Identifying viral infections in vaccinated Chronic Obstructive Pulmonary Disease (COPD) patients using clinical features and inflammatory markers

Abstract

Background: Known inflammatory markers have limited sensitivity and specificity to differentiate viral respiratory tract infections from other causes of acute exacerbation of COPD (AECOPD). To overcome this, we developed a multi-factorial prediction model combining viral symptoms with inflammatory markers.

Methods: Interleukin-6 (IL-6), serum amyloid A (SAA) and viral symptoms were measured in stable COPD and at AECOPD onset and compared with the viral detection rates on multiplex PCR. The predictive accuracy of each measure was assessed using logistic regression and receiver operating characteristics curve (ROC) analysis.

Results: There was a total of 33 viruses detected at the onset of 148 AECOPD, the majority 26 (79%) were picornavirus. Viral symptoms with the highest predictive values were rhinorrhoea [Odds ratio (OR) 4.52; 95% CI 1.99-10.29; P < 0.001] and sore throat (OR 2.64; 95% CI 1.14-6.08; P = 0.022), combined the AUC ROC curve was 0.67. At AECOPD onset patients experienced a 1.6-fold increase in IL-6 (P = 0.008) and 4.5-fold increase in SAA (P < 0.001). The addition of IL-6 to the above model significantly improved diagnostic accuracy compared with symptoms alone (AUC ROC 0.80 (P = 0.012).

Conclusion: The addition of inflammatory markers increases the specificity of a clinical case definition for viral infection, particularly picornavirus infection.

Figure 1

Viral detection rates per season; detection by PCR versus case definition. Y‐axis indicates the number of respiratory viral infections identified, the X‐axis indicates the season and year in which the viral AECOPD were identified. The pale blue bars indicate positive for viral infection according to a clinical case definition and the dark blue bars indicate viral infection defined by positive multiplex respiratory PCR. +ve: positive; total CV+ve: total clinical viral symptom positive.

Figure 2

Diagnostic accuracy of models to identify respiratory viral infection. The blue scale displays the AUC ROC for the clinical model to discriminate viral infection defined by positive multiplex PCR (AUC ROC 0·67; 95% CI 0·52–0·83) and the red scale displays the AUC ROC for clinical model combined with IL‐6 (AUC ROC 0·80; 95% CI 0·70–0·91). The diagnostic accuracy of the clinical prediction model was significantly increased by the addition of IL‐6 (P = 0·012).

Figure 3

ROC models to discriminate respiratory viral infection. The blue scale displays the AUC ROC for the combination of the clinical model, IL‐6 and SAA to discriminate viral infection defined by positive multiplex PCR (AUC ROC 0·82; 95% CI 0·72–0·91) and the red scale displays the AUC ROC for IL‐6 & SAA combined (AUC ROC 0·68; 95% CI 0·54–0·82). This figure demonstrates that the diagnostic accuracy for discriminating viral from non‐viral events is significantly increased by the addition of the clinical case definition (P = 0·048). The diagonal line across the centre of ROC space indicates the line of no effect or the point at which the test does not add any additional diagnostic information (equivalent to AUC ROC of 0·50). The reference test for viral infection is defined by positive multiplex PCR.