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Determinants and predictors for the long-term disease burden of intracranial meningioma patients

INTRODUCTION: Meningioma is a heterogeneous disease and patients may suffer from long-term tumor- and treatment-related sequelae. To help identify patients at risk for these late effects, we first assessed variables associated with impaired long-term health-related quality of life (HRQoL) and impair...

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Autores principales: Zamanipoor Najafabadi, Amir H., van der Meer, Pim B., Boele, Florien W., Taphoorn, Martin J. B., Klein, Martin, Peerdeman, Saskia M., van Furth, Wouter R., Dirven, Linda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875939/
https://www.ncbi.nlm.nih.gov/pubmed/33073326
http://dx.doi.org/10.1007/s11060-020-03650-1
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author Zamanipoor Najafabadi, Amir H.
van der Meer, Pim B.
Boele, Florien W.
Taphoorn, Martin J. B.
Klein, Martin
Peerdeman, Saskia M.
van Furth, Wouter R.
Dirven, Linda
author_facet Zamanipoor Najafabadi, Amir H.
van der Meer, Pim B.
Boele, Florien W.
Taphoorn, Martin J. B.
Klein, Martin
Peerdeman, Saskia M.
van Furth, Wouter R.
Dirven, Linda
author_sort Zamanipoor Najafabadi, Amir H.
collection PubMed
description INTRODUCTION: Meningioma is a heterogeneous disease and patients may suffer from long-term tumor- and treatment-related sequelae. To help identify patients at risk for these late effects, we first assessed variables associated with impaired long-term health-related quality of life (HRQoL) and impaired neurocognitive function on group level (i.e. determinants). Next, prediction models were developed to predict the risk for long-term neurocognitive or HRQoL impairment on individual patient-level. METHODS: Secondary data analysis of a cross-sectional multicenter study with intracranial WHO grade I/II meningioma patients, in which HRQoL (Short-Form 36) and neurocognitive functioning (standardized test battery) were assessed. Multivariable regression models were used to assess determinants for these outcomes corrected for confounders, and to build prediction models, evaluated with C-statistics. RESULTS: Data from 190 patients were analyzed (median 9 years after intervention). Main determinants for poor HRQoL or impaired neurocognitive function were patients’ sociodemographic characteristics, surgical complications, reoperation, radiotherapy, presence of edema, and a larger tumor diameter on last MRI. Prediction models with a moderate/good ability to discriminate between individual patients with and without impaired HRQoL (C-statistic 0.73, 95% CI 0.65 to 0.81) and neurocognitive function (C-statistic 0.78, 95%CI 0.70 to 0.85) were built. Not all predictors (e.g. tumor location) within these models were also determinants. CONCLUSIONS: The identified determinants help clinicians to better understand long-term meningioma disease burden. Prediction models can help early identification of individual patients at risk for long-term neurocognitive or HRQoL impairment, facilitating tailored provision of information and allocation of scarce supportive care services to those most likely to benefit. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11060-020-03650-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-78759392021-02-22 Determinants and predictors for the long-term disease burden of intracranial meningioma patients Zamanipoor Najafabadi, Amir H. van der Meer, Pim B. Boele, Florien W. Taphoorn, Martin J. B. Klein, Martin Peerdeman, Saskia M. van Furth, Wouter R. Dirven, Linda J Neurooncol Clinical Study INTRODUCTION: Meningioma is a heterogeneous disease and patients may suffer from long-term tumor- and treatment-related sequelae. To help identify patients at risk for these late effects, we first assessed variables associated with impaired long-term health-related quality of life (HRQoL) and impaired neurocognitive function on group level (i.e. determinants). Next, prediction models were developed to predict the risk for long-term neurocognitive or HRQoL impairment on individual patient-level. METHODS: Secondary data analysis of a cross-sectional multicenter study with intracranial WHO grade I/II meningioma patients, in which HRQoL (Short-Form 36) and neurocognitive functioning (standardized test battery) were assessed. Multivariable regression models were used to assess determinants for these outcomes corrected for confounders, and to build prediction models, evaluated with C-statistics. RESULTS: Data from 190 patients were analyzed (median 9 years after intervention). Main determinants for poor HRQoL or impaired neurocognitive function were patients’ sociodemographic characteristics, surgical complications, reoperation, radiotherapy, presence of edema, and a larger tumor diameter on last MRI. Prediction models with a moderate/good ability to discriminate between individual patients with and without impaired HRQoL (C-statistic 0.73, 95% CI 0.65 to 0.81) and neurocognitive function (C-statistic 0.78, 95%CI 0.70 to 0.85) were built. Not all predictors (e.g. tumor location) within these models were also determinants. CONCLUSIONS: The identified determinants help clinicians to better understand long-term meningioma disease burden. Prediction models can help early identification of individual patients at risk for long-term neurocognitive or HRQoL impairment, facilitating tailored provision of information and allocation of scarce supportive care services to those most likely to benefit. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11060-020-03650-1) contains supplementary material, which is available to authorized users. Springer US 2020-10-19 2021 /pmc/articles/PMC7875939/ /pubmed/33073326 http://dx.doi.org/10.1007/s11060-020-03650-1 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Clinical Study
Zamanipoor Najafabadi, Amir H.
van der Meer, Pim B.
Boele, Florien W.
Taphoorn, Martin J. B.
Klein, Martin
Peerdeman, Saskia M.
van Furth, Wouter R.
Dirven, Linda
Determinants and predictors for the long-term disease burden of intracranial meningioma patients
title Determinants and predictors for the long-term disease burden of intracranial meningioma patients
title_full Determinants and predictors for the long-term disease burden of intracranial meningioma patients
title_fullStr Determinants and predictors for the long-term disease burden of intracranial meningioma patients
title_full_unstemmed Determinants and predictors for the long-term disease burden of intracranial meningioma patients
title_short Determinants and predictors for the long-term disease burden of intracranial meningioma patients
title_sort determinants and predictors for the long-term disease burden of intracranial meningioma patients
topic Clinical Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875939/
https://www.ncbi.nlm.nih.gov/pubmed/33073326
http://dx.doi.org/10.1007/s11060-020-03650-1
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