Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: A machine learning approach
Many oncology treatments have been associated with cardiovascular (CV) adverse events. Cases of CV events, including myocarditis have been reported for PD-1 and PD-L1 therapies. We created a machine learning model to predict potential CV events in PD-(L)1 patients using the CancerLinQ database. The study concluded that, using traditional cardiac risk factors, our model was able to predict potential cardiac events in PD-(L)1 patients. Our model found that high lymphocyte count may be protective while weight loss and a history of cardiac disease (e.g. heart failure) could indicate a poor prognosis.