Data Quality Assurance Tool for the Acute to Chronic Pain Signatures Study (A2CPS): An Interactive R Shiny Application.

Background/Aims: Clinical trials and observational studies support the synthesis and development of clinical guidelines, highlighting the need for strong data quality assurance measures. The Acute to Chronic Pain Signatures (A2CPS) program is a large-scale, multi-site observational study investigating chronic post-surgical pain and opioid dependence. Its primary goal is to identify biomarkers predictive of progression from acute to chronic pain following knee arthroplasty or thoracic surgery. The A2CPS sites collect data across various domains, including brain magnetic resonance imaging, electronic health records, psychosocial measures, multi-omics, Quantitative Sensory testing, and functional testing.While A2CPS is an observational study, its aims, design, and methodology closely align with clinical trial practices. This includes interdisciplinary collaboration, standardized protocols, defined eligibility criteria, and oversight by a Data and Safety Monitoring Committee.In multifaceted studies like A2CPS, high-quality data are paramount to ensure the accuracy of predictive biomarkers. To improve quality assurance, we developed the A2CPS Data Monitoring Web Application (Web App), an interactive R Shiny web app with real-time data monitoring capabilities. Here, we describe the functionality and utility of the A2CPS Data Monitoring Web App in streamlining quality assurance for the A2CPS study. Methods: The Web App is a secure R Shiny web application accessible to authorized A2CPS Data Integration and Resource Center (DIRC) members. It retrieves and preprocesses data from REDCap, which is then fed into the R Shiny framework. The user interface has a navigation bar and six subpanels, providing easy access to the app's modules and enabling users to switch seamlessly among subpanels. Each subpanel addresses a specific use case and has the functionality to generate downloadable error reports for individual sites, making it easy to share quality documents and communicate with data collection sites. The DIRC uses these reports to identify errors, coordinate remediation, and facilitate targeted training for research personnel. Results: Regular use of the Web App, coupled with engagement with the training team, resulted in an overall reduction of 50% in data quality errors over one year in case report form data (i.e., in-person visit data). The decline in errors was consistent across all sites despite steady enrollment rates, indicating that real-time data monitoring enables focused feedback, mitigates recurring errors, and streamlines data quality assurance. Conclusion: The A2CPS Data Monitoring Web App plays a key role in A2CPS data quality assurance. This robust open-source solution reduces data entry errors and provides targeted feedback and training to the data collection sites. Our results demonstrate the potential for using open-source computational frameworks for data monitoring and quality assurance purposes in both clinical trials and observational studies.