Clinical symptoms of dengue infection among patients from a non-endemic area and potential for a predictive model: A multiple logistic regression analysis and decision tree

Under-recognition of dengue infection may lead to increased morbidity and mortality, whereas early detection is shown to help improve patient outcomes. Recent incidence and outbreak reports of dengue virus in the United States and other temperate regions where dengue was not typically seen have raised concerns regarding appropriate diagnosis and management by healthcare providers unfamiliar with the disease. This study aimed to describe self-reported clinical symptoms of dengue fever in a non-endemic cohort and to establish a clinically useful predictive algorithm based on presenting features that can assist in the early evaluation of potential dengue infection. Volunteers who experienced febrile illness while traveling in dengue-endemic countries were recruited for this study. History of illness and blood samples were collected at enrollment. Participants were classified as dengue naive or dengue exposed based on neutralizing antibody titers. Statistical analysis was performed to compare characteristics between the two groups. A regression model including joint/muscle/bone pain, rash, dyspnea, and rhinorrhea predicts dengue infection with 78% sensitivity, 63% specificity, 80% positive predictive value, and 61% negative predictive value. A decision tree model including joint/muscle/bone pain, dyspnea, and rash yields 77% sensitivity and 67% specificity. Diagnosis of dengue fever is challenging because of the nonspecific nature of clinical presentation. A sensitive predicting model can be helpful to triage suspected dengue infection in the non-endemic setting, but specificity requires additional testing including laboratory evaluation.