About: A Quantitative Framework for Modeling COVID-19 Risk During Adjuvant Therapy Using Published Randomized Trials of Glioblastoma in the Elderly

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About: A Quantitative Framework for Modeling COVID-19 Risk During Adjuvant Therapy Using Published Randomized Trials of Glioblastoma in the Elderly Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A Quantitative Framework for Modeling COVID-19 Risk During Adjuvant Therapy Using Published Randomized Trials of Glioblastoma in the Elderly
Creator	Alexander, B Alexander, Brian Cagney, D Cagney, Daniel Fell, Geoffrey Rahman, R Rahman, Rifaquat Tabrizi, S Tabrizi, Shervin Tanguturi, S Tanguturi, Shyam Trippa, Lorenzo Ventz, Steffen Wen, Patrick
Source	Medline; PMC
abstract	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: 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: 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: 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.
has issue date	2020-04-27(xsd:dateTime)
bibo:doi	10.1093/neuonc/noaa111
bibo:pmid	32339235
has license	no-cc
sha1sum (hex)	32101f348566b51e572010df1708537a62ca6a2e
schema:url	https://doi.org/10.1093/neuonc/noaa111
resource representing a document's title	A Quantitative Framework for Modeling COVID-19 Risk During Adjuvant Therapy Using Published Randomized Trials of Glioblastoma in the Elderly
has PubMed Central identifier	PMC7197582
has PubMed identifier	32339235
schema:publication	Neuro Oncol
resource representing a document's body	covid:32101f348566b51e572010df1708537a62ca6a2e#body_text
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