A novel Iowa–Mayo validated composite risk assessment tool for allogeneic stem cell transplantation survival outcome prediction

Abstract Allogeneic hematopoietic stem cell transplantation (HSCT) is a curative option for many hematologic conditions and is associated with considerable morbidity and mortality. Therefore, prognostic tools are essential to navigate the complex patient, disease, donor, and transplant characteristi...

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Autores principales:	Kalyan Nadiminti, Kimberly Langer, Ehsan Shabbir, Mehrdad Hefazi, Lindsay Dozeman, Yogesh Jethava, Bradley Loeffler, Hassan B. AlKhateeb, Mark Litzow, Mrinal Patnaik, Mithun Shah, William Hogan, Umar Farooq, Margarida Silverman, Sarah L. Mott
Formato:	article
Lenguaje:	EN
Publicado:	Nature Publishing Group 2021
Materias:	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/b574ff5ee98a4af49edd007c2f6a50f0
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spelling	oai:doaj.org-article:b574ff5ee98a4af49edd007c2f6a50f02021-11-21T12:42:16ZA novel Iowa–Mayo validated composite risk assessment tool for allogeneic stem cell transplantation survival outcome prediction10.1038/s41408-021-00573-62044-5385https://doaj.org/article/b574ff5ee98a4af49edd007c2f6a50f02021-11-01T00:00:00Zhttps://doi.org/10.1038/s41408-021-00573-6https://doaj.org/toc/2044-5385Abstract Allogeneic hematopoietic stem cell transplantation (HSCT) is a curative option for many hematologic conditions and is associated with considerable morbidity and mortality. Therefore, prognostic tools are essential to navigate the complex patient, disease, donor, and transplant characteristics that differentially influence outcomes. We developed a novel, comprehensive composite prognostic tool. Using a lasso-penalized Cox regression model (n = 273), performance status, HCT-CI, refined disease-risk index (rDRI), donor and recipient CMV status, and donor age were identified as predictors of disease-free survival (DFS). The results for overall survival (OS) were similar except for recipient CMV status not being included in the model. Models were validated in an external dataset (n = 378) and resulted in a c-statistic of 0.61 and 0.62 for DFS and OS, respectively. Importantly, this tool incorporates donor age as a variable, which has an important role in HSCT outcomes. This needs to be further studied in prospective models. An easy-to-use and a web-based nomogram can be accessed here: https://allohsctsurvivalcalc.iowa.uiowa.edu/ .Kalyan NadimintiKimberly LangerEhsan ShabbirMehrdad HefaziLindsay DozemanYogesh JethavaBradley LoefflerHassan B. AlKhateebMark LitzowMrinal PatnaikMithun ShahWilliam HoganUmar FarooqMargarida SilvermanSarah L. MottNature Publishing GrouparticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENBlood Cancer Journal, Vol 11, Iss 11, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Kalyan Nadiminti Kimberly Langer Ehsan Shabbir Mehrdad Hefazi Lindsay Dozeman Yogesh Jethava Bradley Loeffler Hassan B. AlKhateeb Mark Litzow Mrinal Patnaik Mithun Shah William Hogan Umar Farooq Margarida Silverman Sarah L. Mott A novel Iowa–Mayo validated composite risk assessment tool for allogeneic stem cell transplantation survival outcome prediction
description	Abstract Allogeneic hematopoietic stem cell transplantation (HSCT) is a curative option for many hematologic conditions and is associated with considerable morbidity and mortality. Therefore, prognostic tools are essential to navigate the complex patient, disease, donor, and transplant characteristics that differentially influence outcomes. We developed a novel, comprehensive composite prognostic tool. Using a lasso-penalized Cox regression model (n = 273), performance status, HCT-CI, refined disease-risk index (rDRI), donor and recipient CMV status, and donor age were identified as predictors of disease-free survival (DFS). The results for overall survival (OS) were similar except for recipient CMV status not being included in the model. Models were validated in an external dataset (n = 378) and resulted in a c-statistic of 0.61 and 0.62 for DFS and OS, respectively. Importantly, this tool incorporates donor age as a variable, which has an important role in HSCT outcomes. This needs to be further studied in prospective models. An easy-to-use and a web-based nomogram can be accessed here: https://allohsctsurvivalcalc.iowa.uiowa.edu/ .
format	article
author	Kalyan Nadiminti Kimberly Langer Ehsan Shabbir Mehrdad Hefazi Lindsay Dozeman Yogesh Jethava Bradley Loeffler Hassan B. AlKhateeb Mark Litzow Mrinal Patnaik Mithun Shah William Hogan Umar Farooq Margarida Silverman Sarah L. Mott
author_facet	Kalyan Nadiminti Kimberly Langer Ehsan Shabbir Mehrdad Hefazi Lindsay Dozeman Yogesh Jethava Bradley Loeffler Hassan B. AlKhateeb Mark Litzow Mrinal Patnaik Mithun Shah William Hogan Umar Farooq Margarida Silverman Sarah L. Mott
author_sort	Kalyan Nadiminti
title	A novel Iowa–Mayo validated composite risk assessment tool for allogeneic stem cell transplantation survival outcome prediction
title_short	A novel Iowa–Mayo validated composite risk assessment tool for allogeneic stem cell transplantation survival outcome prediction
title_full	A novel Iowa–Mayo validated composite risk assessment tool for allogeneic stem cell transplantation survival outcome prediction
title_fullStr	A novel Iowa–Mayo validated composite risk assessment tool for allogeneic stem cell transplantation survival outcome prediction
title_full_unstemmed	A novel Iowa–Mayo validated composite risk assessment tool for allogeneic stem cell transplantation survival outcome prediction
title_sort	novel iowa–mayo validated composite risk assessment tool for allogeneic stem cell transplantation survival outcome prediction
publisher	Nature Publishing Group
publishDate	2021
url	https://doaj.org/article/b574ff5ee98a4af49edd007c2f6a50f0
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A novel Iowa–Mayo validated composite risk assessment tool for allogeneic stem cell transplantation survival outcome prediction

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