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EP217/#745  COVID-19 effects on ovarian cancer diagnosis, treatment patterns, and survival
  1. Mihae Song1 and
  2. Rebecca Nelson2
  1. 1City of Hope, Gynecologic Oncology, Duarte, USA
  2. 2City of Hope, Computational and Quantitative Medicine, Duarte, USA


Introduction The impact COVID-19 had on cancer rates and outcomes will take years to fully assess. International studies have shown that non-emergency cancer surgeries were postponed in favor of neoadjuvant treatment. The impact the pandemic had on ovarian cancer patients has not yet been systematically examined in a US population-based cohort.

Methods Stage III and IV ovarian cancer patients were ascertained using the National Cancer Database (NCDB). Patients were stratified by timeperiod (2017–2018 v 2020) to assess whether treatment patterns differed across time periods.

Results 26,409 ovarian cancer patients were included, 18,585 diagnosed prior to 2019 and 7,824 in 2020. No differences were found in age at diagnosis, race, or ethnicity across timeperiods. On multivariable logistic regression, patients diagnosed during the COVID timeperiod were more likely to be on Medicare than private insurance (OR=1.19;CI=1.07–1.32) and were more likely to have stage IV disease than stage III disease (OR=1.11;CI=1.05–1.17). Multivariable results also showed that, compared to adjuvant treatment, neoadjuvant chemotherapy (OR=1.13;CI=1.03–1.25) or chemotherapy alone (OR=1.17;CI=1.08–1.27) were more often given during COVID than pre-COVID.

Conclusion/Implications While the emergency threat posed by COVID-19 appears to have subsided, the experiences gained during the COVID-19 pandemic can inform future decisions in times of crises or resource shortages. Our findings show that patients on Medicare were diagnosed with ovarian cancer during the COVID era more often than patients on private insurance and that chemotherapy was used as the first treatment line more often than surgical resection. These results are consistent with international studies.

Abstract EP217/#745 Figure 1

Multivariate logistic regression examining odds ratio for COVID-era factors

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