Cath Lab & PCI Indications - Do They Have to Match? 2025 Quality Summit Presentation (Non-CE)-Cath Lab & PCI Indications

Video Transcription

Video Summary

In this session, Connie Anderson and Patricia Blake from Ohio State University discuss challenges and best practices in coding cardiac catheterization (cath lab) and percutaneous coronary intervention (PCI) indications for registry data, emphasizing that cath lab and PCI indications do not always have to match. They address common difficulties, especially for new abstractors, due to complex definitions and extensive data collection forms. Using real-world examples, they highlight the importance of accurate pre-procedure documentation, collaboration with clinicians, and regular data quality reviews to ensure reliable registry submissions.<br /><br />They stress that cath lab indication is a multi-select field capturing all relevant cardiac conditions prompting the procedure, while PCI indication is single-select for the primary reason. Proper coding impacts quality metrics and appropriate use criteria (AUC) assessments, which help evaluate if a revascularization procedure is justified. They share insights on managing atypical or complex scenarios, such as staged procedures, urgent interventions, and unstable angina in outpatients.<br /><br />The speakers underline the value of understanding AUC scores to identify rarely appropriate cases and the importance of educating clinical teams to improve documentation quality. They advocate ongoing training, inter-rater reliability assessments, and using registry tools for continuous quality improvement. Finally, they highlight the significance of these processes for patient outcomes, institutional reporting, and reimbursement.

Keywords

intravascular imaging

percutaneous coronary intervention

IVUS

OCT

cardiovascular care

plaque assessment

stent placement

non-invasive imaging

clinical guidelines

cardiac catheterization coding

PCI indications

registry data challenges

cath lab multi-select

pre-procedure documentation

appropriate use criteria (AUC)

data quality reviews

clinical collaboration

quality improvement training