A diagnostic model to differentiate simple steatosis from nonalcoholic steatohepatitis based on the likelihood ratio form of Bayes theorem

Autores
Sookoian, Silvia Cristina; Castaño, Gustavo Osvaldo; Burgueño, Adriana Laura; Fernández Gianotti, Tomás; Rosselli, Maria Soledad; Pirola, Carlos José
Año de publicación
2009
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Objective: To evaluate the performance of a diagnostic model based on a composite index using clinical and laboratory data, including cardiovascular biomarkers, to help practitioners to differentiate patients with simple steatosis from those with nonalcoholic steatohepatitis (NASH). Design and methods: 101 patients with biopsy proven features of nonalcoholic fatty liver disease were included. We investigated the usefulness of 9 biomarkers in predicting the histological disease severity, including routine biochemical tests, C-reactive protein, soluble intercellular adhesion molecule-1 (sICAM-1) and anthropometric evaluation. Receiver operating characteristic (ROC) curves and likelihood ratios (LRs) were used to evaluate the fit of each test. A composite index was calculated as the product of each individual test LR. Results: In a model patient who has all positive tests, the post-test probability for NASH would be 99.5%. Conclusion: The capacity of each individual biomarker to independently predict the disease outcome was lower than a composite index constructed after multiplying the LR for each individual test combined into a “multimarker” score.
Fil: Sookoian, Silvia Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital "Dr. Abel Zubizarreta"; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina
Fil: Castaño, Gustavo Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital "Dr. Abel Zubizarreta"; Argentina
Fil: Burgueño, Adriana Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina
Fil: Fernández Gianotti, Tomás. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rosselli, Maria Soledad. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Pirola, Carlos José. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
DIAGNOSTIC MODEL
FATTY LIVER
NASH
BIOMARKERS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/103588
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A diagnostic model to differentiate simple steatosis from nonalcoholic steatohepatitis based on the likelihood ratio form of Bayes theoremSookoian, Silvia CristinaCastaño, Gustavo OsvaldoBurgueño, Adriana LauraFernández Gianotti, TomásRosselli, Maria SoledadPirola, Carlos JoséDIAGNOSTIC MODELFATTY LIVERNASHBIOMARKERShttps://purl.org/becyt/ford/3.2https://purl.org/becyt/ford/3Objective: To evaluate the performance of a diagnostic model based on a composite index using clinical and laboratory data, including cardiovascular biomarkers, to help practitioners to differentiate patients with simple steatosis from those with nonalcoholic steatohepatitis (NASH). Design and methods: 101 patients with biopsy proven features of nonalcoholic fatty liver disease were included. We investigated the usefulness of 9 biomarkers in predicting the histological disease severity, including routine biochemical tests, C-reactive protein, soluble intercellular adhesion molecule-1 (sICAM-1) and anthropometric evaluation. Receiver operating characteristic (ROC) curves and likelihood ratios (LRs) were used to evaluate the fit of each test. A composite index was calculated as the product of each individual test LR. Results: In a model patient who has all positive tests, the post-test probability for NASH would be 99.5%. Conclusion: The capacity of each individual biomarker to independently predict the disease outcome was lower than a composite index constructed after multiplying the LR for each individual test combined into a “multimarker” score.Fil: Sookoian, Silvia Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital "Dr. Abel Zubizarreta"; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; ArgentinaFil: Castaño, Gustavo Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital "Dr. Abel Zubizarreta"; ArgentinaFil: Burgueño, Adriana Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; ArgentinaFil: Fernández Gianotti, Tomás. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rosselli, Maria Soledad. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pirola, Carlos José. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaPergamon-Elsevier Science Ltd2009-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/103588Sookoian, Silvia Cristina; Castaño, Gustavo Osvaldo; Burgueño, Adriana Laura; Fernández Gianotti, Tomás; Rosselli, Maria Soledad; et al.; A diagnostic model to differentiate simple steatosis from nonalcoholic steatohepatitis based on the likelihood ratio form of Bayes theorem; Pergamon-Elsevier Science Ltd; Clinical Biochemistry; 42; 7-8; 5-2009; 624-6290009-9120CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.clinbiochem.2008.11.005info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0009912008005948info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-08-25T15:11:53Zoai:ri.conicet.gov.ar:11336/103588instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-08-25 15:11:54.371CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A diagnostic model to differentiate simple steatosis from nonalcoholic steatohepatitis based on the likelihood ratio form of Bayes theorem
title A diagnostic model to differentiate simple steatosis from nonalcoholic steatohepatitis based on the likelihood ratio form of Bayes theorem
spellingShingle A diagnostic model to differentiate simple steatosis from nonalcoholic steatohepatitis based on the likelihood ratio form of Bayes theorem
Sookoian, Silvia Cristina
DIAGNOSTIC MODEL
FATTY LIVER
NASH
BIOMARKERS
title_short A diagnostic model to differentiate simple steatosis from nonalcoholic steatohepatitis based on the likelihood ratio form of Bayes theorem
title_full A diagnostic model to differentiate simple steatosis from nonalcoholic steatohepatitis based on the likelihood ratio form of Bayes theorem
title_fullStr A diagnostic model to differentiate simple steatosis from nonalcoholic steatohepatitis based on the likelihood ratio form of Bayes theorem
title_full_unstemmed A diagnostic model to differentiate simple steatosis from nonalcoholic steatohepatitis based on the likelihood ratio form of Bayes theorem
title_sort A diagnostic model to differentiate simple steatosis from nonalcoholic steatohepatitis based on the likelihood ratio form of Bayes theorem
dc.creator.none.fl_str_mv Sookoian, Silvia Cristina
Castaño, Gustavo Osvaldo
Burgueño, Adriana Laura
Fernández Gianotti, Tomás
Rosselli, Maria Soledad
Pirola, Carlos José
author Sookoian, Silvia Cristina
author_facet Sookoian, Silvia Cristina
Castaño, Gustavo Osvaldo
Burgueño, Adriana Laura
Fernández Gianotti, Tomás
Rosselli, Maria Soledad
Pirola, Carlos José
author_role author
author2 Castaño, Gustavo Osvaldo
Burgueño, Adriana Laura
Fernández Gianotti, Tomás
Rosselli, Maria Soledad
Pirola, Carlos José
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv DIAGNOSTIC MODEL
FATTY LIVER
NASH
BIOMARKERS
topic DIAGNOSTIC MODEL
FATTY LIVER
NASH
BIOMARKERS
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.2
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Objective: To evaluate the performance of a diagnostic model based on a composite index using clinical and laboratory data, including cardiovascular biomarkers, to help practitioners to differentiate patients with simple steatosis from those with nonalcoholic steatohepatitis (NASH). Design and methods: 101 patients with biopsy proven features of nonalcoholic fatty liver disease were included. We investigated the usefulness of 9 biomarkers in predicting the histological disease severity, including routine biochemical tests, C-reactive protein, soluble intercellular adhesion molecule-1 (sICAM-1) and anthropometric evaluation. Receiver operating characteristic (ROC) curves and likelihood ratios (LRs) were used to evaluate the fit of each test. A composite index was calculated as the product of each individual test LR. Results: In a model patient who has all positive tests, the post-test probability for NASH would be 99.5%. Conclusion: The capacity of each individual biomarker to independently predict the disease outcome was lower than a composite index constructed after multiplying the LR for each individual test combined into a “multimarker” score.
Fil: Sookoian, Silvia Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital "Dr. Abel Zubizarreta"; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina
Fil: Castaño, Gustavo Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital "Dr. Abel Zubizarreta"; Argentina
Fil: Burgueño, Adriana Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina
Fil: Fernández Gianotti, Tomás. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rosselli, Maria Soledad. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Pirola, Carlos José. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Objective: To evaluate the performance of a diagnostic model based on a composite index using clinical and laboratory data, including cardiovascular biomarkers, to help practitioners to differentiate patients with simple steatosis from those with nonalcoholic steatohepatitis (NASH). Design and methods: 101 patients with biopsy proven features of nonalcoholic fatty liver disease were included. We investigated the usefulness of 9 biomarkers in predicting the histological disease severity, including routine biochemical tests, C-reactive protein, soluble intercellular adhesion molecule-1 (sICAM-1) and anthropometric evaluation. Receiver operating characteristic (ROC) curves and likelihood ratios (LRs) were used to evaluate the fit of each test. A composite index was calculated as the product of each individual test LR. Results: In a model patient who has all positive tests, the post-test probability for NASH would be 99.5%. Conclusion: The capacity of each individual biomarker to independently predict the disease outcome was lower than a composite index constructed after multiplying the LR for each individual test combined into a “multimarker” score.
publishDate 2009
dc.date.none.fl_str_mv 2009-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/103588
Sookoian, Silvia Cristina; Castaño, Gustavo Osvaldo; Burgueño, Adriana Laura; Fernández Gianotti, Tomás; Rosselli, Maria Soledad; et al.; A diagnostic model to differentiate simple steatosis from nonalcoholic steatohepatitis based on the likelihood ratio form of Bayes theorem; Pergamon-Elsevier Science Ltd; Clinical Biochemistry; 42; 7-8; 5-2009; 624-629
0009-9120
CONICET Digital
CONICET
url http://hdl.handle.net/11336/103588
identifier_str_mv Sookoian, Silvia Cristina; Castaño, Gustavo Osvaldo; Burgueño, Adriana Laura; Fernández Gianotti, Tomás; Rosselli, Maria Soledad; et al.; A diagnostic model to differentiate simple steatosis from nonalcoholic steatohepatitis based on the likelihood ratio form of Bayes theorem; Pergamon-Elsevier Science Ltd; Clinical Biochemistry; 42; 7-8; 5-2009; 624-629
0009-9120
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.clinbiochem.2008.11.005
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0009912008005948
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.24418

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