Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data
Plain-language summary
This study used routine electronic health record data from more than 2,200 cancer patients treated with immune checkpoint inhibitors to build machine learning models that predict serious immune-related side effects (hepatitis, colitis, and pneumonitis) as well as one-year overall survival. The random forest models each drew on outcome-specific sets of features, such as laboratory measurements aggregated over time, and reached AUCs of roughly 0.73 to 0.76. The authors describe it as, to their knowledge, the first machine learning approach to assess an individual patient's checkpoint-inhibitor risk-benefit profile predominantly from routine structured EHR data, which could help inform treatment decisions and clinical trial selection.
Read the full paper (DOI: 10.1200/CCI.23.00207).
This is a plain-language summary written for discoverability; the authoritative version is the published paper. Part of Travis Osterman's peer-reviewed publications.