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Updated: Aug 21, 2025

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
Identifying Risk Factors, Patient-Reported Experience and Outcome Measures, and Data Capture Tools for an
Michael D Wood1,2, Nicholas C West2, Rama S Sreepada1,2
1Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, BC, Canada.
Insights
This study identified key factors for predicting pediatric postoperative pain. These findings will help develop personalized prehabilitation strategies to reduce persistent pain and opioid use in children.
Area of Science:
- Pediatric surgery
- Pain management
- Digital health
Background:
- The perioperative period offers opportunities for digital health innovation.
- Personalized prehabilitation can optimize pediatric surgical patients' health.
- Systematic use of preoperative data for personalized interventions is lacking.
Purpose of the Study:
- To reduce persistent postsurgical pain (PPSP) and long-term opioid use in children.
- To develop personalized pain risk prediction models.
- To identify targeted prehabilitation strategies.
Main Methods:
- Patient-oriented research methodology with parental/caregiver and clinician expertise.
- Virtual focus groups conducted at a tertiary pediatric hospital.
- Thematic analysis of data to identify risk factors and outcome measures.
Main Results:
- Identified 5 key domains: demographic, psychosocial, and clinical risk factors, patient-reported experience measures (PREMs), and patient-reported outcome measures (PROMs).
- Determined functional requirements for data collection tools, including standardization, validation, and longitudinal tracking.
- Selected established questionnaires for pain, satisfaction, and substance use for a data collection platform.
Conclusions:
- Established 5 key data domains for pain risk and recovery assessment.
- Defined functional requirements and delivery modes for data capture tools.
- Implemented tools to generate data for personalized pain risk prediction models.
Background:
The perioperative period is a data-rich environment with potential for innovation through digital health tools and predictive analytics to optimize patients' health with targeted prehabilitation. Although some risk factors for postoperative pain following pediatric surgery are already known, the systematic use of preoperative information to guide personalized interventions is not yet widespread in clinical practice.
Objective:
Our long-term goal is to reduce the incidence of persistent postsurgical pain (PPSP) and long-term opioid use in children by developing personalized pain risk prediction models that can guide clinicians and families to identify targeted prehabilitation strategies. To develop such a system, our first objective was to identify risk factors, outcomes, and relevant experience measures, as well as data collection tools, for a future data collection and risk modeling study.
Methods:
This study used a patient-oriented research methodology, leveraging parental/caregiver and clinician expertise. We conducted virtual focus groups with participants recruited at a tertiary pediatric hospital; each session lasted approximately 1 hour and was composed of clinicians or family members (people with lived surgical experience and parents of children who had recently undergone a procedure requiring general anesthesia) or both. Data were analyzed thematically to identify potential risk factors for pain, as well as relevant patient-reported experience and outcome measures (PREMs and PROMs, respectively) that can be used to evaluate the progress of postoperative recovery at home. This guidance was combined with a targeted literature review to select tools to collect risk factor and outcome information for implementation in a future study.
Results:
In total, 22 participants (n=12, 55%, clinicians and n=10, 45%, family members) attended 10 focus group sessions; participants included 12 (55%) of 22 persons identifying as female, and 12 (55%) were under 50 years of age. Thematic analysis identified 5 key domains: (1) demographic risk factors, including both child and family characteristics; (2) psychosocial risk factors, including anxiety, depression, and medical phobias; (3) clinical risk factors, including length of hospital stay, procedure type, medications, and pre-existing conditions; (4) PREMs, including patient and family satisfaction with care; and (5) PROMs, including nausea and vomiting, functional recovery, and return to normal activities of daily living. Participants further suggested desirable functional requirements, including use of standardized and validated tools, and longitudinal data collection, as well as delivery modes, including electronic, parent proxy, and self-reporting, that can be used to capture these metrics, both in the hospital and following discharge. Established PREM/PROM questionnaires, pain-catastrophizing scales (PCSs), and substance use questionnaires for adolescents were subsequently selected for our proposed data collection platform.
Conclusions:
This study established 5 key data domains for identifying pain risk factors and evaluating postoperative recovery at home, as well as the functional requirements and delivery modes of selected tools with which to capture these metrics both in the hospital and after discharge. These tools have been implemented to generate data for the development of personalized pain risk prediction models.
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