Amir H Zamanipoor Najafabadi1,2,3, Pim B van der Meer4, Florien W Boele5,6, Martin J B Taphoorn4,7, Martin Klein8, Saskia M Peerdeman9, Wouter R van Furth10,11, Linda Dirven4,7. 1. Department of Neurosurgery, University Neurosurgical Center Holland, Leiden University Medical Center, Albinusdreef 2, Postal Zone J11-R, 2333ZA, Leiden, The Netherlands. amir@lumc.nl. 2. Haaglanden Medical Center & Haga Teaching Hospitals, The Hague, The Netherlands. amir@lumc.nl. 3. Department of Neurology, Leiden University Medical Center, Albinusdreef 2, Postal Zone J11-R, 2333ZA, Leiden, The Netherlands. amir@lumc.nl. 4. Department of Neurology, Leiden University Medical Center, Albinusdreef 2, Postal Zone J11-R, 2333ZA, Leiden, The Netherlands. 5. Leeds Institute of Medical Research at St James's, St James's University Hospital, Leeds, LS9 7TF, UK. 6. Faculty of Medicine and Health, Leeds Institute of Health Sciences, University of Leeds, Leeds, LS2 9JT, UK. 7. Department of Neurology, Haaglanden Medical Center, The Hague, The Netherlands. 8. Department of Medical Psychology, Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. 9. Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. 10. Department of Neurosurgery, University Neurosurgical Center Holland, Leiden University Medical Center, Albinusdreef 2, Postal Zone J11-R, 2333ZA, Leiden, The Netherlands. 11. Haaglanden Medical Center & Haga Teaching Hospitals, The Hague, The Netherlands.
Abstract
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.
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.
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Authors: Amir H Zamanipoor Najafabadi; Pim B van der Meer; Florien W Boele; Martin J B Taphoorn; Martin Klein; Saskia M Peerdeman; Wouter R van Furth; Linda Dirven Journal: Neurosurgery Date: 2020-12-15 Impact factor: 4.654
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