A Quantitative Framework for Modeling COVID-19 Risk During Adjuvant Therapy Using Published Randomized Trials of Glioblastoma in the Elderly.
COVID-19
Glioblastoma
elderly
randomized controlled trials
Journal
Neuro-oncology
ISSN: 1523-5866
Titre abrégé: Neuro Oncol
Pays: England
ID NLM: 100887420
Informations de publication
Date de publication:
27 Apr 2020
27 Apr 2020
Historique:
received:
08
04
2020
entrez:
28
4
2020
pubmed:
28
4
2020
medline:
28
4
2020
Statut:
aheadofprint
Résumé
During the ongoing COVID-19 pandemic, contact with the healthcare system for cancer treatment can increase risk of infection and associated mortality. Treatment recommendations must consider this risk for elderly and vulnerable cancer patients. We re-analyzed trials in elderly glioblastoma (GBM) patients, incorporating COVID-19 risk, in order to provide a quantitative framework for comparing different radiation (RT) fractionation schedules on patient outcomes. We extracted individual patient-level data (IPLD) for 1,321 patients from Kaplan-Meier curves from five randomized trials on treatment of elderly GBM patients including available subanalyses based on MGMT methylation status. We simulated trial data with incorporation of COVID-19 associated mortality risk in several scenarios (low, medium, and high infection and mortality risks). Median overall survival and hazard ratios were calculated for each simulation replicate. Our simulations reveal how COVID-19-associated risks affect survival under different treatment regimens. Hypofractionated RT with concurrent and adjuvant temozolomide (TMZ) demonstrated the best outcomes in low and medium risk scenarios. In frail elderly patients, shorter courses of RT are preferable. In patients with methylated MGMT receiving single modality treatment, TMZ-alone treatment approaches may be an option in settings with high COVID-19-associated risk. Incorporation of COVID-19-associated risk models into analysis of randomized trials can help guide clinical decisions during this pandemic. In elderly GBM patients, our results support prioritization of hypofractionated RT and highlight the utility of MGMT methylation status in decision-making in pandemic scenarios. Our quantitative framework can serve as a model for assessing COVID-19 risk associated with treatment across neuro-oncology.
Sections du résumé
BACKGROUND
BACKGROUND
During the ongoing COVID-19 pandemic, contact with the healthcare system for cancer treatment can increase risk of infection and associated mortality. Treatment recommendations must consider this risk for elderly and vulnerable cancer patients. We re-analyzed trials in elderly glioblastoma (GBM) patients, incorporating COVID-19 risk, in order to provide a quantitative framework for comparing different radiation (RT) fractionation schedules on patient outcomes.
METHODS
METHODS
We extracted individual patient-level data (IPLD) for 1,321 patients from Kaplan-Meier curves from five randomized trials on treatment of elderly GBM patients including available subanalyses based on MGMT methylation status. We simulated trial data with incorporation of COVID-19 associated mortality risk in several scenarios (low, medium, and high infection and mortality risks). Median overall survival and hazard ratios were calculated for each simulation replicate.
RESULTS
RESULTS
Our simulations reveal how COVID-19-associated risks affect survival under different treatment regimens. Hypofractionated RT with concurrent and adjuvant temozolomide (TMZ) demonstrated the best outcomes in low and medium risk scenarios. In frail elderly patients, shorter courses of RT are preferable. In patients with methylated MGMT receiving single modality treatment, TMZ-alone treatment approaches may be an option in settings with high COVID-19-associated risk.
CONCLUSIONS
CONCLUSIONS
Incorporation of COVID-19-associated risk models into analysis of randomized trials can help guide clinical decisions during this pandemic. In elderly GBM patients, our results support prioritization of hypofractionated RT and highlight the utility of MGMT methylation status in decision-making in pandemic scenarios. Our quantitative framework can serve as a model for assessing COVID-19 risk associated with treatment across neuro-oncology.
Identifiants
pubmed: 32339235
pii: 5825707
doi: 10.1093/neuonc/noaa111
pmc: PMC7197582
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Références
Cancer Discov. 2020 Jun;10(6):783-791
pubmed: 32345594
Clin Cancer Res. 2019 Nov 1;25(21):6339-6345
pubmed: 31345838
J Clin Oncol. 2015 Dec 10;33(35):4145-50
pubmed: 26392096
BMC Med Res Methodol. 2012 Feb 01;12:9
pubmed: 22297116
Adv Radiat Oncol. 2020 Apr 01;5(4):659-665
pubmed: 32292839
N Engl J Med. 2020 Apr 30;382(18):1708-1720
pubmed: 32109013
Lancet Infect Dis. 2020 Jun;20(6):669-677
pubmed: 32240634
Lancet Oncol. 2009 May;10(5):459-66
pubmed: 19269895
JAMA Neurol. 2015 May;72(5):589-96
pubmed: 25822375
N Engl J Med. 2017 Mar 16;376(11):1027-1037
pubmed: 28296618
Adv Radiat Oncol. 2020 Apr 01;5(4):582-588
pubmed: 32292842
J Clin Oncol. 2004 May 1;22(9):1583-8
pubmed: 15051755
N Engl J Med. 2005 Mar 10;352(10):987-96
pubmed: 15758009
JAMA. 2020 Apr 7;323(13):1239-1242
pubmed: 32091533
Ann Oncol. 2020 Jul;31(7):894-901
pubmed: 32224151
Lancet Oncol. 2012 Sep;13(9):916-26
pubmed: 22877848
N Engl J Med. 2005 Mar 10;352(10):997-1003
pubmed: 15758010
Lancet Oncol. 2009 Jun;10(6):589-97
pubmed: 19482247
Ann Oncol. 2019 Jun 1;30(6):1005-1013
pubmed: 30860592
Lancet Oncol. 2012 Jul;13(7):707-15
pubmed: 22578793
Ann Intern Med. 2020 May 5;172(9):577-582
pubmed: 32150748
Lancet Oncol. 2020 Mar;21(3):335-337
pubmed: 32066541
N Engl J Med. 2007 Apr 12;356(15):1527-35
pubmed: 17429084
Neuro Oncol. 2012 Nov;14 Suppl 5:v1-49
pubmed: 23095881
Neuro Oncol. 2020 Aug 17;22(8):1162-1172
pubmed: 32064499
J Neurooncol. 2020 Jan;146(2):311-320
pubmed: 31894517
Clin Cancer Res. 2020 Jun 1;26(11):2664-2672
pubmed: 31953312