Seasonal Influenza- Factors Associated with a Severe Clinical Form of the Illness

Abstract

Abstract Introduction. The risk factors associated with the progression of a severe clinical form of seasonal influenza are of a particular importance in developing a current and accurate decision in terms of treatment options. Aim. The aim of the study was to identify the specific factors associated with a severe form of seasonal influenza. Method. The study was conducted as a prospective, group comparison at the University Clinic for Infectious Diseases in Skopje, Macedonia, during the period of January 01, 2012, until January 01, 2015. This study analyzed 122 adult patients, who were clinically-confirmed to be infected with seasonal influenza by laboratory analyses and other necessary tests. These patients were grouped into two categories: patients with a mild form of seasonal influenza, and patients with a severe form of seasonal influenza. Furthermore, the demographic, clinical, and biochemical results obtained were analyzed. The variables in the univariable analysis which were significantly associated with a severe form of seasonal influenza were included in the multivariable logistic regression analysis in order to extract and determine the independent predicttors of a severe form of seasonal influenza. Results. The multivariable analysis yielded cardiovascular diseases (p=0.01), dyspnea (p=0.001), tachypneа >20 respiration/ minute (p=0.005), values of LDH greater than 618 U/L (p=0.048) and SAPS 2score (p=0.031) as independent variables which predict the severity of the illness. The area under the ROC curve [0.826 (95% CI)] suggests that the probability of a severe form of influenza was82.6%. The global accuracy for this model to predict a severe form of influenza was 81.1%, with the sensitivity being 88.5%, and the specificity 72.9%. Conclusion. Cardiovascular diseases, dyspnea, tachypnea, elevated levels of LDH and SAPS 2 score are independent predictive indicators for severe influenza. Early identification of these indicators will allow implementation of adequate medical intervention which will in turn reduce mortality rates.