OncCOVID model predicts impact of delaying cancer treatment during pandemic
medwireNews: US researchers have developed an integrated, web-based survival model – OncCOVID – that may help clinicians to assess the impact of delayed cancer treatment during the COVID-19 global pandemic.
Matthew Schipper (University of Michigan, Ann Arbor) and co-authors say: “During the pandemic, treatment delay is being recommended in a nonquantitative, nonobjective, and nonpersonalized manner, and this approach may be associated with suboptimal outcomes.”
They explain that the OncCOVID model “aims to improve current recommendations and provides quantitative estimates to optimize the outcomes of patients with cancer during the global pandemic.”
The model incorporates pre-COVID-19 cancer stage-specific survival estimates and the consequences of treatment delay with data on local community estimates of COVID-19 risk and COVID-19 mortality and calculates the estimated cumulative overall survival and restricted mean survival time of patients who receive immediate versus delayed cancer treatment.
The version tested in the current study has 47 possible input fields that include variables such as patient age, comorbidity, cancer type and stage, treatment type and duration, infection risk, and data on the healthcare system being used.
As reported in JAMA Oncology, Schipper and team tested the model using data for 5,436,896 individuals in the National Cancer Database.
They found that the impact of delayed cancer treatment “varied substantially across and within cancer types and stages,” with individualized overall survival estimates “associated with patient age, number of comorbidities, treatment received, and specific local community estimates of COVID-19 risk.”
For example, the impact among patients with prostate cancer, who typically only spend 1–2 days in hospital, was “minimal”, the authors say. By contrast, for patients with pancreatic cancer, the increased risk for cancer-specific mortality as a result of treatment delay “exceeded any decrease in COVID-19–specific mortality.”
Schipper et al conclude 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.”
In an accompanying editorial, Elizabeth Garrett-Mayer (American Society of Clinical Oncology, Alexandria, Virginia, USA) and Brian Rini (Vanderbilt-Ingram Cancer Center, Nashville, Tennessee, USA) say: “The authors are to be congratulated for developing a more personalized approach to decision-making that is based on data rather than consensus opinion.”
They add, however, that the OncCOVID model should “be used with some degree of caution,” due to uncertainties surrounding risk estimates and future dynamics of the pandemic. They therefore suggest that “the model’s risk parameters should be updated as new information emerges to reduce uncertainty and increase the accuracy of the model’s estimates.”
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