2081. Building a Decision Tree with Serial Serology Measurements Improves Classification in a Flavivirus Co-circulation Region

Background RT-PCR (reverse transcriptase polymerase chain reaction) is often considered the “gold standard” for diagnosis of Zika Virus (ZIKV) infection; however, it has been shown to have low sensitivity. A possible remedy is to study ZIKV-specific IgG (ZsIgG) and IgM (ZsIgM) antibodies. However, t...

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Published inOpen forum infectious diseases Vol. 5; no. suppl_1; pp. S608 - S609
Main Authors Lumbard, Keith, Arteaga Cabello, Fernando J, Gouel-Cheron, Aurelie, Belaunzarán, Francisco, Nájera-Cancino, Gabriel, Caballero-Sosa, Sandra, Rincón-León, Héctor, Del Carmen Ruis Hernandez, Emilia, Cervantes, Pilar Ramos, Lourdes Guerrero, M, Beigel, John, Trujillo-Murillo, Karina, Pedraza, Gustavo, Sepulveda, Jesús, Escobedo-Lopez, Kenia Melina, Mora-Suarez, Nora K, Reyes-Romero, Monica, Ibarra-González, Violeta, Marínez-Lopez, Julia, Ruiz-Palacios, Guillermo, Hunsberger, Sally
Format Journal Article
LanguageEnglish
Published Oxford Oxford University Press 26.11.2018
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ISSN2328-8957
2328-8957
DOI10.1093/ofid/ofy210.1737

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Abstract Background RT-PCR (reverse transcriptase polymerase chain reaction) is often considered the “gold standard” for diagnosis of Zika Virus (ZIKV) infection; however, it has been shown to have low sensitivity. A possible remedy is to study ZIKV-specific IgG (ZsIgG) and IgM (ZsIgM) antibodies. However, the in vitro cross-reactivities of Dengue virus (DENV) and ZIKV-specific antibodies are well known, leading to diagnostic difficulties in an area with co-circulation of the two viruses. Our goal was to use Zika and Dengue serologic assays to build a classification model that improves upon the PPV of commercial kits while maintaining sensitivity. Methods We conducted a prospective longitudinal study in Southern Mexico where DENV and ZIKV co-circulation occurs (NCT02831699). Patients were included in two cohorts: a cohort of subjects presenting with a febrile rash meeting WHO/PAHO Zika case definition and a household cohort. After signed consent, all subjects enrolled were evaluated on study-visit Days 0, 3 and 7 (for fever rash cohort) and 28. We considered a subject “true positive” for ZIKV or DENV if RT-PCR positive at any time point. The healthy household cohort (with no positive RT-PCR) was considered “true negatives.” We fit a statistical decision tree taking as inputs serial serology measurements and outputting a predicted disease category. Funded in part by the NCI Contract No. HHSN261200800001E. Funded in part by the Mexican Ministry of Health. Results As of March 2018, we have 32 subjects in the Zika PCR+ group, 32 in the Dengue PCR+ group, and 68 in the household group. Our decision tree (Figure 1) achieved PPV of at least 90% on all three disease categories, while maintaining sensitivity above 50%. The highest PPV achieved by the kit manufacturer recommended cutoffs while maintaining a sensitivity of at least 10% on Zika PCR+ subjects is 30/114 (26%), and for Dengue PCR+ subjects is 21/30 (70%). Conclusion Using serology data in a statistical decision tree improves the PPV exhibited by the kit manufacturer recommendations while still maintaining respectable sensitivity. Physicians in regions with co-circulating flaviviruses should be aware of the pitfalls of using only RT-PCR or using pre-established commercial cutoffs in the serology kits for diagnosis. Disclosures All authors: No reported disclosures.
AbstractList Background RT-PCR (reverse transcriptase polymerase chain reaction) is often considered the “gold standard” for diagnosis of Zika Virus (ZIKV) infection; however, it has been shown to have low sensitivity. A possible remedy is to study ZIKV-specific IgG (ZsIgG) and IgM (ZsIgM) antibodies. However, the in vitro cross-reactivities of Dengue virus (DENV) and ZIKV-specific antibodies are well known, leading to diagnostic difficulties in an area with co-circulation of the two viruses. Our goal was to use Zika and Dengue serologic assays to build a classification model that improves upon the PPV of commercial kits while maintaining sensitivity. Methods We conducted a prospective longitudinal study in Southern Mexico where DENV and ZIKV co-circulation occurs (NCT02831699). Patients were included in two cohorts: a cohort of subjects presenting with a febrile rash meeting WHO/PAHO Zika case definition and a household cohort. After signed consent, all subjects enrolled were evaluated on study-visit Days 0, 3 and 7 (for fever rash cohort) and 28. We considered a subject “true positive” for ZIKV or DENV if RT-PCR positive at any time point. The healthy household cohort (with no positive RT-PCR) was considered “true negatives.” We fit a statistical decision tree taking as inputs serial serology measurements and outputting a predicted disease category. Funded in part by the NCI Contract No. HHSN261200800001E. Funded in part by the Mexican Ministry of Health. Results As of March 2018, we have 32 subjects in the Zika PCR+ group, 32 in the Dengue PCR+ group, and 68 in the household group. Our decision tree (Figure 1) achieved PPV of at least 90% on all three disease categories, while maintaining sensitivity above 50%. The highest PPV achieved by the kit manufacturer recommended cutoffs while maintaining a sensitivity of at least 10% on Zika PCR+ subjects is 30/114 (26%), and for Dengue PCR+ subjects is 21/30 (70%). Conclusion Using serology data in a statistical decision tree improves the PPV exhibited by the kit manufacturer recommendations while still maintaining respectable sensitivity. Physicians in regions with co-circulating flaviviruses should be aware of the pitfalls of using only RT-PCR or using pre-established commercial cutoffs in the serology kits for diagnosis. Disclosures All authors: No reported disclosures.
Author Marínez-Lopez, Julia
Sepulveda, Jesús
Beigel, John
Pedraza, Gustavo
Ibarra-González, Violeta
Arteaga Cabello, Fernando J
Trujillo-Murillo, Karina
Caballero-Sosa, Sandra
Belaunzarán, Francisco
Hunsberger, Sally
Lumbard, Keith
Lourdes Guerrero, M
Reyes-Romero, Monica
Cervantes, Pilar Ramos
Ruiz-Palacios, Guillermo
Nájera-Cancino, Gabriel
Del Carmen Ruis Hernandez, Emilia
Escobedo-Lopez, Kenia Melina
Mora-Suarez, Nora K
Gouel-Cheron, Aurelie
Rincón-León, Héctor
AuthorAffiliation 1 Clinical Research Directorate/Clinical Monitoring Research Program, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, Maryland
5 Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, Tapachula, Mexico
3 Biostatistics Research Branch, NIAID, Rockville, Maryland
8 Molecular Biology, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico, Mexico
4 Hospital Regional de Alta Especialidad Ciudad Salud, Tapachula, Mexico
6 Instituto Mexicano del Seguro Social, Tapachula, Mexico
7 Hospital General Tapachula, Tapachula, Mexico
2 Department of Infectious Diseases, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
10 Comisión Coordinadora de los Institutos Nacionales de Salud y Hospitales de Alta Especialidad, Mexico City, Mexico
9 NIAID, Bethesda, Maryland
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The Author(s) 2018. Published by Oxford University Press on behalf of Infectious Diseases Society of America. 2018
Copyright_xml – notice: The Author(s) 2018. Published by Oxford University Press on behalf of Infectious Diseases Society of America. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
– notice: The Author(s) 2018. Published by Oxford University Press on behalf of Infectious Diseases Society of America. 2018
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Snippet Background RT-PCR (reverse transcriptase polymerase chain reaction) is often considered the “gold standard” for diagnosis of Zika Virus (ZIKV) infection;...
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SubjectTerms Abstracts
Decision trees
Dengue fever
Serology
Zika virus
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Title 2081. Building a Decision Tree with Serial Serology Measurements Improves Classification in a Flavivirus Co-circulation Region
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