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

• Main Authors: Saket Navlakha, Sejal Morjaria, Rocio Perez-Johnston, Allen Zhang, Ying Taur
• Format: Article
• Language: English
• Published: BMC 2021-05-01
• Series: BMC Infectious Diseases
• Subjects: Clinical machine learning; COVID-19; Infectious diseases; Cancer; Predictive modeling
• Online Access: https://doi.org/10.1186/s12879-021-06038-2

Abstract 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 348 cancer patients at Memorial Sloan Kettering Cancer Center in New York City. Using only clinical variables 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 area under the receiver operating characteristic curve (AUROC) ranging from 70 to 85%, significantly outperforming prior methods and univariate analyses. Critically, classification accuracy is highest when using a potpourri of clinical variables — including basic patient information, 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.