• Author
    Daniel Kim
  • Discovery PI

    Cecilia Canales, MD, MPH

  • Project Co-Author

    Abraham Correa-Medina, MD; Ha-Jung Kim, MD; Nancy Boulos, MD

  • Abstract Title

    Preoperative Risk Stratification for Poorly Controlled Postoperative Pain and Its Translation Into EHR-Enabled, Implementation Science-Informed Care Pathways: A Scoping Review Protocol

  • Discovery AOC Petal or Dual Degree Program

    Basic, Clinical, & Translational Research

  • Abstract

    Background: Poorly controlled acute postoperative pain affects 20–40% of surgical patients and is associated with persistent postsurgical pain, prolonged opioid use, delayed recovery, and increased healthcare utilization. Although preoperative risk factors and multivariable prediction models have been identified, they remain largely confined to research settings and are not routinely embedded into perioperative clinical workflows. A critical gap exists in synthesizing this literature through an implementation science lens, specifically evaluating whether identified predictors are available as discrete electronic health record (EHR) data elements and whether validated models can be operationalized into clinical decision support (CDS) tools.

    Objective: To map preoperative predictors and prediction models for poorly controlled postoperative pain, assess their EHR computability, and generate implementation-ready specifications for risk-stratified perioperative care pathways.

    Methods: We will conduct a scoping review following PRISMA-ScR reporting standards, searching MEDLINE, Embase, CINAHL, Web of Science, and the Cochrane Library from inception to present with no date or language restrictions. Eligible studies include adult surgical populations reporting preoperative predictors or prediction models for acute postoperative pain outcomes. Two independent reviewers will screen all titles, abstracts, and full texts against eligibility criteria, with discrepancies resolved by consensus. Data will be charted on predictor characteristics, model performance, EHR computability, surgical complexity, and implementation outcomes. Results will be synthesized descriptively and interpreted using a two-axis framework incorporating surgical complexity and patient-level pain vulnerability to address heterogeneity across surgical populations.

    Expected Outputs: An EHR-derivable predictor catalog, prediction model implementability matrix, CDS trigger framework, and risk-stratified care pathway prototype to inform institutional EHR design at UCLA Health.

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