Table_1_A Prognostic Model for Glioblastoma Patients Treated With Standard Therapy Based on a Prospective Cohort of Consecutive Non-Selected Patients .docx (45.73 kB)
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Table_1_A Prognostic Model for Glioblastoma Patients Treated With Standard Therapy Based on a Prospective Cohort of Consecutive Non-Selected Patients From a Single Institution.docx

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posted on 25.02.2021, 04:24 authored by Armita Armina Abedi, Kirsten Grunnet, Ib Jarle Christensen, Signe Regner Michaelsen, Aida Muhic, Søren Møller, Benedikte Hasselbalch, Hans Skovgaard Poulsen, Thomas Urup
Background

Glioblastoma patients administered standard therapies, comprising maximal surgical resection, radiation therapy with concomitant and adjuvant temozolomide, have a variable prognosis with a median overall survival of 15–16 months and a 2-year overall survival of 30%. The aim of this study was to develop a prognostic nomogram for overall survival for glioblastoma patients treated with standard therapy outside clinical trials.

Methods

The study included 680 consecutive, non-selected glioblastoma patients administered standard therapy as primary treatment between the years 2005 and 2016 at Rigshospitalet, Copenhagen, Denmark. The prognostic model was generated employing multivariate Cox regression analysis modeling overall survival.

Results

The following poor prognostic factors were included in the final prognostic model for overall survival: Age (10-year increase: HR = 1.18, 95% CI: 1.08–1.28, p < 0.001), ECOG performance status (PS) 1 vs. 0 (HR = 1.30, 95% CI: 1.07–1.57, p = 0.007), PS 2 vs. 0 (HR = 2.99, 95% CI: 1.99–4.50, p < 0.001), corticosteroid use (HR = 1.42, 95% CI: 1.18–1.70, p < 0.001), multifocal disease (HR = 1.63, 95% CI: 1.25–2.13, p < 0.001), biopsy vs. resection (HR = 1.35, 95% CI: 1.04–1.72, p = 0.02), un-methylated promoter of the MGMT (O6-methylguanine-DNA methyltransferase) gene (HR = 1.71, 95% CI: 1.42–2.04, p < 0.001). The model was validated internally and had a concordance index of 0.65.

Conclusion

A nomogram for overall survival was established. This model can be used for risk stratification and treatment planning, as well as improve enrollment criteria for clinical trials.

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