Assessing the Relevance of Non-molecular Prognostic Systems for Myelodysplastic Syndrome in the Era of Next-Generation Sequencing

Autores
Lincango Yupanki, Marco Vinicio; Andreoli, Verónica; Rivello, Hernán García; Bender, Andrea; Catalán, Ana I; Rahhal, Marilina; Delamer, Rocío; Asinari, Mariana; Mosquera Orgueira, Adrián; Castro, María Belén; Mela Osorio, María José; Navickas, Alicia; Grille, Sofia; Agriello, Evangelina Edith; Arbelbide, Jorge; Basquiera, Ana Lisa; Belli, Carolina Bárbara
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Background: The Molecular International Prognostic Scoring System (IPSS-M) has improvedthe prediction of clinical outcomes for myelodysplastic syndromes (MDS). The ArtificialIntelligence Prognostic Scoring System for MDS (AIPSS-MDS), based on classical clinicalparameters, has outperformed the IPSS, revised version (IPSS-R). For the first time, wevalidated the IPSS-M and other molecular prognostic models and compared them with theestablished IPSS-R and AIPSS-MDS models using data from South American patients.Methods: Molecular and clinical data from 145 patients with MDS and 37 patients withMDS/myeloproliferative neoplasms were retrospectively analyzed.Results: Prognostic power evaluation revealed that the IPSS-M (Harrell’s concordance [C]-index: 0.75, area under the receiver operating characteristic curve [AUC]: 0.68) predictedoverall survival better than the European MDS (EuroMDS; C-index: 0.72, AUC: 0.68) andMunich Leukemia Laboratory (MLL) (C-index: 0.70, AUC: 0.64) models. The IPSS-M prognosticdiscrimination was similar to that of the AIPSS-MDS model (C-index: 0.74, AUC:0.66) and outperformed the IPSS-R model (C-index: 0.70, AUC: 0.61). Considering simplifiedlow- and high-risk groups for clinical management, after restratifying from IPSS-R (57%and 32%, respectively, hazard ratio [HR]: 2.8; P =0.002) to IPSS-M, 12.6% of patients wereupstaged, and 5% were downstaged (HR: 2.9; P =0.001). The AIPSS-MDS recategorized51% of the low-risk cohort as high-risk, with no patients being downstaged (HR: 5.6;P <0.001), consistent with most patients requiring disease-modifying therapy.Conclusions: The IPSS-M and AIPSS-MDS models provide more accurate survival prognosesthan the IPSS-R, EuroMDS, and MLL models. The AIPSS-MDS model is a valid optionfor assessing risks for all patients with MDS, especially in resource-limited centers wheremolecular testing is not currently a standard clinical practice.
Fil: Lincango Yupanki, Marco Vinicio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; Argentina
Fil: Andreoli, Verónica. Hospital Privado Universitario de Cordoba.; Argentina
Fil: Rivello, Hernán García. Instituto Universidad Escuela de Medicina del Hospital Italiano; Argentina
Fil: Bender, Andrea. Laboratorio de Especialidades Bioquimicas (leb Laboratorio);
Fil: Catalán, Ana I. Universidad de la República. Facultad de Medicina. Hospital de Clínicas "Dr. Manuel Quintela"; Uruguay
Fil: Rahhal, Marilina. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; Argentina
Fil: Delamer, Rocío. Fundación Para Combatir la Leucemia; Argentina
Fil: Asinari, Mariana. Hospital Privado Universitario de Cordoba.; Argentina
Fil: Mosquera Orgueira, Adrián. Universidad de Santiago de Compostela; España
Fil: Castro, María Belén. Hospital Privado Universitario de Cordoba.; Argentina
Fil: Mela Osorio, María José. Fundacion Para Combatir la Leucemia.; Argentina
Fil: Navickas, Alicia. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; Argentina
Fil: Grille, Sofia. Universidad de la República. Facultad de Medicina. Hospital de Clínicas "Dr. Manuel Quintela"; Uruguay
Fil: Agriello, Evangelina Edith. Laboratorio de Especialidades Bioquimicas (leb Laboratorio);
Fil: Arbelbide, Jorge. Hospital Italiano; Argentina
Fil: Basquiera, Ana Lisa. Hospital Privado Universitario de Cordoba.; Argentina
Fil: Belli, Carolina Bárbara. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; Argentina
Materia
Mielodisplasia
Pronostico
IPSS-Molecular
Inteligencia Artificial
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:11336/275700
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Assessing the Relevance of Non-molecular Prognostic Systems for Myelodysplastic Syndrome in the Era of Next-Generation SequencingLincango Yupanki, Marco VinicioAndreoli, VerónicaRivello, Hernán GarcíaBender, AndreaCatalán, Ana IRahhal, MarilinaDelamer, RocíoAsinari, MarianaMosquera Orgueira, AdriánCastro, María BelénMela Osorio, María JoséNavickas, AliciaGrille, SofiaAgriello, Evangelina EdithArbelbide, JorgeBasquiera, Ana LisaBelli, Carolina BárbaraMielodisplasiaPronosticoIPSS-MolecularInteligencia Artificialhttps://purl.org/becyt/ford/3.2https://purl.org/becyt/ford/3Background: The Molecular International Prognostic Scoring System (IPSS-M) has improvedthe prediction of clinical outcomes for myelodysplastic syndromes (MDS). The ArtificialIntelligence Prognostic Scoring System for MDS (AIPSS-MDS), based on classical clinicalparameters, has outperformed the IPSS, revised version (IPSS-R). For the first time, wevalidated the IPSS-M and other molecular prognostic models and compared them with theestablished IPSS-R and AIPSS-MDS models using data from South American patients.Methods: Molecular and clinical data from 145 patients with MDS and 37 patients withMDS/myeloproliferative neoplasms were retrospectively analyzed.Results: Prognostic power evaluation revealed that the IPSS-M (Harrell’s concordance [C]-index: 0.75, area under the receiver operating characteristic curve [AUC]: 0.68) predictedoverall survival better than the European MDS (EuroMDS; C-index: 0.72, AUC: 0.68) andMunich Leukemia Laboratory (MLL) (C-index: 0.70, AUC: 0.64) models. The IPSS-M prognosticdiscrimination was similar to that of the AIPSS-MDS model (C-index: 0.74, AUC:0.66) and outperformed the IPSS-R model (C-index: 0.70, AUC: 0.61). Considering simplifiedlow- and high-risk groups for clinical management, after restratifying from IPSS-R (57%and 32%, respectively, hazard ratio [HR]: 2.8; P =0.002) to IPSS-M, 12.6% of patients wereupstaged, and 5% were downstaged (HR: 2.9; P =0.001). The AIPSS-MDS recategorized51% of the low-risk cohort as high-risk, with no patients being downstaged (HR: 5.6;P <0.001), consistent with most patients requiring disease-modifying therapy.Conclusions: The IPSS-M and AIPSS-MDS models provide more accurate survival prognosesthan the IPSS-R, EuroMDS, and MLL models. The AIPSS-MDS model is a valid optionfor assessing risks for all patients with MDS, especially in resource-limited centers wheremolecular testing is not currently a standard clinical practice.Fil: Lincango Yupanki, Marco Vinicio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; ArgentinaFil: Andreoli, Verónica. Hospital Privado Universitario de Cordoba.; ArgentinaFil: Rivello, Hernán García. Instituto Universidad Escuela de Medicina del Hospital Italiano; ArgentinaFil: Bender, Andrea. Laboratorio de Especialidades Bioquimicas (leb Laboratorio);Fil: Catalán, Ana I. Universidad de la República. Facultad de Medicina. Hospital de Clínicas "Dr. Manuel Quintela"; UruguayFil: Rahhal, Marilina. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; ArgentinaFil: Delamer, Rocío. Fundación Para Combatir la Leucemia; ArgentinaFil: Asinari, Mariana. Hospital Privado Universitario de Cordoba.; ArgentinaFil: Mosquera Orgueira, Adrián. Universidad de Santiago de Compostela; EspañaFil: Castro, María Belén. Hospital Privado Universitario de Cordoba.; ArgentinaFil: Mela Osorio, María José. Fundacion Para Combatir la Leucemia.; ArgentinaFil: Navickas, Alicia. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; ArgentinaFil: Grille, Sofia. Universidad de la República. Facultad de Medicina. Hospital de Clínicas "Dr. Manuel Quintela"; UruguayFil: Agriello, Evangelina Edith. Laboratorio de Especialidades Bioquimicas (leb Laboratorio);Fil: Arbelbide, Jorge. Hospital Italiano; ArgentinaFil: Basquiera, Ana Lisa. Hospital Privado Universitario de Cordoba.; ArgentinaFil: Belli, Carolina Bárbara. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; ArgentinaKorean Society for Laboratory Medicine2024-07-26info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/275700Lincango Yupanki, Marco Vinicio; Andreoli, Verónica; Rivello, Hernán García; Bender, Andrea; Catalán, Ana I; et al.; Assessing the Relevance of Non-molecular Prognostic Systems for Myelodysplastic Syndrome in the Era of Next-Generation Sequencing; Korean Society for Laboratory Medicine; Annals of Laboratory Medicine; 45; 1; 26-7-2024; 44-522234-38062234-3814CONICET DigitalCONICETenginfo:eu-repo/semantics/reference/url/https://doi.org/10.3343/alm.2024.0089info:eu-repo/semantics/altIdentifier/url/http://annlabmed.org/journal/view.html?doi=10.3343/alm.2024.0089info:eu-repo/semantics/altIdentifier/doi/10.3343/alm.2024.0089info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-12-03T08:35:18Zoai:ri.conicet.gov.ar:11336/275700instacron: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:34982025-12-03 08:35:19.007CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Assessing the Relevance of Non-molecular Prognostic Systems for Myelodysplastic Syndrome in the Era of Next-Generation Sequencing
title Assessing the Relevance of Non-molecular Prognostic Systems for Myelodysplastic Syndrome in the Era of Next-Generation Sequencing
spellingShingle Assessing the Relevance of Non-molecular Prognostic Systems for Myelodysplastic Syndrome in the Era of Next-Generation Sequencing
Lincango Yupanki, Marco Vinicio
Mielodisplasia
Pronostico
IPSS-Molecular
Inteligencia Artificial
title_short Assessing the Relevance of Non-molecular Prognostic Systems for Myelodysplastic Syndrome in the Era of Next-Generation Sequencing
title_full Assessing the Relevance of Non-molecular Prognostic Systems for Myelodysplastic Syndrome in the Era of Next-Generation Sequencing
title_fullStr Assessing the Relevance of Non-molecular Prognostic Systems for Myelodysplastic Syndrome in the Era of Next-Generation Sequencing
title_full_unstemmed Assessing the Relevance of Non-molecular Prognostic Systems for Myelodysplastic Syndrome in the Era of Next-Generation Sequencing
title_sort Assessing the Relevance of Non-molecular Prognostic Systems for Myelodysplastic Syndrome in the Era of Next-Generation Sequencing
dc.creator.none.fl_str_mv Lincango Yupanki, Marco Vinicio
Andreoli, Verónica
Rivello, Hernán García
Bender, Andrea
Catalán, Ana I
Rahhal, Marilina
Delamer, Rocío
Asinari, Mariana
Mosquera Orgueira, Adrián
Castro, María Belén
Mela Osorio, María José
Navickas, Alicia
Grille, Sofia
Agriello, Evangelina Edith
Arbelbide, Jorge
Basquiera, Ana Lisa
Belli, Carolina Bárbara
author Lincango Yupanki, Marco Vinicio
author_facet Lincango Yupanki, Marco Vinicio
Andreoli, Verónica
Rivello, Hernán García
Bender, Andrea
Catalán, Ana I
Rahhal, Marilina
Delamer, Rocío
Asinari, Mariana
Mosquera Orgueira, Adrián
Castro, María Belén
Mela Osorio, María José
Navickas, Alicia
Grille, Sofia
Agriello, Evangelina Edith
Arbelbide, Jorge
Basquiera, Ana Lisa
Belli, Carolina Bárbara
author_role author
author2 Andreoli, Verónica
Rivello, Hernán García
Bender, Andrea
Catalán, Ana I
Rahhal, Marilina
Delamer, Rocío
Asinari, Mariana
Mosquera Orgueira, Adrián
Castro, María Belén
Mela Osorio, María José
Navickas, Alicia
Grille, Sofia
Agriello, Evangelina Edith
Arbelbide, Jorge
Basquiera, Ana Lisa
Belli, Carolina Bárbara
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Mielodisplasia
Pronostico
IPSS-Molecular
Inteligencia Artificial
topic Mielodisplasia
Pronostico
IPSS-Molecular
Inteligencia Artificial
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.2
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Background: The Molecular International Prognostic Scoring System (IPSS-M) has improvedthe prediction of clinical outcomes for myelodysplastic syndromes (MDS). The ArtificialIntelligence Prognostic Scoring System for MDS (AIPSS-MDS), based on classical clinicalparameters, has outperformed the IPSS, revised version (IPSS-R). For the first time, wevalidated the IPSS-M and other molecular prognostic models and compared them with theestablished IPSS-R and AIPSS-MDS models using data from South American patients.Methods: Molecular and clinical data from 145 patients with MDS and 37 patients withMDS/myeloproliferative neoplasms were retrospectively analyzed.Results: Prognostic power evaluation revealed that the IPSS-M (Harrell’s concordance [C]-index: 0.75, area under the receiver operating characteristic curve [AUC]: 0.68) predictedoverall survival better than the European MDS (EuroMDS; C-index: 0.72, AUC: 0.68) andMunich Leukemia Laboratory (MLL) (C-index: 0.70, AUC: 0.64) models. The IPSS-M prognosticdiscrimination was similar to that of the AIPSS-MDS model (C-index: 0.74, AUC:0.66) and outperformed the IPSS-R model (C-index: 0.70, AUC: 0.61). Considering simplifiedlow- and high-risk groups for clinical management, after restratifying from IPSS-R (57%and 32%, respectively, hazard ratio [HR]: 2.8; P =0.002) to IPSS-M, 12.6% of patients wereupstaged, and 5% were downstaged (HR: 2.9; P =0.001). The AIPSS-MDS recategorized51% of the low-risk cohort as high-risk, with no patients being downstaged (HR: 5.6;P <0.001), consistent with most patients requiring disease-modifying therapy.Conclusions: The IPSS-M and AIPSS-MDS models provide more accurate survival prognosesthan the IPSS-R, EuroMDS, and MLL models. The AIPSS-MDS model is a valid optionfor assessing risks for all patients with MDS, especially in resource-limited centers wheremolecular testing is not currently a standard clinical practice.
Fil: Lincango Yupanki, Marco Vinicio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; Argentina
Fil: Andreoli, Verónica. Hospital Privado Universitario de Cordoba.; Argentina
Fil: Rivello, Hernán García. Instituto Universidad Escuela de Medicina del Hospital Italiano; Argentina
Fil: Bender, Andrea. Laboratorio de Especialidades Bioquimicas (leb Laboratorio);
Fil: Catalán, Ana I. Universidad de la República. Facultad de Medicina. Hospital de Clínicas "Dr. Manuel Quintela"; Uruguay
Fil: Rahhal, Marilina. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; Argentina
Fil: Delamer, Rocío. Fundación Para Combatir la Leucemia; Argentina
Fil: Asinari, Mariana. Hospital Privado Universitario de Cordoba.; Argentina
Fil: Mosquera Orgueira, Adrián. Universidad de Santiago de Compostela; España
Fil: Castro, María Belén. Hospital Privado Universitario de Cordoba.; Argentina
Fil: Mela Osorio, María José. Fundacion Para Combatir la Leucemia.; Argentina
Fil: Navickas, Alicia. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; Argentina
Fil: Grille, Sofia. Universidad de la República. Facultad de Medicina. Hospital de Clínicas "Dr. Manuel Quintela"; Uruguay
Fil: Agriello, Evangelina Edith. Laboratorio de Especialidades Bioquimicas (leb Laboratorio);
Fil: Arbelbide, Jorge. Hospital Italiano; Argentina
Fil: Basquiera, Ana Lisa. Hospital Privado Universitario de Cordoba.; Argentina
Fil: Belli, Carolina Bárbara. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; Argentina
description Background: The Molecular International Prognostic Scoring System (IPSS-M) has improvedthe prediction of clinical outcomes for myelodysplastic syndromes (MDS). The ArtificialIntelligence Prognostic Scoring System for MDS (AIPSS-MDS), based on classical clinicalparameters, has outperformed the IPSS, revised version (IPSS-R). For the first time, wevalidated the IPSS-M and other molecular prognostic models and compared them with theestablished IPSS-R and AIPSS-MDS models using data from South American patients.Methods: Molecular and clinical data from 145 patients with MDS and 37 patients withMDS/myeloproliferative neoplasms were retrospectively analyzed.Results: Prognostic power evaluation revealed that the IPSS-M (Harrell’s concordance [C]-index: 0.75, area under the receiver operating characteristic curve [AUC]: 0.68) predictedoverall survival better than the European MDS (EuroMDS; C-index: 0.72, AUC: 0.68) andMunich Leukemia Laboratory (MLL) (C-index: 0.70, AUC: 0.64) models. The IPSS-M prognosticdiscrimination was similar to that of the AIPSS-MDS model (C-index: 0.74, AUC:0.66) and outperformed the IPSS-R model (C-index: 0.70, AUC: 0.61). Considering simplifiedlow- and high-risk groups for clinical management, after restratifying from IPSS-R (57%and 32%, respectively, hazard ratio [HR]: 2.8; P =0.002) to IPSS-M, 12.6% of patients wereupstaged, and 5% were downstaged (HR: 2.9; P =0.001). The AIPSS-MDS recategorized51% of the low-risk cohort as high-risk, with no patients being downstaged (HR: 5.6;P <0.001), consistent with most patients requiring disease-modifying therapy.Conclusions: The IPSS-M and AIPSS-MDS models provide more accurate survival prognosesthan the IPSS-R, EuroMDS, and MLL models. The AIPSS-MDS model is a valid optionfor assessing risks for all patients with MDS, especially in resource-limited centers wheremolecular testing is not currently a standard clinical practice.
publishDate 2024
dc.date.none.fl_str_mv 2024-07-26
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/11336/275700
Lincango Yupanki, Marco Vinicio; Andreoli, Verónica; Rivello, Hernán García; Bender, Andrea; Catalán, Ana I; et al.; Assessing the Relevance of Non-molecular Prognostic Systems for Myelodysplastic Syndrome in the Era of Next-Generation Sequencing; Korean Society for Laboratory Medicine; Annals of Laboratory Medicine; 45; 1; 26-7-2024; 44-52
2234-3806
2234-3814
CONICET Digital
CONICET
url http://hdl.handle.net/11336/275700
identifier_str_mv Lincango Yupanki, Marco Vinicio; Andreoli, Verónica; Rivello, Hernán García; Bender, Andrea; Catalán, Ana I; et al.; Assessing the Relevance of Non-molecular Prognostic Systems for Myelodysplastic Syndrome in the Era of Next-Generation Sequencing; Korean Society for Laboratory Medicine; Annals of Laboratory Medicine; 45; 1; 26-7-2024; 44-52
2234-3806
2234-3814
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/reference/url/https://doi.org/10.3343/alm.2024.0089
info:eu-repo/semantics/altIdentifier/url/http://annlabmed.org/journal/view.html?doi=10.3343/alm.2024.0089
info:eu-repo/semantics/altIdentifier/doi/10.3343/alm.2024.0089
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
dc.publisher.none.fl_str_mv Korean Society for Laboratory Medicine
publisher.none.fl_str_mv Korean Society for Laboratory Medicine
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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