• Dendy Macaulay, Shawn X Sun, Rachael A Sorg, Sherry Y Yan, Gourab De, Eric Q Wu, and Paul F Simonelli.
• Analysis Group, Inc., New York, NY, USA. Electronic address: dmacaulay@analysisgroup.com.
• Respir Med. 2013 Oct 1; 107 (10): 1568-77.

BackgroundAdministrative claims are an important data source for COPD research but lack a validated measure of patient COPD severity, which is an important determinant of treatment and outcomes.MethodsPatients with ≥1 diagnosis of COPD and spirometry results from 01/2004-05/2011 were identified from an electronic health records database linked to healthcare claims. Patients were classified into 3 COPD severity groups based on spirometry and Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines: GOLD-Unclassified, Mild/Moderate, and Severe/Very Severe. A multinomial logistic regression model was constructed using claims data from 3 months before and after (observation period) the most recent spirometry (index date) to categorize patient COPD severity. A random selection of 90% of patients in each severity level was selected to build the model, and the remaining 10% were used as a validation sample. Model predictions were evaluated for sensitivity, specificity, accuracy, and concordance.ResultsAmong 2028 COPD patients who met sample selection criteria, 886, 683, and 459 patients were in the GOLD-Unclassified, Mild/Moderate, and Severe/Very Severe categories, respectively. The final model included age, sex, comorbidities (such as pulmonary fibrosis and diabetes), COPD-related resource utilization (such as oxygen use), and all-cause healthcare utilization. In the validation sample, the model correctly predicted COPD severity for 62.7% of all patients (accuracy for predicting GOLD-Unclassified: 73.5%; Mild/Moderate: 70.6%; Severe/Very Severe: 81.4%) with kappa = 0.41.ConclusionsThe prediction model was developed using clinically measured COPD severity to provide researchers an approach to classify patients using claims data when clinical measures are not available.Copyright © 2013 Elsevier Ltd. All rights reserved.

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