Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning

Navlakha, S.; Morjaria, S.; Perez-Johnston, R.; Zhang, A.; Taur, Y.

Background: Accurately predicting outcomes for cancer patients with COVID-19 has been clinically challenging. Numerous clinical variables have been retrospectively associated with disease severity, but the predictive value of these variables, and how multiple variables interact to increase risk, remains unclear. Methods: We used machine learning algorithms to predict COVID-19 severity in 354 cancer patients at Memorial Sloan Kettering Cancer Center in New York City. Using clinical variables only collected on or before a patient's COVID-19 positive date (time zero), we sought to classify patients into one of three possible future outcomes: Severe-early (the patient required high levels of oxygen support within 3 days of being tested positive for COVID-19), Severe-late (the patient required high levels of oxygen after 3 days), and Non-severe (the patient never required oxygen support). Results: Our algorithm classified patients into these classes with an AUROC ranging from 70-85%, significantly outperforming prior methods and univariate analyses. Critically, classification accuracy is highest when using a potpourri of clinical variables -- including patient demographics, pre-existing diagnoses, laboratory and radiological work, and underlying cancer type -- suggesting that COVID-19 in cancer patients comes with numerous, combinatorial risk factors. Conclusions: Overall, we provide a computational tool that can identify high-risk patients early in their disease progression, which could aid in clinical decision-making and selecting treatment options.