Diagnosing pneumonia, influenza and obstructive pulmonary diseases in adult patients presenting to primary care with acute cough: a multinomial logistic regression analysis

Background: Community-acquired lower respiratory tract infection (LRTI) is a broad term which describes airways/pulmonary tissue inflammations due to viral and/or bacterial infection, below the level of the larynx. Known infections that can affect the lower respiratory tract, among others, are: pneumonia, exacerbation of chronic obstructive airway disease (e.g. asthma or COPD) and influenza (can affect both the upper and lower respiratory tracts). The objective of this report is to assess the diagnostic value of signs and symptoms and added value of biomarkers, e.g. C-Reactive Protein, of adult patients from 16 primary care networks from 12 European countries who presented to primary care with acute cough for GRACE studies in the diagnosis of pneumonia, influenza and obstructive pulmonary diseases treating the diagnoses in parallel rather than in series. Methods: Missing information on patients diagnosis’ symptoms and signs were imputed using Multiple Imputation Chained Equations. Candidate clinical predictors able to discriminate patients’ condition (i.e. Pneumonia, Influenza, Asthma, COPD or ”Other”) were chosen through Random Forest approach. The contribution to the log odds of a category versus the baseline was then estimated for the selected clinical predictors with a Multinomial Logistic Regression(MNL) model. The diagnostic accuracy and prediction power of the model were assessed for each pair of categories by fitting a Logistic Regression(LR) and estimating the area under each curve (AUC) for each models’ Receiver Operating Characteristic (ROC) Curve. Results: The pairs with the highest AUC were : Influenza when reference is Pneumonia, Asthma when reference is Pneumonia, COPD when reference is Pneumonia and Influenza when reference is Asthma. Influenza when reference is Pneumonia model revealed an AUC value equal to 0.92 i.e. a randomly selected patient from the Influenza group has a predicted probability for influenza larger than that for a randomly chosen patient from the Pneumonia group 92 percent of the time. Retrofitting the same logic, 0.86, 0.79 and 0.85 were the identified AUC values for Asthma when reference is Pneumonia, COPD when reference is Pneumonia and Influenza when reference is Asthma respectively. The added diagnostic value of CRP was quantified by fitting the logistic regression models without CRP and calculating the AUC value of their ROC curve. All models where CRP was significant (i.e. Influenza when reference is Pneumonia , Asthma when reference is Pneumonia and COPD when reference is Pneumonia) had a lower AUC value after we omitted CRP. The difference proved insignificant. Conclusions: Based only on symptoms and signs taken into consideration in this report, we can conclude that differentiating between: pneumonia, influenza when reference is other diagnoses as well as between each pair diagnosis (Influenza, Asthma or COPD when reference is Pneumonia and Influenza when reference is Asthma) at first day of patient consultation, can be adequately accurate.