Development and Validation of Predictive Models for Transition from Acute to Persistent Pain after Major Surgery
Abstract
The transition from acute to pathological persistent pain is complex and is dependent on multiple biological, psychological, and social-environmental factors that change across the surgical care continuum. Current approaches for predicting PPSP are primarily based on risk factors assessed at a single time point, most often - preoperatively. Moving beyond a one-time baseline assessment to a multifactorial and temporal measurement approach is a relatively unexplored research avenue that has a substantial potential. A temporal approach, accounting for multiple factors across the care continuum can afford opportunities for ascertaining the impact of time-varying patient and clinical events across the surgical care continuum. Our central hypothesis is that advanced machine learning models that account for individual biological, cognitive, and psychological factors across the surgical care continuum will allow personalized prediction of PPSP. In this context, pragmatic prediction models for precise PPSP prediction will allow appropriate resource allocation in mitigating PPSP and long-term disability in high-risk individuals. From a research standpoint, such models will allow the efficient testing of perioperative interventions or rehabilitation programs, by implementing appropriate risk stratification to improve assay sensitivity in future clinical trials. We have two specific aims: Aim 1: Collect longitudinal prospective data for a comprehensive biological, psychological, cognitive, and psychophysical characterization of a surgical patient cohort. Aim 2: Develop, validate, and test advanced machine learning models for predicting PPSP.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2022
- Accession Number
- AD1191196
Entities
People
- Simon Haroutounian
Organizations
- Washington University in St. Louis