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Updated: Aug 21, 2025

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP

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 Wood^1,2, Nicholas C West², Rama S Sreepada^1,2

¹Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, BC, Canada.

JMIR Perioperative Medicine

|November 15, 2022

Summary

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.

Keywords:

anesthesia children digital health tool focus groups health outcome individualized risk opioid use pain participatory medicine patient-oriented research patient-reported experience measures patient-reported outcome measures pediatrics perioperative postoperative postsurgical pain risk prediction surgery thematic analysis virtual focus group

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Last Updated: Aug 21, 2025

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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.