The Accuracy of PUMA Questionnaire in Combination With Peak Expiratory Flow Rate to Identify At-risk, Undiagnosed COPD Patients

Abstract

Introduction

The English PUMA questionnaire emerges as an effective COPD case-finding tool. We aimed to use the PUMA questionnaire in combination with peak expiratory flow rate (PEFR) to improve case-finding efficacy in Chinese population.

Methods

This cross-sectional, observational study included two stages: translating English to Chinese PUMA (C-PUMA) questionnaire with linguistic validation and psychometric evaluation, followed by clinical validation. Eligible participants (with age ≥40 years, respiratory symptoms, smoking history ≥10 pack-years) were enrolled and subjected to three questionnaires (C-PUMA, COPD assessment test [CAT], and generic health survey [SF-12V2]), PEFR measurement, and confirmatory spirometry. The C-PUMA score and PEFR were incorporated into a PUMA-PEFR prediction model applying binary logistic regression coefficients to estimate the probability of COPD (P_COPD).

Results

C-PUMA was finalized through standard forward–backward translation processes and confirmation of good readability, comprehensibility, and reliability. In clinical validation, 240 participants completed the study. 78/240 (32.5%) were diagnosed with COPD. C-PUMA exhibited significant validity (correlated with CAT or physical component scores of SF-12V2, both P < 0.05, respectively). PUMA-PEFR model had higher diagnostic accuracy than C-PUMA alone (area under ROC curve, 0.893 vs. 0.749, P < 0.05). The best cutoff values of C-PUMA and PUMA-PEFR model (P_COPD) were ≥6 and ≥0.39, accounting for a sensitivity/specificity/numbers needed to screen of 77%/64%/3 and 79%/88%/2, respectively. C-PUMA ≥5 detected more underdiagnosed patients, up to 11.5% (vs. C-PUMA ≥6).

Conclusion

C-PUMA is well-validated. The PUMA-PEFR model provides more accurate and cost-effective case-finding efficacy than C-PUMA alone in at-risk, undiagnosed COPD patients. These tools can be useful to detect COPD early.