BACKGROUND: Elderly patients with glioblastoma have an especially poor prognosis; optimizing their medical and surgical care remains of paramount importance. OBJECTIVE: To investigate patient and treatment characteristics of elderly vs nonelderly patients and develop an algorithm to predict elderly patients' survival. METHODS: Retrospective analysis of 554 patients (mean age = 60.8; 42.0% female) undergoing first glioblastoma resection or biopsy at our institution (2005-2011). RESULTS: Of the 554 patients, 218 (39%) were elderly (≥65 yr). Compared with nonelderly, elderly patients were more likely to receive biopsy only (26% vs 16%), have ≥1 medical comorbidity (40% vs 20%), and develop postresection morbidity (eg, seizure, delirium; 25% vs 14%), and were less likely to receive temozolomide (TMZ) (78% vs 90%) and gross total resection (31% vs 45%). To predict benefit of resection in elderly patients (n = 161), we identified 5 factors known in the preoperative period that predicted survival in a multivariate analysis. We then assigned points to each (1 point: Charlson comorbidity score >0, subtotal resection, tumor >3 cm; 2 points: preoperative weakness, Charlson comorbidity score >1, tumor >5 cm, age >75 yr; 4 points: age >85 yr). Having 3 to 5 points (n = 78, 56%) was associated with decreased survival compared to 0 to 2 points (n = 41, 29%, 8.5 vs 16.9 mo; P = .001) and increased survival compared to 6 to 9 points (n = 20, 14%, 8.5 vs 4.5 mo; P < .001). Patients with 6 to 9 points did not survive significantly longer than elderly patients receiving biopsy only (n = 57, 4.5 vs 2.7 mo; P = .58). CONCLUSION: Further optimization of the medical and surgical care of elderly glioblastoma patients may be achieved by providing more beneficial therapies while avoiding unnecessary resection in those not likely to receive benefit from this intervention.
BACKGROUND: Elderly patients with glioblastoma have an especially poor prognosis; optimizing their medical and surgical care remains of paramount importance. OBJECTIVE: To investigate patient and treatment characteristics of elderly vs nonelderly patients and develop an algorithm to predict elderly patients' survival. METHODS: Retrospective analysis of 554 patients (mean age = 60.8; 42.0% female) undergoing first glioblastoma resection or biopsy at our institution (2005-2011). RESULTS: Of the 554 patients, 218 (39%) were elderly (≥65 yr). Compared with nonelderly, elderly patients were more likely to receive biopsy only (26% vs 16%), have ≥1 medical comorbidity (40% vs 20%), and develop postresection morbidity (eg, seizure, delirium; 25% vs 14%), and were less likely to receive temozolomide (TMZ) (78% vs 90%) and gross total resection (31% vs 45%). To predict benefit of resection in elderly patients (n = 161), we identified 5 factors known in the preoperative period that predicted survival in a multivariate analysis. We then assigned points to each (1 point: Charlson comorbidity score >0, subtotal resection, tumor >3 cm; 2 points: preoperative weakness, Charlson comorbidity score >1, tumor >5 cm, age >75 yr; 4 points: age >85 yr). Having 3 to 5 points (n = 78, 56%) was associated with decreased survival compared to 0 to 2 points (n = 41, 29%, 8.5 vs 16.9 mo; P = .001) and increased survival compared to 6 to 9 points (n = 20, 14%, 8.5 vs 4.5 mo; P < .001). Patients with 6 to 9 points did not survive significantly longer than elderly patients receiving biopsy only (n = 57, 4.5 vs 2.7 mo; P = .58). CONCLUSION: Further optimization of the medical and surgical care of elderly glioblastomapatients may be achieved by providing more beneficial therapies while avoiding unnecessary resection in those not likely to receive benefit from this intervention.
Authors: Ankush Chandra; Jonathan W Rick; Cecilia Dalle Ore; Darryl Lau; Alan T Nguyen; Diego Carrera; Alexander Bonte; Annette M Molinaro; Philip V Theodosopoulos; Michael W McDermott; Mitchel S Berger; Manish K Aghi Journal: Neurosurg Focus Date: 2018-06 Impact factor: 4.047
Authors: P R Kappen; E Kakar; C M F Dirven; M van der Jagt; M Klimek; R J Osse; A P J E Vincent Journal: Neurosurg Rev Date: 2021-08-16 Impact factor: 2.800
Authors: Marc Zanello; Alexandre Roux; Renata Ursu; Sophie Peeters; Luc Bauchet; Georges Noel; Jacques Guyotat; Pierre-Jean Le Reste; Thierry Faillot; Fabien Litre; Nicolas Desse; Evelyne Emery; Antoine Petit; Johann Peltier; Jimmy Voirin; François Caire; Jean-Luc Barat; Jean-Rodolphe Vignes; Philippe Menei; Olivier Langlois; Edouard Dezamis; Antoine Carpentier; Phong Dam Hieu; Philippe Metellus; Johan Pallud Journal: J Neurooncol Date: 2017-07-19 Impact factor: 4.130
Authors: Theodore Wang; Anthony Pham; Stella Yoo; Frank J Attenello; Richard Jennelle; Naveed Wagle; Eric L Chang; Gabriel Zada Journal: World Neurosurg Date: 2020-01-27 Impact factor: 2.104