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Multivariate time-series analysis of biomarkers from a dengue cohort offers new approaches for diagnosis and prognosis.
Vasey, Baptiste; Shankar, Anuraj H; Herrera, Bobby Brooke; Becerra, Aniuska; Xhaja, Kris; Echenagucia, Marion; Machado, Sara R; Caicedo, Diana; Miller, John; Amedeo, Paolo; Naumova, Elena N; Bosch, Irene.
Afiliação
  • Vasey B; Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom.
  • Shankar AH; Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom.
  • Herrera BB; E25Bio Inc., Cambridge, Massachusetts, United States of America.
  • Becerra A; Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
  • Xhaja K; Center for Infectious Diseases and Vaccine Research, University of Massachusetts, Worcester, Massachusetts, United States of America.
  • Echenagucia M; Center for Infectious Diseases and Vaccine Research, University of Massachusetts, Worcester, Massachusetts, United States of America.
  • Machado SR; Centro Nacional de Hemofilia at Banco Municipal de Sangre, Universidad Central de Venezuela, Caracas, Venezuela.
  • Caicedo D; Department of Health Policy, London School of Economics, London, United Kingdom.
  • Miller J; Pontificia Universidad Javeriana, Cali, Colombia.
  • Amedeo P; J. Craig Venter Institute, La Jolla, California, United States of America.
  • Naumova EN; J. Craig Venter Institute, La Jolla, California, United States of America.
  • Bosch I; Friedman School of Nutrition Science and Policy, Tufts University, Boston, Massachusetts, United States of America.
PLoS Negl Trop Dis ; 14(6): e0008199, 2020 06.
Article em En | MEDLINE | ID: mdl-32544159
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Biomarcadores / Dengue / Testes Diagnósticos de Rotina Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies Limite: Adolescent / Adult / Aged / Child / Child, preschool / Female / Humans / Male / Middle aged País/Região como assunto: America do sul / Venezuela Idioma: En Revista: PLoS Negl Trop Dis Assunto da revista: MEDICINA TROPICAL Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Reino Unido País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Biomarcadores / Dengue / Testes Diagnósticos de Rotina Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies Limite: Adolescent / Adult / Aged / Child / Child, preschool / Female / Humans / Male / Middle aged País/Região como assunto: America do sul / Venezuela Idioma: En Revista: PLoS Negl Trop Dis Assunto da revista: MEDICINA TROPICAL Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Reino Unido País de publicação: Estados Unidos