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.

JMIR Perioperative Medicine
|November 15, 2022
PubMed

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.
Abstract