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Mathematical model to predict the risk of future exacerbations in non-smoking patients with COPD
PDPI Sumatera Utara, 28 Mar 2018 14:38:10

Aliaksei Kadushkin1 and Anatoli Tahanovich1
1Biochemistry, Belarusian State Medical University, Minsk, Belarus


Background: The majority of developed prediction models for future exacerbations included smoking patients with COPD, but they may be not representative for non-smoking COPD patients.

Aim: To develop criteria for assessing risk of frequent exacerbations in non-smoking patients with COPD.

Methods: Twenty eight inflammatory biomarkers in peripheral blood, including lymphocytes subpopulations, cytokines, chemokines, immunoglobulins, acute-phase proteins, and eight clinical parameters were quantified in 42 non-smokers with COPD. The validation cohort consisted of 20 non-smoking patients with COPD. We defined non-smokers as subjects who smoked less than 100 cigarettes in their lifetime. Frequent exacerbations were determined as two or more exacerbations. Patients with infrequent exacerbations were defined as those who had no or one exacerbation in a year. Logistic regression analysis was used to develop the prediction model.

Read More : Association between lung function decline and occupational exposures in patients with COPD

The final mathematical model to predict the frequency of exacerbations during 12 months after the examinationin non-smoking patients with COPD included three variables: CAT (COPD Assessment Test) score and plasma concentrations of vascular endothelial growth factor and C-reactive protein. The developed model had a sensitivity of 85.0%, specificity of 81.0%, accuracy of 82.9%, positive predictive value (PPV) of 81.0% and negative predictive value (NPV) of 85.0%.Sensitivity, specificity, accuracy, PPV and NPV of proposed model in the validation cohort were 83.3%, 78.6%, 80.0%, 62.5% and 91.7% respectively.

Conclusion: The proposed model to assess the risk of future COPD exacerbations is robust and can be easily applied to individual non-smoking patients.

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