Development and Validation of a Healthcare Utilization-Based Algorithm to Identify Acute Exacerbations of Chronic Obstructive Pulmonary Disease

• Published in: International journal of chronic obstructive pulmonary disease Vol. 16; pp. 1687 - 1698
• Main Authors: Mapel, Douglas W, Roberts, Melissa H, Sama, Susan, Bobbili, Priyanka J, Cheng, Wendy Y, Duh, Mei Sheng, Nguyen, Catherine, Thompson-Leduc, Philippe, Van Dyke, Melissa K, Rothnie, Kieran J, Sundaresan, Devi, Certa, Julia M, Whiting, Thomas S, Brown, Jennifer L, Roblin, Douglas W
• Format: Journal Article
• Language: English
• Published: London Dove Medical Press Limited 01.01.2021; Dove Medical Press Ltd; Dove; Dove Medical Press
• Subjects: aecopd events; algorithm; Algorithms; Chronic obstructive pulmonary disease; claims database; Consulting services; electronic medical records; Health care policy; Health insurance; Health services utilization; Hospitalization; Insurance coverage; Lung diseases; Lung diseases, Obstructive; Medical care; Medical records; Medical research; Medicine, Experimental; Original Research; Patients; Pharmaceutical industry; predictive values; Spirometry; Tuberculosis; Utilization; validation; United States; United Kingdom; United States > US
• ISSN: 1178-2005; 1176-9106; 1178-2005
• DOI: 10.2147/COPD.S302241

Introduction: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are important events that may precipitate other adverse outcomes. Accurate AECOPD event identification in electronic administrative data is essential for improving population health surveillance and practice management. Objective: Develop codified algorithms to identify moderate and severe AECOPD in two US healthcare systems using administrative data and electronic medical records, and validate their performance by calculating positive predictive value (PPV) and negative predictive value (NPV). Methods: Data from two large regional integrated health systems were used. Eligible patients were identified using International Classification of Diseases (Ninth Edition) COPD diagnosis codes. Two algorithms were developed: one to identify potential moderate AECOPD by selecting outpatient/emergency visits associated with AECOPD-related codes and antibiotic/systemic steroid prescriptions; the other to identify potential severe AECOPD by selecting inpatient visits associated with corresponding codes. Algorithms were validated via patient chart review, adjudicated by a pulmonologist. To estimate PPV, 300 potential moderate AECOPD and 250 potential severe AECOPD events underwent review. To estimate NPV, 200 patients without any AECOPD identified by the algorithms (100 patients each without moderate or severe AECOPD) during the two years following the index date underwent review to identify AECOPD missed by the algorithm (false negatives). Results: The PPVs (95% confidence interval [CI]) for both moderate and severe AECOPD were high: 293/298 (98.3% [96.1-99.5]) and 216/225 (96.0% [92.5-98.2]), respectively. NPV was lower for moderate AECOPD (75.0% [65.3-83.1]) than for severe AECOPD (95.0% [88.7-98.4]). Results were consistent across both healthcare systems. Conclusion: This study developed healthcare utilization-based algorithms to identify moderate and severe AECOPD in two separate healthcare systems. PPV for both algorithms was high; NPV was lower for the moderate algorithm. Replication and consistency of results across two healthcare systems support the external validity of these findings. Keywords: claims database, algorithm, validation, electronic medical records, predictive values, AECOPD events