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A Diagnostic Prediction Model to Distinguish Multisystem Inflammatory Syndrome in Children

OBJECTIVE: Features of multisystem inflammatory syndrome in children (MIS‐C) overlap with other syndromes, making the diagnosis difficult for clinicians. We aimed to compare clinical differences between patients with and without clinical MIS‐C diagnosis and develop a diagnostic prediction model to a...

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Autores principales: Clark, Matthew T., Rankin, Danielle A., Peetluk, Lauren S., Gotte, Alisa, Herndon, Alison, McEachern, William, Smith, Andrew, Clark, Daniel E., Hardison, Edward, Esbenshade, Adam J., Patrick, Anna, Halasa, Natasha B., Connelly, James A., Katz, Sophie E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wiley Periodicals, Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9746665/
https://www.ncbi.nlm.nih.gov/pubmed/36319189
http://dx.doi.org/10.1002/acr2.11509
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author Clark, Matthew T.
Rankin, Danielle A.
Peetluk, Lauren S.
Gotte, Alisa
Herndon, Alison
McEachern, William
Smith, Andrew
Clark, Daniel E.
Hardison, Edward
Esbenshade, Adam J.
Patrick, Anna
Halasa, Natasha B.
Connelly, James A.
Katz, Sophie E.
author_facet Clark, Matthew T.
Rankin, Danielle A.
Peetluk, Lauren S.
Gotte, Alisa
Herndon, Alison
McEachern, William
Smith, Andrew
Clark, Daniel E.
Hardison, Edward
Esbenshade, Adam J.
Patrick, Anna
Halasa, Natasha B.
Connelly, James A.
Katz, Sophie E.
author_sort Clark, Matthew T.
collection PubMed
description OBJECTIVE: Features of multisystem inflammatory syndrome in children (MIS‐C) overlap with other syndromes, making the diagnosis difficult for clinicians. We aimed to compare clinical differences between patients with and without clinical MIS‐C diagnosis and develop a diagnostic prediction model to assist clinicians in identification of patients with MIS‐C within the first 24 hours of hospital presentation. METHODS: A cohort of 127 patients (<21 years) were admitted to an academic children's hospital and evaluated for MIS‐C. The primary outcome measure was MIS‐C diagnosis at Vanderbilt University Medical Center. Clinical, laboratory, and cardiac features were extracted from the medical record, compared among groups, and selected a priori to identify candidate predictors. Final predictors were identified through a logistic regression model with bootstrapped backward selection in which only variables selected in more than 80% of 500 bootstraps were included in the final model. RESULTS: Of 127 children admitted to our hospital with concern for MIS‐C, 45 were clinically diagnosed with MIS‐C and 82 were diagnosed with alternative diagnoses. We found a model with four variables—the presence of hypotension and/or fluid resuscitation, abdominal pain, new rash, and the value of serum sodium—showed excellent discrimination (concordance index 0.91; 95% confidence interval: 0.85‐0.96) and good calibration in identifying patients with MIS‐C. CONCLUSION: A diagnostic prediction model with early clinical and laboratory features shows excellent discrimination and may assist clinicians in distinguishing patients with MIS‐C. This model will require external and prospective validation prior to widespread use.
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spelling pubmed-97466652022-12-14 A Diagnostic Prediction Model to Distinguish Multisystem Inflammatory Syndrome in Children Clark, Matthew T. Rankin, Danielle A. Peetluk, Lauren S. Gotte, Alisa Herndon, Alison McEachern, William Smith, Andrew Clark, Daniel E. Hardison, Edward Esbenshade, Adam J. Patrick, Anna Halasa, Natasha B. Connelly, James A. Katz, Sophie E. ACR Open Rheumatol Original Article OBJECTIVE: Features of multisystem inflammatory syndrome in children (MIS‐C) overlap with other syndromes, making the diagnosis difficult for clinicians. We aimed to compare clinical differences between patients with and without clinical MIS‐C diagnosis and develop a diagnostic prediction model to assist clinicians in identification of patients with MIS‐C within the first 24 hours of hospital presentation. METHODS: A cohort of 127 patients (<21 years) were admitted to an academic children's hospital and evaluated for MIS‐C. The primary outcome measure was MIS‐C diagnosis at Vanderbilt University Medical Center. Clinical, laboratory, and cardiac features were extracted from the medical record, compared among groups, and selected a priori to identify candidate predictors. Final predictors were identified through a logistic regression model with bootstrapped backward selection in which only variables selected in more than 80% of 500 bootstraps were included in the final model. RESULTS: Of 127 children admitted to our hospital with concern for MIS‐C, 45 were clinically diagnosed with MIS‐C and 82 were diagnosed with alternative diagnoses. We found a model with four variables—the presence of hypotension and/or fluid resuscitation, abdominal pain, new rash, and the value of serum sodium—showed excellent discrimination (concordance index 0.91; 95% confidence interval: 0.85‐0.96) and good calibration in identifying patients with MIS‐C. CONCLUSION: A diagnostic prediction model with early clinical and laboratory features shows excellent discrimination and may assist clinicians in distinguishing patients with MIS‐C. This model will require external and prospective validation prior to widespread use. Wiley Periodicals, Inc. 2022-11-01 /pmc/articles/PMC9746665/ /pubmed/36319189 http://dx.doi.org/10.1002/acr2.11509 Text en © 2022 The Authors. ACR Open Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Original Article
Clark, Matthew T.
Rankin, Danielle A.
Peetluk, Lauren S.
Gotte, Alisa
Herndon, Alison
McEachern, William
Smith, Andrew
Clark, Daniel E.
Hardison, Edward
Esbenshade, Adam J.
Patrick, Anna
Halasa, Natasha B.
Connelly, James A.
Katz, Sophie E.
A Diagnostic Prediction Model to Distinguish Multisystem Inflammatory Syndrome in Children
title A Diagnostic Prediction Model to Distinguish Multisystem Inflammatory Syndrome in Children
title_full A Diagnostic Prediction Model to Distinguish Multisystem Inflammatory Syndrome in Children
title_fullStr A Diagnostic Prediction Model to Distinguish Multisystem Inflammatory Syndrome in Children
title_full_unstemmed A Diagnostic Prediction Model to Distinguish Multisystem Inflammatory Syndrome in Children
title_short A Diagnostic Prediction Model to Distinguish Multisystem Inflammatory Syndrome in Children
title_sort diagnostic prediction model to distinguish multisystem inflammatory syndrome in children
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9746665/
https://www.ncbi.nlm.nih.gov/pubmed/36319189
http://dx.doi.org/10.1002/acr2.11509
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