed.
Keyphrases
- chronic pain
- pain management
- healthcare
- primary care
- mental health
- neuropathic pain
- end stage renal disease
- ejection fraction
- physical activity
- multiple sclerosis
- newly diagnosed
- anti inflammatory
- chronic kidney disease
- gene expression
- metabolic syndrome
- machine learning
- social media
- prognostic factors
- genome wide
- skeletal muscle
- high intensity
- deep learning
- antiretroviral therapy