Decisions regarding the initiation of cancer therapy have become increasingly complex in the era of coronavirus disease COVID Health care professionals and patients must balance the benefits and risks of immediate treatment for cancer with the potential subsequent increases in the risk of COVID and its associated complications, including Potenial. Unlike approaches that triage cancer treatment using a tiered approach, Hartman et al 1 have developed a web-based comprehensive model OncCOVID to estimate the risk of delaying the initiation of cancer treatment for an individual patient with cancer.
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This model estimates risk for patients with cancer of varying types and stages, and it incorporates other known risk factors for COVID, such as comorbidities, patient age, local COVID prevalence, and reproduction number which reflects the mean number of new patients with infections by each case in a region. The model is based on the use of epidemiologic data regarding treatment delays and outcomes from patients with cancer to estimate the risk of delayed treatment for a specific patient based on individual risk profile. The model has been developed into a useful application with a simple and intuitive interface. The web-based interface provides an accessible platform that can be used on a smartphone. Many of the necessary inputs for The Potential Benefits Of Adjuvant Therapy model have default values that can be modified for quantities such as relative risk [RR] of infection and other parameters that may vary across cancer care practice or treatment settingbut the user is required to input data on patient-level characteristics.
The utility of this model is clear in several specific settings. First, when the pandemic overwhelmed health care settings in certain areas eg, New York City and Detroithealth care professionals did not have the capacity to provide treatment for all patients with cancer, and decisions for care rationing had to be made. This scenario may arise again in autumn with a potential seasonal increase in cases.
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The OncCOVID model will be useful for cancer care facilities in high-prevalence areas that are required to triage patients because of limited resources. However, at the current time late summerthe model is unlikely to be used widely given the current infection rates and hospital capacities at levels that appear to allow most cancer care practices to maintain treatment recommendations without delays. Second, because OncCOVID provides interpretable output regarding risks eg, the number of weeks or months of life lost owing to a treatment delay of a specific durationthe The Potential Benefits Of Adjuvant Therapy from OncCOVID can be used to facilitate treatment timing discussions with patients.
The model estimates may help a reluctant patient understand the increased risks of delaying treatment and, vice versa, the estimates may help another patient understand that the risks of cancer treatment delay are minimal, and the patient can safely delay treatment until infection levels in their region have decreased. In other words, the OncCOVID results can provide recommendations to support a clinically appropriate rationale for treatment timing.
Such a model could apply to other medical fields. Choosing to receive treatment now vs later is not a substantially different benefit or risk decision than is choosing between Beneits potential treatment strategies or between less intense adjuvant treatment immediately vs more intense adjuvant treatment at relapse. In these cases, using data-based inferences to guide clinical recommendations can augment clinical judgment and personal experience.
Based on current US health care settings and strategies for mitigating contagion, these levels are likely overestimates.
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However, it is admittedly a challenge to find more reliable or current estimates, although emerging data suggest that the risks of infection are not higher among cancer patients receiving treatment. Hartman et al 1 have considered the risks of several discrete categories of treatment, but the complexities of cancer https://soundofliberation.com/essay/unto-this-last/how-psychiatry-has-changed-since-the-1970.php make Potenhial almost impossible to tailor these assumptions to all cancer patients.
Although a welcome feature of the model, most users will not have valid estimates available and will instead rely on the model defaults. Users are encouraged to repeat the model estimation by entering different values for some of click the following article modifiable parameters to assess the sensitivity of the model estimates to The Potential Benefits Of Adjuvant Therapy assumptions. Encouraging hTerapy to consider multiple outputs to compare results will better characterize the uncertainty of the model Thrrapy based on different assumptions. Another realistic scenario in the pandemic setting is to stop treatment after the patient has received a The Potential Benefits Of Adjuvant Therapy treatment schedule eg, pause treatment after 2 cycles of chemotherapy instead of completing a planned course of 4 cycles.
However, the current OncCOVID model is based on delaying the start of treatment; the data entered in the model are based on days between diagnosis and the initiation of treatment, and they do not address delays in the midst of treatment. Other sources of uncertainty are future infection levels and reproduction numbers. Given the potential for a seasonal surge in late ; the uncertainty of local, state, and federal guidelines and recommendations; and variable human behavior and adherence to behaviors that limit disease spread, it is challenging to estimate, for any given locale in the US, whether the case numbers will be lower. If the Afjuvant rates are higher in a few months, delaying treatment may expose the patient to an increased risk of COVID in later months in addition to the potential disease-based risks of delay. As described, OncCOVID also provides information to support clinical decision-making and facilitate discussions of the risks and benefits with patients.
The authors are to be congratulated for developing a more personalized approach to decision-making that is based on data rather than consensus opinion. Published Online: October 29, No other disclosures were reported.
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