Diagnostic algorithm for detecting chronic obstructive pulmonary disease in primary care

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Abstract

Background. This article aims to implement an adaptation of the international standardized questionnaires, to assess the diagnostic value of their Russian-language version for diagnostics of chronic obstructive pulmonary disease (COPD), to develop the diagnostic algorithm based on questionnaires and spirometry results in order to form COPD high risk group in primary care.

Materials and methods. The main results were based on the RESPECT study (RESearch on the PrEvalence and the diagnosis of COPD and its Tobacco-related etiology) population of St. Petersburg.

Results. The repeatability of Russian-language version of investigated questionnaires was significantly high.

Conclusion. A new diagnostic algorithm was developed to identify individuals at high risk of COPD and could be recommended for primary health care.

About the authors

K. V. Ovakimyan

North-Western State Medical University named after I.I. Mechnikov

Author for correspondence.
Email: ursa-alba@yandex.ru
Russian Federation, Saint Petersburg

M. A. Pokhaznikova

North-Western State Medical University named after I.I. Mechnikov

Email: ursa-alba@yandex.ru
Russian Federation, Saint Petersburg

E. A. Andreeva

Northern State Medical University

Email: ursa-alba@yandex.ru
Russian Federation, Arkhangelsk

O. Yu. Kutznetsova

North-Western State Medical University named after I.I. Mechnikov

Email: ursa-alba@yandex.ru
Russian Federation, Saint Petersburg

S. L. Plavinskij

North-Western State Medical University named after I.I. Mechnikov

Email: ursa-alba@yandex.ru
Russian Federation, Saint Petersburg

References

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Supplementary files

Supplementary Files
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2. Fig. 1. The main stages of study questionnaire 1 and questionnaire 2

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3. Fig. 2. ROC-curve of diagnostic ability of the logistic regression model of the probability of having COPD (for questionnaire 2), n = 1413

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4. Fig. 3. ROC-curve of diagnostic ability of the logistic regression model of the probability of having COPD (for questionnaire 3), n = 1413

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5. Fig. 4. ROC-curve of diagnostic ability of the logistic regression model of the probability of having COPD (for questionnaire 3), n = 694

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6. Fig. 5. Diagnostic algorithm for preliminary diagnosis of COPD in the stage of primary care

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Copyright (c) 2018 Ovakimyan K.V., Pokhaznikova M.A., Andreeva E.A., Kutznetsova O.Y., Plavinskij S.L.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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