Journal of neuro-oncology · 2020
Determinants and predictors for the long-term disease burden of intracranial meningioma patients.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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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, Dutch Meningioma Consortium
Аннотация
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.
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.