Enterovirus D68 outbreak detection through a syndromic disease epidemiology network.

Lindsay Meyers, Jennifer Dien Bard, Ben Galvin, Jeff Nawrocki, Hubert G M Niesters, Kathleen A Stellrecht, Kirsten St George, Judy A Daly, Anne J Blaschke, Christine Robinson, Huanyu Wang, Camille V Cook, Ferdaus Hassan, Sam R Dominguez, Kristin Pretty, Samia Naccache, Katherine E Olin, Benjamin M Althouse, Jay D Jones, Christine C Ginocchio, Mark A Poritz, Amy Leber, Rangaraj Selvarangan
Author Information
  1. Lindsay Meyers: BioFire Diagnostics, Salt Lake City, UT, 84103, United States. Electronic address: lindsay.meyers@biofiredx.com.
  2. Jennifer Dien Bard: Department of Pathology and Laboratory Medicine, Children's Hospital of Los Angeles, Los Angeles, CA 90027, United States; Keck School of Medicine, University of Southern California, Los Angeles, CA 90039, United States. Electronic address: jdienbard@chla.usc.edu.
  3. Ben Galvin: BioFire Diagnostics, Salt Lake City, UT, 84103, United States. Electronic address: Ben.Galvin@biofiredx.com.
  4. Jeff Nawrocki: BioFire Diagnostics, Salt Lake City, UT, 84103, United States. Electronic address: Jeff.Nawrocki@biofiredx.com.
  5. Hubert G M Niesters: The University of Groningen, University Medical Center Groningen, Department of Medical Microbiology, Division of Clinical Virology, Groningen, The Netherlands. Electronic address: h.g.m.niesters@umcg.nl.
  6. Kathleen A Stellrecht: Department of Pathology and Laboratory Medicine, Albany Medical Center, Albany, NY 12208, United States. Electronic address: StellrK@amc.edu.
  7. Kirsten St George: New York State Department of Health, Albany, NY, 12202, United States. Electronic address: kirsten.st.george@health.ny.gov.
  8. Judy A Daly: Department of Pathology, University of Utah, Salt Lake City, UT 84132, United States; Division of Inpatient Medicine, Primary Children's Hospital, Salt Lake City, UT 84132, United States. Electronic address: Judy.Daly@imail.org.
  9. Anne J Blaschke: Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT 84132, United States. Electronic address: Anne.Blaschke@hsc.utah.edu.
  10. Christine Robinson: Department of Pathology and Laboratory Medicine, Children's Colorado, Aurora, CO 80045, United States. Electronic address: Christine.Robinson@childrenscolorado.org.
  11. Huanyu Wang: Department of Laboratory Medicine, Nationwide Children's Hospital, Columbus, OH 43205, United States. Electronic address: huanyu.wang@nationwidechildrens.org.
  12. Camille V Cook: BioFire Diagnostics, Salt Lake City, UT, 84103, United States. Electronic address: Camille.Cook@biofiredx.com.
  13. Ferdaus Hassan: Department of Pathology and Laboratory Medicine, Children's Mercy Hospital, Kansas City, MO 64108, United States. Electronic address: mfhassan@cmh.edu.
  14. Sam R Dominguez: Department of Pathology and Laboratory Medicine, Children's Colorado, Aurora, CO 80045, United States. Electronic address: Samuel.Dominguez@childrenscolorado.org.
  15. Kristin Pretty: Department of Pathology and Laboratory Medicine, Children's Colorado, Aurora, CO 80045, United States. Electronic address: Kristin.Pretty@childrenscolorado.org.
  16. Samia Naccache: Department of Pathology and Laboratory Medicine, Children's Hospital of Los Angeles, Los Angeles, CA 90027, United States. Electronic address: snaccache@chla.usc.edu.
  17. Katherine E Olin: BioFire Diagnostics, Salt Lake City, UT, 84103, United States.
  18. Benjamin M Althouse: Information School, University of Washington, Seattle, WA, 98105, United States; Department of Biology, New Mexico State University, Las Cruces, NM, 88003, United States. Electronic address: bma85@uw.edu.
  19. Jay D Jones: BioFire Diagnostics, Salt Lake City, UT, 84103, United States. Electronic address: Jay.Jones@biofiredx.com.
  20. Christine C Ginocchio: BioFire Diagnostics, Salt Lake City, UT, 84103, United States; Global Medical Affairs, bioMérieux, Durham, NC 27712, United States; Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, United States. Electronic address: christine.ginocchio@biomerieux.com.
  21. Mark A Poritz: BioFire Defense, Salt Lake City, UT 84107, United States. Electronic address: mporitz@firebirdbio.com.
  22. Amy Leber: Department of Laboratory Medicine, Nationwide Children's Hospital, Columbus, OH 43205, United States. Electronic address: Amy.Leber@nationwidechildrens.org.
  23. Rangaraj Selvarangan: Department of Pathology and Laboratory Medicine, Children's Mercy Hospital, Kansas City, MO 64108, United States. Electronic address: rselvarangan@cmh.edu.

Abstract

BACKGROUND: In 2014, enterovirus D68 (EV-D68) was responsible for an outbreak of severe respiratory illness in children, with 1,153 EV-D68 cases reported across 49 states. Despite this, there is no commercial assay for its detection in routine clinical care. BioFire® Syndromic Trends (Trend) is an epidemiological network that collects, in near real-time, deidentified. BioFire test results worldwide, including data from the BioFire® Respiratory Panel (RP).
OBJECTIVES: Using the RP version 1.7 (which was not explicitly designed to differentiate EV-D68 from other picornaviruses), we formulate a model, Pathogen Extended Resolution (PER), to distinguish EV-D68 from other human rhinoviruses/enteroviruses (RV/EV) tested for in the panel. Using PER in conjunction with Trend, we survey for historical evidence of EVD68 positivity and demonstrate a method for prospective real-time outbreak monitoring within the network.
STUDY DESIGN: PER incorporates real-time polymerase chain reaction metrics from the RPRV/EV assays. Six institutions in the United States and Europe contributed to the model creation, providing data from 1,619 samples spanning two years, confirmed by EV-D68 gold-standard molecular methods. We estimate outbreak periods by applying PER to over 600,000 historical Trend RP tests since 2014. Additionally, we used PER as a prospective monitoring tool during the 2018 outbreak.
RESULTS: The final PER algorithm demonstrated an overall sensitivity and specificity of 87.1% and 86.1%, respectively, among the gold-standard dataset. During the 2018 outbreak monitoring period, PER alerted the research network of EV-D68 emergence in July. One of the first sites to experience a significant increase, Nationwide Children's Hospital, confirmed the outbreak and implemented EV-D68 testing at the institution in response. Applying PER to the historical Trend dataset to determine rates among RP tests, we find three potential outbreaks with predicted regional EV-D68 rates as high as 37% in 2014, 16% in 2016, and 29% in 2018.
CONCLUSIONS: Using PER within the Trend network was shown to both accurately predict outbreaks of EV-D68 and to provide timely notifications of its circulation to participating clinical laboratories.

Keywords

MeSH Term

Algorithms
Child
Disease Outbreaks
Enterovirus D, Human
Enterovirus Infections
Epidemiological Monitoring
Europe
Humans
Respiratory Tract Infections
Sensitivity and Specificity
United States

Word Cloud

Created with Highcharts 10.0.0EV-D68PERoutbreakTrendnetworkRP20141real-timeUsinghistoricalmonitoring2018D68detectionclinicalBioFire®datamodelprospectivewithinconfirmedgold-standardtests1%amongdatasetratesoutbreaksEnterovirusBACKGROUND:enterovirusresponsiblesevererespiratoryillnesschildren153casesreportedacross49statesDespitecommercialassayroutinecareSyndromicTrendsepidemiologicalcollectsneardeidentifiedBioFiretestresultsworldwideincludingRespiratoryPanelOBJECTIVES:version7explicitlydesigneddifferentiatepicornavirusesformulatePathogenExtendedResolutiondistinguishhumanrhinoviruses/enterovirusesRV/EVtestedpanelconjunctionsurveyevidenceEVD68positivitydemonstratemethodSTUDYDESIGN:incorporatespolymerasechainreactionmetricsRPRV/EVassaysSixinstitutionsUnitedStatesEuropecontributedcreationproviding619samplesspanningtwoyearsmolecularmethodsestimateperiodsapplying600000sinceAdditionallyusedtoolRESULTS:finalalgorithmdemonstratedoverallsensitivityspecificity8786respectivelyperiodalertedresearchemergenceJulyOnefirstsitesexperiencesignificantincreaseNationwideChildren'sHospitalimplementedtestinginstitutionresponseApplyingdeterminefindthreepotentialpredictedregionalhigh37%16%201629%CONCLUSIONS:shownaccuratelypredictprovidetimelynotificationscirculationparticipatinglaboratoriessyndromicdiseaseepidemiologyD-68EpidemiologyMachinelearning

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