Evidence map›Paper›PMID 37685502›Full record

ArticleJournal of clinical medicine2023

Stratification of Pediatric COVID-19 Cases Using Inflammatory Biomarker Profiling and Machine Learning.

Devika Subramanian, Aadith Vittala, Xinpu Chen, Christopher Julien, Sebastian Acosta, Craig Rusin, Carl Allen, Nicholas Rider, Zbigniew Starosolski, Ananth Annapragada and 1 more

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact, top 76% 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

2 citing papers in PubMed, 0 citations in OpenAlex.

  1. Review
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 1 country.

Devika SubramanianDepartment of Computer Science, Rice University, 6100 Main St. MS 132, Houston, TX 77005, USA.
Aadith VittalaDepartment of Computer Science, Rice University, 6100 Main St. MS 132, Houston, TX 77005, USA.
Xinpu ChenTexas Children's Hospital/Baylor College of Medicine, 6621 Fannin Street, WB110.06, Houston, TX 77030, USA.
Christopher JulienTexas Children's Hospital/Baylor College of Medicine, 6621 Fannin Street, WB110.06, Houston, TX 77030, USA.
Sebastian AcostaTexas Children's Hospital/Baylor College of Medicine, 6621 Fannin Street, WB110.06, Houston, TX 77030, USA.
Craig RusinTexas Children's Hospital/Baylor College of Medicine, 6621 Fannin Street, WB110.06, Houston, TX 77030, USA.
Carl AllenTexas Children's Hospital/Baylor College of Medicine, 6621 Fannin Street, WB110.06, Houston, TX 77030, USA.
Nicholas RiderTexas Children's Hospital/Baylor College of Medicine, 6621 Fannin Street, WB110.06, Houston, TX 77030, USA.
Zbigniew StarosolskiTexas Children's Hospital/Baylor College of Medicine, 6621 Fannin Street, WB110.06, Houston, TX 77030, USA.
Ananth AnnapragadaTexas Children's Hospital/Baylor College of Medicine, 6621 Fannin Street, WB110.06, Houston, TX 77030, USA.
Sridevi DevarajTexas Children's Hospital/Baylor College of Medicine, 6621 Fannin Street, WB110.06, Houston, TX 77030, USA.
Baylor College of Medicine · USTexas Children's Hospital · USRice University · US

Funding

AICORE-kids: Artificial Intelligence COVID-19 Risk AssEssment for kidsR61HD105593 · NICHD · BAYLOR COLLEGE OF MEDICINE · PI ALLEN, CARL E, ANNAPRAGADA, ANANTH V · 2021 to 2022
$1.6M
NICHD NIH HHS R61 HD105593NIH HHS NIH R61-HD 105593
6 · The paper itself

Abstract

While pediatric COVID-19 is rarely severe, a small fraction of children infected with SARS-CoV-2 go on to develop multisystem inflammatory syndrome (MIS-C), with substantial morbidity. An objective method with high specificity and high sensitivity to identify current or imminent MIS-C in children infected with SARS-CoV-2 is highly desirable. The aim was to learn about an interpretable novel cytokine/chemokine assay panel providing such an objective classification. This retrospective study was conducted on four groups of pediatric patients seen at multiple sites of Texas Children's Hospital, Houston, TX who consented to provide blood samples to our COVID-19 Biorepository. Standard laboratory markers of inflammation and a novel cytokine/chemokine array were measured in blood samples of all patients. Group 1 consisted of 72 COVID-19, 70 MIS-C and 63 uninfected control patients seen between May 2020 and January 2021 and predominantly infected with pre-alpha variants. Group 2 consisted of 29 COVID-19 and 43 MIS-C patients seen between January and May 2021 infected predominantly with the alpha variant. Group 3 consisted of 30 COVID-19 and 32 MIS-C patients seen between August and October 2021 infected with alpha and/or delta variants. Group 4 consisted of 20 COVID-19 and 46 MIS-C patients seen between October 2021 andJanuary 2022 infected with delta and/or omicron variants. Group 1 was used to train an L1-regularized logistic regression model which was tested using five-fold cross validation, and then separately validated against the remaining naïve groups. The area under receiver operating curve (AUROC) and F1-score were used to quantify the performance of the cytokine/chemokine assay-based classifier. Standard laboratory markers predict MIS-C with a five-fold cross-validated AUROC of 0.86 ± 0.05 and an F1 score of 0.78 ± 0.07, while the cytokine/chemokine panel predicted MIS-C with a five-fold cross-validated AUROC of 0.95 ± 0.02 and an F1 score of 0.91 ± 0.04, with only sixteen of the forty-five cytokines/chemokines sufficient to achieve this performance. Tested on Group 2 the cytokine/chemokine panel yielded AUROC = 0.98 and F1 = 0.93, on Group 3 it yielded AUROC = 0.89 and F1 = 0.89, and on Group 4 AUROC = 0.99 and F1 = 0.97. Adding standard laboratory markers to the cytokine/chemokine panel did not improve performance. A top-10 subset of these 16 cytokines achieves equivalent performance on the validation data sets. Our findings demonstrate that a sixteen-cytokine/chemokine panel as well as the top ten subset provides a highly sensitive, and specific method to identify MIS-C in patients infected with SARS-CoV-2 of all the major variants identified to date.

Indexed as

COVID-19cytokineinflammationmachine learningSARS CoV-2

Identifiers

PMID37685502
PMCPMC10487951
OpenAlexW4386049872

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.