Multivariate time-series analysis of biomarkers from a dengue cohort offers new approaches for diagnosis and prognosis.

Dengue is a major public health problem worldwide with distinct clinical manifestations: an acute presentation (dengue fever, DF) similar to other febrile illnesses (OFI) and a more severe, life-threatening form (severe dengue, SD). Due to nonspecific clinical presentation during the early phase of dengue infection, differentiating DF from OFI has remained a challenge, and current methods to determine severity of dengue remain poor early predictors. We present a prospective clinical cohort study conducted in Caracas, Venezuela from 2001–2005, designed to determine whether clinical and hematological parameters could distinguish DF from OFI, and identify early prognostic biomarkers of SD. From 204 enrolled suspected dengue patients, there were 111 confirmed dengue cases. Piecewise mixed effects regression and nonparametric statistics were used to analyze longitudinal records. Decreased serum albumin and fibrinogen along with increased D-dimer, thrombin-antithrombin complex, activated partial thromboplastin time and thrombin time were prognostic of SD on the day of defervescence. In the febrile phase, the day-to-day rates of change in serum albumin and fibrinogen concentration, along with platelet counts, were significantly decreased in dengue patients compared to OFI, while the day-to-day rates of change of lymphocytes (%) and thrombin time were increased. In dengue patients, the absolute lymphocytes to neutrophils ratio showed specific temporal increase, enabling classification of dengue patients entering the critical phase with an area under the ROC curve of 0.79. Secondary dengue patients had elongation of Thrombin time compared to primary cases while the D-dimer formation (fibrinolysis marker) remained always lower for secondary compared to primary cases. Based on partial analysis of 31 viral complete genomes, a high frequency of C-to-T transitions located at the third codon position was observed, suggesting deamination events with five major hot spots of amino acid polymorphic sites outside in non-structural proteins. No association of severe outcome was statistically significant for any of the five major polymorphic sites found. This study offers an improved understanding of dengue hemostasis and a novel way of approaching dengue diagnosis and disease prognosis using piecewise mixed effect regression modeling. It also suggests that a better discrimination of the day of disease can improve the diagnostic and prognostic classification power of clinical variables using ROC curve analysis. The piecewise mixed effect regression model corroborated key early clinical determinants of disease, and offers a time-series approach for future vaccine and pathogenesis clinical studies. Author summary: Dengue fever results in a self-limiting, non-specific febrile illness. In approximately 10% of cases, the disease progresses to a severe, life-threatening syndrome. While hematological derangement is a key indicator of dengue, the mechanisms by which pathophysiological changes occur over the course of infection remain unclear. Additionally, there are limited clinical algorithms to facilitate rapid prognosis of dengue. We conducted a prospective clinical cohort study in Caracas, Venezuela to determine whether clinical and hematological parameters could distinguish dengue fever from other febrile illnesses, and identify early prognostic biomarkers of severe disease. Piecewise linear mixed effects regression models demonstrate that rates of change of albumin, fibrinogen, lymphocytes, platelets and thrombin time were significantly different between dengue and other febrile illnesses, and that the absolute value of albumin, fibrinogen, thrombin-antithrombin complex, thrombin time and partial thromboplastin time were prognostic of severe dengue on the day of defervescence. Our study offers extended insights into dengue pathogenesis and provides new approaches to dengue diagnosis and severity prognosis. [ABSTRACT FROM AUTHOR]