# Longitudinal Resilience and Risk Factors in Pediatric Postoperative Pain (LORRIS): Protocol for a Prospective Longitudinal Swiss University Children’s Hospitals-Based Study

## TL;DR

This study explores how emotional factors in children and parents influence the development of chronic pain after surgery, tracking changes over a year.

## Contribution

The study introduces a longitudinal approach to examine emotional variability, regulation, and differentiation as predictors of chronic postsurgical pain in children.

## Key findings

- Emotional state and regulation will be assessed through daily diaries and questionnaires at multiple time points.
- K-means clustering will identify pain and emotion-related trajectories in children post-surgery.
- Regression models will predict chronic pain risk based on emotional and psychological factors.

## Abstract

Chronic postsurgical pain (CPSP) is defined as pain that persists after a surgical procedure and has a significant impact on quality of life. Previous studies show the importance of psychological factors in CPSP, yet the majority of studies focused solely on negative emotions. This longitudinal observational study aims to broaden this knowledge base by examining the role of emotional state, emotion variability, emotion regulation and emotion differentiation on the child and the parent level for the development CPSP, and to describe pain and emotion-related trajectories following surgery.

We intend to include 280 children and adolescents aged 8–18 years with a planned orthopaedic surgery and their parents. A total of five assessment time points is planned: 3 weeks before surgery (baseline), 2 weeks after surgery (post) and 3 months (follow-up (FU) 1), 6 months and 12 months after surgery. At baseline and post only, children and parents are asked to complete a daily diary thrice a day for a week where they rate their current emotional state and their pain severity (children only). Emotional state ratings will be used to calculate indices of emotion variability, emotion regulation and emotion differentiation. Children and parents will complete questionnaires at each time point, including measures on quality of life, social support, sleep, and symptoms of anxiety and depression.

To predict development of CPSP, generalised linear regression models will be used, resulting in ORs and 95% CIs. Pearson product-moment correlations between predictors and outcomes will be evaluated at each time point. The primary outcome of the prediction model is CPSP at FU1. For the trajectory analysis, the classification method K-means for longitudinal data will be used to determine clusters in the data.

The Ethics Committee of the Canton of Zurich, Switzerland, has approved the study (ID: 2023-01475). Participants will be compensated, and a dissemination workshop will be held.

NCT05816174.