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What are the benefits and risks associated with delayed treatment for an individual patient with cancer during the coronavirus disease 2019 pandemic, and does the use of a web-based survival model (OncCOVID) aid treatment decisions?
In this decision analytical modeling study including data from more than 6 million patients with cancer, the OncCOVID model found heterogeneity regarding the impact of delayed cancer treatment owing to patient and cancer factors that are not currently captured by commonly used triage systems. Whether delayed cancer treatment harms or improves expected survival compared with immediate treatment is dependent on patient, cancer, treatment, and community factors.
The study’s results indicate that the OncCOVID web application may allow clinicians to estimate the net impact of delayed cancer treatment for individual patients and to prioritize patients for immediate treatment in settings with limited treatment capacity.
Cancer treatment delay has been reported to variably impact cancer-specific survival and coronavirus disease 2019 (COVID-19)–specific mortality during the severe acute respiratory syndrome coronavirus 2 pandemic. During the pandemic, treatment delay is being recommended in a nonquantitative, nonobjective, and nonpersonalized manner, and this approach may be associated with suboptimal outcomes. Quantitative integration of cancer mortality estimates and data on the consequences of treatment delay is needed to aid treatment decisions and improve patient outcomes.
To obtain quantitative integration of cancer-specific and COVID-19–specific mortality estimates that can be used to make optimal decisions for individual patients and optimize resource allocation.
Design, Setting, and Participants
In this decision analytical model, age-specific and stage-specific estimates of overall survival pre–COVID-19 were adjusted by the probability of COVID-19 (individualized by county, treatment-specific variables, hospital exposure frequency, and COVID-19 infectivity estimates), COVID-19 mortality (individualized by age-specific, comorbidity-specific, and treatment-specific variables), and delay of cancer treatment (impact and duration). These model estimates were integrated into a web application (OncCOVID) to calculate estimates of the cumulative overall survival and restricted mean survival time of patients who received immediate vs delayed cancer treatment. Using currently available information about COVID-19, a susceptible-infected-recovered model that accounted for the increased risk among patients at health care treatment centers was developed. This model integrated the data on cancer mortality and the consequences of treatment delay to aid treatment decisions. Age-specific and cancer stage–specific estimates of overall survival pre–COVID-19 were extracted from the Surveillance, Epidemiology, and End Results database for 691 854 individuals with 25 cancer types who received cancer diagnoses in 2005 to 2006. Data from 5 436 896 individuals in the National Cancer Database were used to estimate the independent impact of treatment delay by cancer type and stage. In addition, data from 275 patients in a nested case-control study were used to estimate the COVID-19 mortality rate by age group and number of comorbidities. Data were analyzed from March 17 to May 21, 2020.
COVID-19 and cancer.
Main Outcomes and Measures
Estimates of restricted mean survival time after the receipt of immediate vs delayed cancer treatment.
At the time of the study, the OncCOVID web application allowed for the selection of up to 47 individualized variables to assess net survival for an individual patient with cancer. Substantial heterogeneity was found regarding the association between delayed cancer treatment and net survival among patients with a given cancer type and stage, and these 2 variables were insufficient to discriminate the net impact of immediate vs delayed treatment. Individualized overall survival estimates were associated with patient age, number of comorbidities, treatment received, and specific local community estimates of COVID-19 risk.
Conclusions and Relevance
This decision analytical modeling study found that the OncCOVID web-based application can quantitatively aid in the resource allocation of individualized treatment for patients with cancer during the COVID-19 global pandemic.
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Accepted for Publication: August 4, 2020.
Corresponding Author: Matthew J. Schipper, PhD, Department of Radiation Oncology, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109 (email@example.com).
Published Online: October 29, 2020. doi:10.1001/jamaoncol.2020.5403
Author Contributions: Ms Hartman and Dr Schipper had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Schipper and Spratt contributed equally.
Concept and design: Hartman, Dess, Jackson, Morris, Li, Zaorsky, Schipper, Spratt.
Acquisition, analysis, or interpretation of data: Hartman, Sun, Devasia, Chase, Jairath, Dess, Jackson, Morris, Hochstedler, Abbott, Kidwell, Walter, M. Wang, X. Wang, Zaorsky, Schipper, Spratt.
Drafting of the manuscript: Hartman, Sun, Devasia, Hochstedler, Kidwell, Schipper, Spratt.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Hartman, Sun, Devasia, Chase, Jairath, Morris, Kidwell, M. Wang, X. Wang, Zaorsky, Schipper.
Obtained funding: Spratt.
Administrative, technical, or material support: Zaorsky, Spratt.
Supervision: Dess, Jackson, Kidwell, Schipper, Spratt.
Conflict of Interest Disclosures: Dr Zaorsky reported receiving grants from the American Cancer Society–CEOs Against Cancer Tri State chapter for clinician scientist development, the National Institutes of Health, the Penn State Cancer Institute, and the Penn State College of Medicine; remuneration from Springer Nature for a published textbook; and personal fees from Weatherby Healthcare outside the submitted work. Dr Schipper reported receiving personal fees from Innovative Analytics outside the submitted work. Dr Spratt reported receiving grants from Janssen Pharmaceuticals and personal fees from AstraZeneca, Blue Earth Diagnostics, and Janssen Pharmaceuticals outside the submitted work. No other disclosures were reported.
Funding/Support: This study was supported by grants P30-CA046592, P50-CA186786, T32-CA083654, and NSF DGE-1256260 from the National Cancer Institute, National Institutes of Health, and National Science Foundation.
Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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