Abstract
Background: Low- and middle-income countries (LMICs) account for > 90% of deaths and illness episodes related to COPD; however, this condition is commonly underdiagnosed in these settings. Case-finding instruments for COPD may improve diagnosis and identify individuals that need treatment, but few have been validated in resource-limited settings.
Methods: We conducted a population-based cross-sectional study in Uganda to assess the diagnostic accuracy of a respiratory symptom, exposure and functional questionnaire in combination with peak expiratory flow for COPD diagnosis using post-bronchodilator FEV1/FVC z-score below the 5th percentile as the gold standard. We included locally relevant exposure questions and statistical learning techniques to identify the most important risk factors for COPD. We used 80% of the data to develop the case-finding instrument and validated it in the remaining 20%. We evaluated for calibration and discrimination using standard approaches. The final score, COLA (COPD in LMICs Assessment), included seven questions, age and pre-bronchodilator peak expiratory flow.
Results: We analyzed data from 1,173 participants (average age 47 years, 46.9% male, 4.5% with COPD) with acceptable and reproducible spirometry. The seven questions yielded a cross-validated area-under-the-curve [AUC] of 0.68 (95% CI 0.61- 0.75) with higher scores conferring greater odds of COPD. The inclusion of peak expiratory flow and age improved prediction in a validation sample (AUC=0.83, 95% CI 0.78- 0.88) with a positive predictive value of 50% and a negative predictive value of 96%. The final instrument (COLA) included seven questions, age and pre-bronchodilator peak expiratory flow.
Conclusion: COLA predicted COPD in urban and rural settings in Uganda has high calibration and discrimination, and could serve as a simple, low-cost screening tool in resource-limited settings.
