March 10, 2022
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Machine learning model finds COVID-19 risks for cancer patients

Jeremy Thomas/LLNL

A new study by researchers at LLNL and the University of California, San Francisco, looks to identify cancer-related risks for poor outcomes from COVID-19. Analyzing one of the largest databases of patients with cancer and COVID-19, the team found previously unreported links between a rare type of cancer—as well as two cancer treatment-related drugs—and an increased risk of hospitalization from COVID-19. The findings appear in the journal Cancer Medicine. Using a logistical regression approach, the team examined de-identified Electronic Health Record data from the UC Health COVID Research Data Set on nearly a half-million patients who underwent COVID-19 testing at all 17 UC-affiliated hospitals. The dataset included nearly 50,000 patients with cancer—more than 17,000 of whom also had tested positive for COVID—and contained information on patient demographics, comorbidities, lab work, cancer types, and various cancer therapies. Read more at LLNL News.