233: Electronic Health Records Acquisition and Processing in the Acute to Chronic Pain Signatures Study

Topic: Clinical Science
Subtopic: Pain category (ICD-11), Chronic postsurgical pain, Assessment and Measurement of Pain/Nociception, Digital Health Technologies , Treatment/management

Background and Aims: The Acute to Chronic Pain Signatures (A2CPS) study is a large, multisite, longitudinal observational project aimed at identifying biomarkers predictive of the transition from acute to chronic pain following total knee arthroplasty and thoracic surgery (n=2400). A key component of the study is the integration of Electronic Health Record (EHR) data collected from seven healthcare systems across two Multisite Clinical Centers.

Methods: EHR data encompassing clinical characteristics, surgical details, medication history, laboratory findings, pain assessments, and comorbidity profiles are abstracted, harmonized, and quality-checked by a centralized data processing pipeline. This paper details the acquisition, preprocessing, harmonization, and derived variables included in the current A2CPS EHR data release (v2.0, ~500 participants). Processed data include Charlson and Elixhauser comorbidity indices, clinical condition groupings based on ICD codes, rural-urban commuting area (RUCA) classifications, and opioid use metrics.

Results: These derivatives support the assessment of perioperative risk factors and enable the comparison of patient-reported outcomes with EHR-based data. To support data interoperability and downstream analyses, the A2CPS EHR data are being aligned with the OMOP Common Data Model. While limitations such as source variability, misclassification, and missing data persist, ongoing quality assurance and harmonization efforts help mitigate these challenges.

Conclusions: Overall, the A2CPS EHR workflow provides a robust, scalable framework for integrating and analyzing multisite clinical data, supporting the study’s goal of uncovering predictive biomarkers of chronic pain and improving understanding of perioperative outcomes.

References: Predicting chronic postsurgical pain: current evidence and a novel program to develop predictive biomarker signatures. PAIN, online June 15, 2023. Kathleen Sluka, Tor Wager, Stephani Sutherland, Patricia Labosky, Tessa Balach et al.

Multi-Site Observational Study to Assess Biomarkers for Susceptibility or Resilience to Chronic Pain: The Acute to Chronic Pain Signatures (A2CPS) Study Protocol. Frontiers in Medicine, April 25, 2022. Giovanni Berardi, Laura Frey-Law, Kathleen Sluka, Emine Bayman, Christopher Coffey et al.

Presenting Author: Stephani Sutherland
Submitting Author: Stephani Sutherland

Senior Author: Robert McCarthy

IASP 2026 Global Year theme of Neuropathic Pain: No

• Briha Ansari: PhD - Johns Hopkins University
• Martin Lindquist: PhD - Johns Hopkins University
• Erik Westlund: PhD - Johns Hopkins University
• Chad M Brummett: MD - University of Michigan
• Douglas Colquhoun: MD - University of Michigan
• Yanxi (Nora) Liu: PhD - Johns Hopkins University
• Robert J. McCarthy: PharmD - Rush University Medical Center
• the A2CPS Consortium - Johns Hopkins University