Assessment of Simulated SARS-CoV-2 Infection and Mortality Risk Associated With Radiation Therapy Among Patients in 8 Randomized Clinical Trials.


Journal

JAMA network open
ISSN: 2574-3805
Titre abrégé: JAMA Netw Open
Pays: United States
ID NLM: 101729235

Informations de publication

Date de publication:
01 03 2021
Historique:
entrez: 29 3 2021
pubmed: 30 3 2021
medline: 10 4 2021
Statut: epublish

Résumé

During the COVID-19 pandemic, cancer therapy may put patients at risk of SARS-CoV-2 infection and mortality. The impacts of proposed alternatives on reducing infection risk are unknown. To investigate how the COVID-19 pandemic is associated with the risks and benefits of standard radiation therapy (RT). This comparative effectiveness study used estimated individual patient-level data extracted from published Kaplan-Meier survival figures from 8 randomized clinical trials across oncology from 1993 to 2014 that evaluated the inclusion of RT or compared different RT fractionation regimens. Included trials were Dutch TME and TROG 01.04 examining rectal cancer; CALGB 9343, OCOG hypofractionation trial, FAST-Forward, and NSABP B-39 examining early stage breast cancer, and CHHiP and HYPO-RT-PC examining prostate cancer. Risk of SARS-CoV-2 infection and mortality associated with receipt of RT in the treatment arms were simulated and trials were reanalyzed. Data were analyzed between April 1, 2020, and June 30, 2020. COVID-19 risk associated with treatment was simulated across different pandemic scenarios, varying infection risk per fractions (IRFs) and case fatality rates (CFRs). Overall survival was evaluated using Cox proportional hazards modeling under different pandemic scenarios. Estimated IPLD from a total of 14 170 patients were included in the simulations. In scenarios with low COVID-19-associated risks (IRF, 0.5%; CFR, 5%), fractionation was not significantly associated with outcomes. In locally advanced rectal cancer, short-course RT was associated with better outcomes than long-course chemoradiation (TROG 01.04) and was associated with similar outcomes as RT omission (Dutch TME) in most settings (eg, TROG 01.04 median HR, 0.66 [95% CI, 0.46-0.96]; Dutch TME median HR, 0.91 [95% CI, 0.80-1.03] in a scenario with IRF 5% and CFR 20%). Moderate hypofractionation in early stage breast cancer (OCOG hypofractionation trial) and prostate cancer (CHHiP) was not associated with survival benefits in the setting of COVID-19 (eg, OCOG hypofractionation trial median HR, 0.89 [95% CI, 0.74-1.06]; CHHiP median HR, 0.87 [95% CI, 0.75-1.01] under high-risk scenario with IRF 10% and CFR 30%). More aggressive hypofractionation (FAST-Forward, HYPO-RT-PC) and accelerated partial breast irradiation (NSABP B-39) were associated with improved survival in higher risk scenarios (eg, FAST-Forward median HR, 0.58 [95% CI, 0.49-0.68]; HYPO-RT-PC median HR, 0.60 [95% CI, 0.48-0.75] under scenario with IRF 10% and CFR 30%). In this comparative effectiveness study of data from 8 clinical trials of patients receiving radiation therapy to simulate COVID-19 risk and mortality rates, treatment modification was not associated with altered risk from COVID-19 in lower-risk scenarios and was only associated with decreased mortality in very high COVID-19-risk scenarios. This model, which can be adapted to dynamic changes in COVID-19 risk, provides a flexible, quantitative approach to assess the potential impact of treatment modifications and supports the continued delivery of standard evidence-based care with appropriate precautions against COVID-19.

Identifiants

pubmed: 33779742
pii: 2777836
doi: 10.1001/jamanetworkopen.2021.3304
pmc: PMC8008289
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e213304

Commentaires et corrections

Type : CommentIn

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Auteurs

Shervin Tabrizi (S)

Harvard Radiation Oncology Program, Boston, Massachusetts.
Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts.

Lorenzo Trippa (L)

Dana-Farber Cancer Institute, Department of Biostatistics and Computational Biology, Harvard School of Public Health, Boston, Massachusetts.

Daniel Cagney (D)

Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts.

Ayal A Aizer (AA)

Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts.

Shyam Tanguturi (S)

Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts.

Steffen Ventz (S)

Dana-Farber Cancer Institute, Department of Biostatistics and Computational Biology, Harvard School of Public Health, Boston, Massachusetts.

Geoffrey Fell (G)

Dana-Farber Cancer Institute, Department of Biostatistics and Computational Biology, Harvard School of Public Health, Boston, Massachusetts.

Jennifer R Bellon (JR)

Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts.

Harvey Mamon (H)

Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts.

Paul L Nguyen (PL)

Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts.

Anthony V D'Amico (AV)

Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts.

Daphne Haas-Kogan (D)

Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts.

Brian M Alexander (BM)

Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts.

Rifaquat Rahman (R)

Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts.

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