Evidence map›Paper›PMID 34499659›Full record

ArticlePloS one2021

Optimizing use of multi-antibody assays for Lyme disease diagnosis: A bioinformatic approach.

Richard Porwancher, Lisa Landsberg

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 1 country.

Richard PorwancherDivision of Infectious Diseases, Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, United States of America.ORCID 0000-0002-4152-4383
Lisa LandsbergClinical Research Operations & Regulatory Affairs, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania, United States of America.
Rutgers, The State University of New Jersey · USTemple University · US

Funding

Improving Lyme disease diagnosis through bioinformaticsR43AI069564 · NIAID · INFECTIOUS DISEASE CONSULTANTS, PC · PI PORWANCHER, RICHARD B. · 2006 to 2008
$238k
NIAID NIH HHS R43 AI069564
6 · The paper itself

Abstract

Multiple different recombinant and peptide antigens are now available for serodiagnosis of Lyme disease (LD), but optimizing test utilization remains challenging. Since 1995 the Centers for Disease Control and Prevention (CDC) has recommended a 2-tiered serologic approach consisting of a first-tier whole-cell enzyme immunoassay (EIA) for polyvalent antibodies to Borrelia burgdorferi followed by confirmation of positive or equivocal results by IgG and IgM immunoblots [standard 2-tiered (STT) approach]. Newer modified 2-tiered (MTT) approaches employ a second-tier EIA to detect antibodies to B. burgdorferi rather than immunoblotting. We applied modern bioinformatic techniques to a large public database of recombinant and peptide antigen-based immunoassays to improve testing strategy. A retrospective CDC collection of 280 LD samples and 559 controls had been tested using the STT approach as well as kinetic-EIAs for VlsE1-IgG, C6-IgG, VlsE1-IgM, and pepC10-IgM antibodies. When used individually, the cutoff for each kinetic-EIA was set to generate 99% specificity. Utilizing logistic-likelihood regression analysis and receiver operating characteristic (ROC) techniques we determined that VlsE1-IgG, C6-IgG, and pepC10-IgM antibodies each contributed significant diagnostic information; a single-tier diagnostic score (DS) was generated for each sample using a weighted linear combination of antibody levels to these 3 antigens. DS performance was then compared to the STT and to MTT models employing different combinations of kinetic-EIAs. After setting the DS cutoff to match STT specificity (99%), the DS was 22.5% more sensitive than the STT for early-acute-phase disease (95% CI: 11.8% to 32.2%), 16.0% more sensitive for early-convalescent-phase disease (95% CI: 7.2% to 24.7%), and equivalent for detection of disseminated infection. The DS was also significantly more sensitive for early-acute-phase LD than MTT models whose specificity met or exceeded 99%. Prospective validation of this single-tier diagnostic score for Lyme disease will require larger studies using a broader range of potential cross-reacting conditions.

Indexed as

Antibodies, BacterialAntigens, BacterialBorrelia burgdorferiCase-Control StudiesComputational BiologyDiagnostic Tests, RoutineEarly DiagnosisHumansLogistic ModelsLyme DiseaseRetrospective StudiesSensitivity and SpecificityAntibodies, BacterialAntigens, Bacterial

Identifiers

PMID34499659
PMCPMC8428682
OpenAlexW3197324740

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.