Prediction of mortality, bleeding, and ischaemic events in patients with cancer and acute coronary syndrome: a model development and validation study

Accurate assessment of mortality, bleeding, and atherothrombotic risk in patients with cancer and acute coronary syndrome could inform novel personalised treatment strategies, but no standardised tools for this purpose exist. We aimed to develop and validate a clinically applicable risk score for mortality, bleeding, and ischaemic events in patients with cancer and acute coronary syndrome.

In this model development and validation study, we obtained data for 1 017 759 patients who presented with acute coronary syndrome in England, UK (n=815 170; 36 771 with cancer), Sweden (n=194 059; 10 262 with cancer), and Switzerland (n=8530; 203 with cancer) between Jan 1, 2004, and Aug 8, 2023. Machine learning models were developed to predict all-cause mortality, major bleeding events, and ischaemic events, defined as a composite of cardiovascular death, myocardial infarction, and ischaemic stroke, in patients with cancer and acute coronary syndrome from England in a competing risks framework with a prediction horizon of 6 months. Final models (the ONCO-ACS score) were externally validated in geographically distinct held out datasets from the English Midlands, Sweden, and Switzerland.