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

Abstract

IntroductionAcute 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.ObjectiveDevelop 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).MethodsData 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).ResultsThe 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.ConclusionThis 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.