Evidence map›Paper›PMID 37598693›Full record

ArticleThe Lancet. Child & adolescent health2023

Subgroups of children with Kawasaki disease: a data-driven cluster analysis.

Hao Wang, Chisato Shimizu, Emelia Bainto, Shea Hamilton, Heather R Jackson, Diego Estrada-Rivadeneyra, Myrsini Kaforou, Michael Levin, Joan M Pancheri, Kirsten B Dummer and 2 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Lancet. Child & adolescent health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 1 pooled it
17.8field-weighted citation impact, top 1% 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

43 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 3 institutions in 2 countries.

Hao WangKawasaki Disease Research Center, Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Chisato ShimizuKawasaki Disease Research Center, Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Emelia BaintoKawasaki Disease Research Center, Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Shea HamiltonSection of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK.
Heather R JacksonSection of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK.
Diego Estrada-RivadeneyraSection of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK.
Myrsini KaforouSection of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK.
Michael LevinSection of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK.
Joan M PancheriKawasaki Disease Research Center, Department of Pediatrics, University of California San Diego, La Jolla, CA, USA; Rady Children's Hospital-San Diego, San Diego, CA, USA.
Kirsten B DummerKawasaki Disease Research Center, Department of Pediatrics, University of California San Diego, La Jolla, CA, USA; Rady Children's Hospital-San Diego, San Diego, CA, USA.
Adriana H TremouletKawasaki Disease Research Center, Department of Pediatrics, University of California San Diego, La Jolla, CA, USA; Rady Children's Hospital-San Diego, San Diego, CA, USA.
Jane C BurnsKawasaki Disease Research Center, Department of Pediatrics, University of California San Diego, La Jolla, CA, USA; Rady Children's Hospital-San Diego, San Diego, CA, USA. Electronic address: jcburns@health.ucsd.edu.
Imperial College London · GBUniversity of California San Diego · USRady Children's Hospital-San Diego · US

Funding

Diagnosing and predicting risk in children with SARS-CoV-2- related illnessR33HD105590 · NICHD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BURNS, JANE C · 2023 to 2023
$2.9M
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
NICHD NIH HHS R33 HD105590NICHD NIH HHS R61 HD105590
6 · The paper itself

Abstract

backgroundAlthough Kawasaki disease is commonly regarded as a single disease entity, variability in clinical manifestations and disease outcome has been recognised. We aimed to use a data-driven approach to identify clinical subgroups.

methodsWe analysed clinical data from patients with Kawasaki disease diagnosed at Rady Children's Hospital (San Diego, CA, USA) between Jan 1, 2002, and June 30, 2022. Patients were grouped by hierarchical clustering on principal components with k-means parcellation based on 14 variables, including age at onset, ten laboratory test results, day of illness at the first intravenous immunoglobulin infusion, and normalised echocardiographic measures of coronary artery diameters at diagnosis. We also analysed the seasonality and Kawasaki disease incidence from 2002 to 2019 by subgroup. To explore the biological underpinnings of identified subgroups, we did differential abundance analysis on proteomic data of 6481 proteins from 32 patients with Kawasaki disease and 24 healthy children, using linear regression models that controlled for age and sex.

findingsAmong 1016 patients with complete data in the final analysis, four subgroups were identified with distinct clinical features: (1) hepatobiliary involvement with elevated alanine transaminase, gamma-glutamyl transferase, and total bilirubin levels, lowest coronary artery aneurysm but highest intravenous immunoglobulin resistance rates (n=157); (2) highest band neutrophil count and Kawasaki disease shock rate (n=231); (3) cervical lymphadenopathy with high markers of inflammation (erythrocyte sedimentation rate, C-reactive protein, white blood cell, and platelet counts) and lowest age-adjusted haemoglobin Z scores (n=315); and (4) young age at onset with highest coronary artery aneurysm but lowest intravenous immunoglobulin resistance rates (n=313). The subgroups had distinct seasonal and incidence trajectories. In addition, the subgroups shared 211 differential abundance proteins while many proteins were unique to a subgroup.

interpretationOur data-driven analysis provides insight into the heterogeneity of Kawasaki disease, and supports the existence of distinct subgroups with important implications for clinical management and research design and interpretation.

fundingUS National Institutes of Health and the Irving and Francine Suknow Foundation.

Indexed as

AneurysmMucocutaneous Lymph Node SyndromeChildCluster AnalysisHumansImmunoglobulins, IntravenousProteomicsUnited StatesImmunoglobulins, Intravenous

Identifiers

PMID37598693
PMCPMC10756500
OpenAlexW4385916922

What OpenQuestion holds

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LicenceTDM
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Registered trials

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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.