Validation of algorithms to identify elective percutaneous coronary interventions in administrative databases

• Published in: PloS one Vol. 15; no. 4; p. e0231100
• Main Authors: Derington, Catherine G., Heath, Lauren J., Kao, David P., Delate, Thomas
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.04.2020; Public Library of Science (PLoS)
• Subjects: Adolescent; Adult; Aged; Algorithms; Angina pectoris; Angioplasty; Balloon angioplasty; Cardiology; Codes; Computer and Information Sciences; Confidence intervals; Coronary Angiography; Databases, Factual; Diagnostic systems; Elective Surgical Procedures - statistics & numerical data; Emergency medical care; Emergency medical services; Emergency procedures; Exercise Test - methods; Female; Health care delivery; Heart attacks; Heart Diseases - diagnostic imaging; Heart Diseases - epidemiology; Heart Diseases - pathology; Heart Diseases - surgery; Humans; Male; Medical equipment; Medical research; Medicine and Health Sciences; Middle Aged; Patient satisfaction; Patients; Percutaneous Coronary Intervention - statistics & numerical data; Pharmaceutical sciences; Pharmacy; Physical Sciences; Research and Analysis Methods; Retrospective Studies; Sensitivity; Studies; United States > US; Colorado
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0231100

Elective percutaneous coronary interventions (PCI) are difficult to discriminate from non-elective PCI in administrative data due to non-specific encounter codes, limiting the ability to track outcomes, ensure appropriate medical management, and/or perform research on patients who undergo elective PCI. The objective of this study was to assess the abilities of several algorithms to identify elective PCI procedures using administrative data containing diagnostic, utilization, and/or procedural codes. For this retrospective study, administrative databases in an integrated healthcare delivery system were queried between 1/1/2015 and 6/31/2016 to identify patients who had an encounter for a PCI. Using clinical criteria, each encounter was classified via chart review as a valid PCI, then as elective or non-elective. Cases were tested against nine pre-determined algorithms. Performance statistics (sensitivity, specificity, positive predictive value, and negative predictive value) and associated 95% confidence intervals (CI) were calculated. Of 521 PCI encounters reviewed, 497 were valid PCI, 93 of which were elective. An algorithm that excluded emergency room visit events had the highest sensitivity (97.9%, 95%CI 92.5%-99.7%) while an algorithm that included events occurring within 90 days of a cardiologist visit and coronary angiogram or stress test had the highest positive predictive value (62.2%, 95%CI 50.8%-72.7%). Without an encounter code specific for elective PCI, an algorithm excluding procedures associated with an emergency room visit had the highest sensitivity to identify elective PCI. This offers a reasonable approach to identify elective PCI from administrative data.