Serial real-time RT-PCR and serology measurements substantially improve Zika and Dengue virus infection classification in a co-circulation area

Abstract

Background: Real-time RT-PCR (Reverse Transcriptase Polymerase Chain Reaction) is considered the gold standard for Zika virus (ZIKV) infection diagnosis, despite its low sensitivity. Diagnosis using recommended serologic cutoffs in co-circulating Flaviviruses areas maybe inadequate due to in-vitro cross-reactivities of Flaviviruses-specific antibodies. We evaluated Zika diagnosis in symptomatic patients using serial RT-PCR and develop a classification model using serial Dengue virus (DENV) and ZIKV serologies.

Methods: A prospective longitudinal multicentric study in Southern Mexico (NCT02831699) enrolled symptomatic and non-symptomatic participants. In the classification model, true positives were symptomatic (using a modified World Health Organization/Pan American Health Organization definition) with RT-PCR positive for ZIKV or DENV. True negatives were non-symptomatic with negative RT-PCR. Serial serology measurements were used to predict disease status.

Results: Analyzing ZIKV and DENV RT-PCR at 3 timepoints between days 3 and 13 of symptom onset detected 25% more cases than a single RT-PCR analysis between day 0 and 6. When considering sensitivity and specificity together, the serial serology model predicted all categories of disease and negatives better than manufactures cutoffs. Their cutoffs optimized sensitivity or specificity but not both.

Conclusions: We demonstrated the importance of serial RT-PCR and antibody measurements to diagnose arbovirus infection in symptomatic patients living in regions with co-circulating flaviviruses.

Keywords: Antibodies; Dengue virus; Kinetics; Reverse transcriptase polymerase chain reaction; Viremia; Zika virus.

Conflict of interest statement

Conflicts of interest:

Pablo F Belaunzarán-Zamudio has received non-financial support as consultant for Sanofi-Pasteur and funding for projects not related to this work. The other authors, Aurelie Gouel-Cheron, Keith Lumbard, Sally Hunsberger, Fernando J. Arteaga-Cabello, John Beigel, Pablo F. Belaunzarán-Zamudio, Sandra Caballero-Sosa, Kenia Escobedo-López, Violeta Ibarra-González, Gabriel Nájera-Cancino, Héctor Rincón-León, Emilia Ruiz-Hernández, Karina Trujillo-Murillo, have no conflicts of interest to declare.

Copyright © 2019 Elsevier B.V. All rights reserved.

Figures

Figure 1.

Consort diagram. Green boxes represent groups included in our analysis. * : only included those subjects who have no missing serology data on both SV0 and 28, and at most one missing PCR measurement. FU = Follow-Up. GBS = Guillain Barre Syndrom.

Figure 2.

Spaghetti plots for ZIKV and DENV sIgG and sIgM among the three cohorts: ZIKV-sIgG (row 1), ZIKV-sIgM (row 2), DENV-sIgG (row 3) and DENV-sIgM (row 4) by study visit for each cohort: Definitive Dengue, Definitive Zika and Household. Each colored line represents the trajectory of one subject in the study. The horizontal black line represents the manufacturer-recommended cutoff for defining seropositivity.

Figure 3:

Decision Tree results. In the flow diagram each level shows the decision criteria and variable to use to predict the disease group. The colored figures show the criteria diagrammatically. The left figure shows the cut points for the initial cutpoint of low DENV-sIgG SV 28. The purple area shows prediction of household patients with ZIKV-sIgG high and the red area shows the prediction of DENV infected patients. The dots show the true disease status of the people who would fall in the purple and red areas. Red dots are true DENV infected patients, green triangles are true ZIKV infected patients and purple squares are Household subjects. The figure on the right is for the initial cutpoint of high DENV-sIgG SV 28. The green area shows prediction of ZIKV infected patients with ZIKV-sIgM change high and the red area shows the prediction of DENV infected patients.