Optimizing management of the elderly patient with glioblastoma: Survival prediction online tool based on BC Cancer Registry real-world data. Issue 1 (13th April 2022)

• Record Type: Journal Article
• Authors: Zhao, Rachel; Zeng, Jonathan; DeVries, Kimberly; Proulx, Ryan; Krauze, Andra Valentina
• Is Part Of: Neuro-oncology advances. Volume 4:Issue 1(2022)
• Publication Date: 2022-04-13
• Subjects: elderly -- glioblastoma -- radiation -- real-world data -- survival prediction; 616.99481
• DOI: 10.1093/noajnl/vdac052
• Languages: English
• ISSNs: 2632-2498

Abstract

Abstract: Background: Glioblastoma (GBM) is associated with fatal outcomes and devastating neurological presentations especially impacting the elderly. Management remains controversial and representation in clinical trials poor. We generated 2 nomograms and a clinical decision making web tool using real-world data. Methods: Patients ≥60 years of age with histologically confirmed GBM (ICD-O-3 histology codes 9440/3, 9441/3, and 9442/3) diagnosed 2005–2015 were identified from the BC Cancer Registry ( n = 822). Seven hundred and twenty-nine patients for which performance status was captured were included in the analysis. Age, performance and resection status, administration of radiation therapy (RT), and chemotherapy were reviewed. Nomograms predicting 6- and 12-month overall survival (OS) probability were developed using Cox proportional hazards regression internally validated by c -index. A web tool powered by JavaScript was developed to calculate the survival probability. Results: Median OS was 6.6 months (95% confidence interval [CI] 6–7.2 months). Management involved concurrent chemoradiation (34%), RT alone (42%), and chemo alone (2.3%). Twenty-one percent of patients did not receive treatment beyond surgical intervention. Age, performance status, extent of resection, chemotherapy, and RT administration were all significant independent predictors of OS. Patients <80 years old who received RT had a significant survival advantage, regardless of extent of resection (hazard ratio range from 0.22 to 0.60, CI 0.15–0.95). A nomogram was constructed from all 729 patients (Harrell's Concordance Index = 0.78 [CI 0.71–0.84]) with a second nomogram based on subgroup analysis of the 452 patients who underwent RT (Harrell's Concordance Index = 0.81 [CI 0.70–0.90]). An online calculator based on both nomograms was generated for clinical use. Conclusions: Two nomograms and accompanying web tool incorporating commonly captured clinical features were generated based on real-world data to optimize decision making in the clinic.