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COPD. 2005 Jun;2(2):225-32.

Development of a population-based screening questionnaire for COPD.

Author information

1
University of Liverpool, Liverpool, L9 7AL, UK. pmacal@liverpool.ac.uk

Abstract

COPD is commonly under-diagnosed, in part because people at risk are unaware of the relevant risk factors and do not recognize related symptoms. Providing this information might permit earlier disease identification but the questions chosen should identify those with spirometrically defined airflow obstruction. Using a population-based data set, we have determined which questions identify persons most likely to have airflow obstruction. Potential questions were selected by review of COPD risk factors and clinical features. Validation was by retrospective analysis of the NHANES III data set, a population-based U.S. household survey that included spirometry. We examined the predictive ability of individual questions in a multi-variate framework to correctly discriminate between persons with and without spirometric airway obstruction (defined as FEV1/FVC < 0.70). We then tested the discriminatory ability of the questions in combination. The following items showed significant predictive ability: increased age, smoking status, pack-years, cough, wheeze, and prior diagnosis of asthma or COPD. The best performing combination was age, smoking status, pack-years smoked, wheeze, phlegm, body mass index, and prior diagnosis of obstructive lung disease. Using this combination in a population of current and former smokers aged 40 and over, we achieved a sensitivity of 85% and specificity of 45%, with a positive predictive value of 38% and a negative predictive value of 88%. Performance of this tool is comparable to other screening methods designed for use in a general population. Symptom-based questionnaires can be a viable method to identify persons likely to have COPD in the general population. Dissemination of such tools should raise awareness among at-risk persons and help identify COPD patients in the primary care setting.

PMID:
17136949
[Indexed for MEDLINE]

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