Extracting lung function measurements to enhance phenotyping of chronic obstructive pulmonary disease (COPD) in an electronic health record using automated tools.

Abstract

Chronic obstructive pulmonary disease (COPD) is associated with poor quality of life, hospitalization and mortality. COPD phenotype includes using pulmonary function tests to determine airflow obstruction from the forced expiratory volume in one second (FEV1):forced vital capacity. FEV1 is a commonly used value for severity but is difficult to identify in structured electronic health record (EHR) data. Using the Microsoft SQL Server's full-text search feature and string functions supporting regular-expression-like operations, we developed an automated tool to extract FEV1 values from progress notes to improve ascertainment of FEV1 in EHR in the Veterans Aging Cohort Study (VACS). The automated tool increased quantifiable FEV1 values from 12,425 to 16,274 (24% increase in numeric FEV1). Using chart review as the reference, positive predictive value of the tool was 99% (95% Confidence interval: 98.2-100.0%) for identifying quantifiable FEV1 values and a recall value of 100%, yielding an F-measure of 0.99. The tool correctly identified FEV1 measurements in 95% of cases. A SQL-based full text search of clinical notes for quantifiable FEV1 is efficient and improves the number of values available in VA data. Future work will examine how these methods can improve phenotyping of patients with COPD in the VA.