People with kidney failure undergoing noncardiac surgery face an elevated risk of cardiovascular events and mortality. Existing risk prediction tools for perioperative events are either inaccurate in this population or include many variables that may complicate implementation. We developed and evaluated the performance of simplified machine-learning models for major cardiac events and mortality within 30 days after noncardiac surgery in patients with kidney failure in Alberta and Manitoba, Canada.