Establishment of a Predictive Model Related to Pathogen Invasion for Infectious Diseases and Its Diagnostic Value in Fever of Unknown Origin.

The present study aimed to establish a list of parameters indicative of pathogen invasion and develop a predictive model to distinguish the etiologies of fever of unknown origin (FUO) into infectious and non-infectious causes. From January 2014 to September 2017, 431 patients with FUO were prospectively enrolled in the study population. This study established a list of 26 variables from the following 4 aspects: host factors, epidemiological factors, behavioral factors, and iatrogenic factors. Predefined predicted variables were included in a multivariate logistic regression analysis to develop a predictive model. The predictive model and the corresponding scoring system were developed using data from the confirmed diagnoses and 9 variables were eventually identified. These factors were incorporated into the predictive model. This model discriminated between infectious and non-infectious causes of FUO with an AUC of 0.72, sensitivity of 0.71, and specificity of 0.63. The predictive model and corresponding scoring system based on factors concerning pathogen invasion appear to be reliable screening tools to discriminate between infectious and non-infectious causes of FUO.