The global burden of the ongoing COVID-19 pandemic has predominantly been measured using metrics like case numbers, hospitalizations and deaths. However, the short-term health impacts are more difficult to capture, especially for breast cancer(BC) patients. An expert opinion formulated by multiple national organizations provided preliminary guidance on the prioritization and treatment of breast cancer(BC) during this covid outbreak. Most breast surgery and adjuvant treatment for malignant conditions have been postponed or adjusted. As we know, BC treatment is time-dependent. How standard treatment modifications and surgery postponement would affect BC patients are unknown. Currently, there is no research that has applied an informatics approach to study short-term and possible long-term impacts to BC patients. Our hypothesis is: The pandemic has caused changes in neoadjuvant and adjuvant therapy for newly diagnosed primary breast cancer patients. Pandemic associated delays to surgery and differences in treatment may be associated with unexpected new comorbidities and incidence of extreme advanced disease such as infection resulting from neutropenia, increased DVT, bone loss, stroke, etc. Exploratory data mining for subgroup cohorts would be used to identify breast cancer treatment related adverse outcome oriented subgroup patterns in Cerner’s Real-World dataset.
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