Cognitive impairment three months after surgery is an independent predictor of survival time in glioblastoma patients.
Brain tumor
Cognitive functioning
Glioblastoma
Karnofsky performance status
Survival
Journal
Journal of neuro-oncology
ISSN: 1573-7373
Titre abrégé: J Neurooncol
Pays: United States
ID NLM: 8309335
Informations de publication
Date de publication:
Aug 2020
Aug 2020
Historique:
received:
16
01
2020
accepted:
25
06
2020
pubmed:
10
7
2020
medline:
22
6
2021
entrez:
10
7
2020
Statut:
ppublish
Résumé
Cognitive functioning is increasingly investigated for its prognostic value in glioblastoma (GBM) patients, but the association of cognitive status during early adjuvant treatment with survival time is unclear. The aim of this study was to determine whether cognitive performance three months after surgical resection predicted survival time, while using a clinically intuitive time ratio (TR) statistic. Newly diagnosed patients with GBM undergoing resection between November 2010 and February 2018 completed computerized cognitive assessment 3 months after surgery with the CNS Vital Signs battery (8 measures). The association of cognitive performance (continuous Z scores and dichotomous impairment status; impaired vs. unimpaired) with survival time was assessed with multivariate Accelerated Failure Time (AFT) models that also included clinical prognostic factors and covariates related to cognitive performances. 114 patients were included in the analyses (median survival time 16.4 months). Of the clinical factors, postoperative Karnofsky Performance Status (TR 1.51), surgical (TR 2.20) and non-surgical (TR 1.94) salvage treatment, and pre-surgical tumor volume (cm These findings suggest that impaired performances on tests of executive control and processing speed in the early phase of adjuvant treatment can reflect a worse prognostic outlook rather than an early treatment effect, and their assessment might allow for early refinement of current prognostic stratification.
Identifiants
pubmed: 32643066
doi: 10.1007/s11060-020-03577-7
pii: 10.1007/s11060-020-03577-7
pmc: PMC7452884
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
103-111Subventions
Organisme : CZ fonds
ID : 201300447
Organisme : CZ fonds
ID : 201500028
Organisme : Zonmw
ID : 824003007
Références
Chambless LB, Kistka HM, Parker SL et al (2015) The relative value of postoperative versus preoperative Karnofsky Performance Scale scores as a predictor of survival after surgical resection of glioblastoma multiforme. J Neurooncol 121(2):359–364. https://doi.org/10.1007/s11060-014-1640-x
doi: 10.1007/s11060-014-1640-x
pubmed: 25344883
Li J, Wang M, Won M et al (2011) Validation and simplification of the radiation therapy oncology group recursive partitioning analysis classification for glioblastoma. Int J Radiat Oncol Biol Phys 81(3):623–630. https://doi.org/10.1016/j.ijrobp.2010.06.012
doi: 10.1016/j.ijrobp.2010.06.012
pubmed: 20888136
Stark AM, Stepper W, Mehdorn HM (2010) Outcome evaluation in glioblastoma patients using different ranking scores: KPS, GOS, mRS and MRC. Eur J Cancer Care (Engl) 19(1):39–44. https://doi.org/10.1111/j.1365-2354.2008.00956.x
doi: 10.1111/j.1365-2354.2008.00956.x
Hutchinson TA, Boyd NF, Feinstein AR et al (1979) Scientific problems in clinical scales, as demonstrated in the Karnofsky index of performance status. J Chron Dis 32(9–10):661–666
doi: 10.1016/0021-9681(79)90096-1
Noll KR, Bradshaw ME, Wefel JS et al (2017) Neurocognitive functioning is associated with functional independence in newly diagnosed patients with temporal lobe glioma. Neurooncol Pract 5(3):184–193. https://doi.org/10.1093/nop/npx028
doi: 10.1093/nop/npx028
pubmed: 30094046
pmcid: 6075221
Gately L, Collins A, Murphy M et al (2016) Age alone is not a predictor for survival in glioblastoma. J Neurooncol 129(3):479–485. https://doi.org/10.1007/s11060-016-2194-x
doi: 10.1007/s11060-016-2194-x
pubmed: 27406585
Lamborn KR, Chang SM, Prados MD (2004) Prognostic factors for survival of patients with glioblastoma: recursive partitioning analysis. Neuro Oncol 6(3):227–235. https://doi.org/10.1215/S1152851703000620
doi: 10.1215/S1152851703000620
pubmed: 15279715
pmcid: 1871999
Asher A, Fu JB, Bailey C et al (2016) Fatigue among patients with brain tumors. CNS Oncol 5(2):91–100. https://doi.org/10.1016/j.soncn.2018.10.010
doi: 10.1016/j.soncn.2018.10.010
pubmed: 26987038
pmcid: 6047436
Johnson DR, Wefel JS (2013) Relationship between cognitive function and prognosis in glioblastoma. CNS Oncol 2(2):195–201. https://doi.org/10.2217/cns.13.5
doi: 10.2217/cns.13.5
pubmed: 25057978
pmcid: 6169490
Brown PD, Buckner JC, O'Fallon JR et al (2004) Importance of baseline mini-mental state examination as a prognostic factor for patients with low-grade glioma. Int J Radiat Oncol Biol Phys 59(1):117–125. https://doi.org/10.1016/j.ijrobp.2003.10.040
doi: 10.1016/j.ijrobp.2003.10.040
pubmed: 15093907
Lee S-T, Park C-K, Kim JW et al (2015) Early cognitive function tests predict early progression in glioblastoma. Neurooncol Pract 2(3):137–143. https://doi.org/10.1093/nop/npv007
doi: 10.1093/nop/npv007
pubmed: 31386094
pmcid: 6668270
Taphoorn MJ, Klein M (2004) Cognitive deficits in adult patients with brain tumours. Lancet Neurol 3:159–168. https://doi.org/10.1016/s1474-4422(04)00680-5
doi: 10.1016/s1474-4422(04)00680-5
pubmed: 14980531
Ali FS, Hussain MR, Gutiérrez C et al (2018) Cognitive disability in adult patients with brain tumors. Cancer Treat Rev 65:33–40. https://doi.org/10.1016/j.ctrv.2018.02.007
doi: 10.1016/j.ctrv.2018.02.007
pubmed: 29533821
Habets EJ, Kloet A, Walchenbach R et al (2014) Tumour and surgery effects on cognitive functioning in high grade glioma patients. Acta Neurochir (Wien) 156(8):1451–1459. https://doi.org/10.1007/s00701-014-2115-8
doi: 10.1007/s00701-014-2115-8
van Loenen IS, Rijnen SJM, Bruijn J et al (2018) Group changes in cognitive performance after surgery mask changes in individual patients with glioblastoma. World Neurosurg 117:e172–e179. https://doi.org/10.1016/j.wneu.2018.05.232
doi: 10.1016/j.wneu.2018.05.232
pubmed: 29886297
Gorlia T, van den Bent MJ, Hegi ME et al (2008) Nomograms for predicting survival of patients with newly diagnosed glioblastoma: prognostic factor analysis of EORTC and NCIC trial 26981–22981/CE.3. Lancet Oncol 9(1):29–38. https://doi.org/10.1016/S1470-2045(07)70384-4
doi: 10.1016/S1470-2045(07)70384-4
pubmed: 18082451
Johnson DR, Sawyer AM, Meyers CA et al (2012) Early measures of cognitive function predict survival in Patients with newly diagnosed glioblastoma. Neuro Oncol 14(6):808–816. https://doi.org/10.1093/neuonc/nos082
doi: 10.1093/neuonc/nos082
pubmed: 22508762
pmcid: 3367851
Klein M, Postma TJ, Taphoorn MJ et al (2003) The prognostic value of cognitive functioning in the survival of Patients with high-grade glioma. Neurology 61(12):1796–1798. https://doi.org/10.1212/01.WNL.0000098892.33018.4C
doi: 10.1212/01.WNL.0000098892.33018.4C
pubmed: 14694052
Noll KR, Sullaway CM, Wefel JS (2019) Depressive symptoms and executive function in relation to survival in patients with glioblastoma. J Neurooncol 142(1):183–191. https://doi.org/10.1007/s11060-018-03081-z
doi: 10.1007/s11060-018-03081-z
pubmed: 30680509
Tanzilli A, Pace A, Fabi A et al (2019) Neurocognitive evaluation in older adult patients affected by glioma. J Geriatr Oncol. https://doi.org/10.1016/j.jgo.2019.06.015
doi: 10.1016/j.jgo.2019.06.015
pubmed: 31277954
Wei LJ (1992) The accelerated failure time model: a useful alternative to the cox regression model in survival analysis. Stat Med 11(14–15):1871–1879. https://doi.org/10.1002/sim.4780111409
doi: 10.1002/sim.4780111409
pubmed: 1480879
Butterbrod E, Bruijn J, Braaksma MM et al (2019) Predicting disease progression in high-grade glioma with neuropsychological parameters: the value of personalized longitudinal assessment. J Neurooncol 144(3):511–518. https://doi.org/10.1007/s11060-019-03249-1
doi: 10.1007/s11060-019-03249-1
pubmed: 31342318
pmcid: 6764928
Gualtieri CT, Johnson LG (2006) Reliability and validity of a computerized neurocognitive test battery. CNS Vital Signs Arch Clin Neuropsychol 21(7):623–643. https://doi.org/10.1016/j.acn.2006.05.007
doi: 10.1016/j.acn.2006.05.007
pubmed: 17014981
Rijnen SJM, Meskal I, Emons WHM et al (2017) Evaluation of normative data of a widely used computerized neuropsychological battery: applicability and effects of sociodemographic variables in a dutch sample. Assessment. https://doi.org/10.1177/1073191117727346
doi: 10.1177/1073191117727346
pubmed: 28895436
pmcid: 6990455
Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Stat Methodol 57(1):289–300
Therneau T (2015) A package for survival analysis in S. Version 2.44–1.1. https://CRAN.Rproject.org/package=survival
Hilverda K, Bosma I, Heimans JJ et al (2010) Cognitive functioning in glioblastoma patients during radiotherapy and temozolomide treatment: initial findings. J Neurooncol 97(1):89–94. https://doi.org/10.1007/s11060-0099993-2
doi: 10.1007/s11060-0099993-2
pubmed: 19718545
Brown PD, Jensen AW, Felten SJ et al (2006) Detrimental effects of tumor progression on cognitive function of patients with high-grade glioma. J Clin Oncol 24(34):5427–5433. https://doi.org/10.1200/jco.2006.08.5605
doi: 10.1200/jco.2006.08.5605
pubmed: 17135644
Wefel JS, Kayl AE, Meyers CA (2004) Neuropsychological dysfunction associated with cancer and cancer therapies: a conceptual review of an emerging target. Br J Cancer 90(9):1691–1696. https://doi.org/10.1038/sj.bjc.6601772
doi: 10.1038/sj.bjc.6601772
pubmed: 15150608
pmcid: 2410277
Wu PH, Coultrap S, Pinnix C et al (2012) Radiation induces acute alterations in neuronal function. PLoS ONE 7(5):e37677. https://doi.org/10.1371/journal.pone.0037677
doi: 10.1371/journal.pone.0037677
pubmed: 22662188
pmcid: 3360766
Butler JM, Rapp SR, Shaw EG (2006) Managing the cognitive effects of brain tumor radiation therapy. Curr Treat Options Oncol 7(6):517–523. https://doi.org/10.1007/s11864-006-0026-5
doi: 10.1007/s11864-006-0026-5
pubmed: 17032563
Banich MT (2009) Executive function: the search for an integrated account. Curr Dir Psychol Sci 18(2):89–94
doi: 10.1111/j.1467-8721.2009.01615.x
Leavitt VM, Wylie G, Krch D et al (2014) Does slowed processing speed account for executive deficits in Multiple sclerosis? Evidence from neuropsychological performance and structural neuroimaging. Rehabil Psychol 59(4):422–428. https://doi.org/10.1037/a0037517
doi: 10.1037/a0037517
pubmed: 25133903
Giovagnoli AR (2012) Investigation of cognitive impairments in people with brain tumors. J Neurooncol 108(2):277–283. https://doi.org/10.1007/s11060-012-0815-6
doi: 10.1007/s11060-012-0815-6
pubmed: 22392124
MacPherson SE, Cox SR, Dickie DA et al (2017) Processing speed and the relationship between Trail Making Test-B performance, cortical thinning and white matter microstructure in older adults. Cortex 95:92–103. https://doi.org/10.1016/j.cortex.2017.07.021
doi: 10.1016/j.cortex.2017.07.021
pubmed: 28865241
pmcid: 5637162
Tombaugh TN (2004) Trail Making Test A and B: Normative data stratified by age and education. Arch Clin Neuropsychol 19(2):203–214. https://doi.org/10.1016/S0887-6177(03)00039-8
doi: 10.1016/S0887-6177(03)00039-8
pubmed: 15010086
Louis DN, Perry AP, Reifenberger et al (2016) The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. https://doi.org/10.1007/s00401-016-1545-1
doi: 10.1007/s00401-016-1545-1
pubmed: 27704282
pmcid: 5481163
Molinaro AM, Taylor JW, Wiencke JK et al (2019) Genetic and molecular epidemiology of adult diffuse glioma. Nat Rev Neurol 15(7):405–417. https://doi.org/10.1038/s41582-019-0220-2
doi: 10.1038/s41582-019-0220-2
pubmed: 31227792
pmcid: 7286557
Hartmann C, Hentschel B, Wick W et al (2010) Patients with IDH1 wild type anaplastic astrocytomas exhibit worse prognosis than IDH1-mutated glioblastomas, and IDH1 mutation status accounts for the unfavorable prognostic effect of higher age: implications for classification of gliomas. Acta Neuropathol 120(6):707–718. https://doi.org/10.1007/s00401-010-0781-z
doi: 10.1007/s00401-010-0781-z
pubmed: 21088844
van Kessel E, Emons MAC, Wajer IH et al (2019) Tumor-related neurocognitive dysfunction in patients with diffuse glioma: a retrospective cohort study prior to antitumor treatment. Neurooncol Pract. https://doi.org/10.1093/nop/npz008
doi: 10.1093/nop/npz008
pubmed: 31832216
Kay R, Kinnersley N (2002) On the use of the accelerated failure time model as an alternative to the Proportional hazards model in the treatment of time to event data: a case study in influenza. Ther Innov Regul Sci 36(3):571–579. https://doi.org/10.1177/009286150203600312
doi: 10.1177/009286150203600312