Evidence map›Paper›PMID 35732822›Full record

ArticlePediatric research2023

Bridging a diagnostic Kawasaki disease classifier from a microarray platform to a qRT-PCR assay.

Rowan Kuiper, Victoria J Wright, Dominic Habgood-Coote, Chisato Shimizu, Daphne Huigh, Adriana H Tremoulet, Danielle van Keulen, Clive J Hoggart, Jesus Rodriguez-Manzano, Jethro A Herberg and 4 more

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Article in Pediatric research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.4field-weighted citation impact, top 11% 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

7 citing papers in PubMed, 12 citations in OpenAlex.

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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

14 authors at 3 institutions in 2 countries.

Rowan Kuiper *SkylineDx, Rotterdam, The Netherlands. r.kuiper@skylinedx.com.
Victoria J Wright *Department of Infectious Disease, Imperial College London, London, UK.
Dominic Habgood-CooteDepartment of Infectious Disease, Imperial College London, London, UK.
Chisato ShimizuDepartment of Pediatrics, Rady Children's Hospital and University of California San Diego, La Jolla, CA, USA.
Daphne HuighSkylineDx, Rotterdam, The Netherlands.
Adriana H TremouletDepartment of Pediatrics, Rady Children's Hospital and University of California San Diego, La Jolla, CA, USA.
Danielle van KeulenSkylineDx, Rotterdam, The Netherlands.
Clive J HoggartDepartment of Infectious Disease, Imperial College London, London, UK.
Jesus Rodriguez-ManzanoDepartment of Infectious Disease, Imperial College London, London, UK.
Jethro A HerbergDepartment of Infectious Disease, Imperial College London, London, UK.
Myrsini KaforouDepartment of Infectious Disease, Imperial College London, London, UK.
Dennie Tempel *SkylineDx, Rotterdam, The Netherlands.
Jane C Burns *Department of Pediatrics, Rady Children's Hospital and University of California San Diego, La Jolla, CA, USA.
Michael Levin *Department of Infectious Disease, Imperial College London, London, UK.
Imperial College London · GBRady Children's Hospital-San Diego · USUniversity of California, San Diego · US

Funding

Innate Immune Activation and Endothelial Cell Dysfunction in Acute Kawasaki DiseaseR01HL140898 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BURNS, JANE C, HOFFMAN, HAROLD M · 2018 to 2022
$4.3M
Diagnosing and predicting risk in children with SARS-CoV-2- related illnessR61HD105590 · NICHD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BURNS, JANE C · 2021 to 2022
$1.8M
MRF_ MRF-160-0008-ELP-KAFO-C0801NHLBI NIH HHS R01 HL140898NICHD NIH HHS R61 HD105590Wellcome Trust 206508/Z/17/Z
6 · The paper itself

Abstract

backgroundKawasaki disease (KD) is a systemic vasculitis that mainly affects children under 5 years of age. Up to 30% of patients develop coronary artery abnormalities, which are reduced with early treatment. Timely diagnosis of KD is challenging but may become more straightforward with the recent discovery of a whole-blood host response classifier that discriminates KD patients from patients with other febrile conditions. Here, we bridged this microarray-based classifier to a clinically applicable quantitative reverse transcription-polymerase chain reaction (qRT-PCR) assay: the Kawasaki Disease Gene Expression Profiling (KiDs-GEP) classifier.

methodsWe designed and optimized a qRT-PCR assay and applied it to a subset of samples previously used for the classifier discovery to reweight the original classifier.

resultsThe performance of the KiDs-GEP classifier was comparable to the original classifier with a cross-validated area under the ROC curve of 0.964 [95% CI: 0.924-1.00] vs 0.992 [95% CI: 0.978-1.00], respectively. Both classifiers demonstrated similar trends over various disease conditions, with the clearest distinction between individuals diagnosed with KD vs viral infections.

conclusionWe successfully bridged the microarray-based classifier into the KiDs-GEP classifier, a more rapid and more cost-efficient qRT-PCR assay, bringing a diagnostic test for KD closer to the hospital clinical laboratory. IMPACT: A diagnostic test is needed for Kawasaki disease and is currently not available. We describe the development of a One-Step multiplex qRT-PCR assay and the subsequent modification (i.e., bridging) of the microarray-based host response classifier previously described by Wright et al. The bridged KiDs-GEP classifier performs well in discriminating Kawasaki disease patients from febrile controls. This host response clinical test for Kawasaki disease can be adapted to the hospital clinical laboratory.

Indexed as

Mucocutaneous Lymph Node SyndromeChildChild, PreschoolFeverGene Expression ProfilingHumansReverse Transcriptase Polymerase Chain ReactionROC Curve

Identifiers

PMID35732822
PMCPMC9988687
OpenAlexW4283367833

What OpenQuestion holds

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LicenceCC BY
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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.