biostudies-literatureUnknown29(2)Zhang XEffective risk stratification is crucial for managing acute coronary syndrome (ACS). This study evaluated whether general-purpose large language models (LLMs) can reliably execute the complex clinical reasoning required for cardiovascular prognosis. We quantitatively assessed three LLMs-ChatGPT 4o, DeepSeek R1, and Grok 3-for predicting one-year major adverse cardiovascular events (MACEs), using 29 guideline-recommended features from 903 participants in the LM-ACS cohort and 64 participants in the MIMIC database. All models demonstrated significant risk overestimation and substantial output variability across ten independent runs. Although Grok 3 showed the highest initial accuracy, its average performance over all runs showed no significant difference. ROC analysis indicated that none of iScience114644https://www.ebi.ac.uk/biostudies/studies/S-EPMC12874424biostudies-literatureLarge language models for predicting one-year major adverse cardiovascular events in acute coronary syndrome.PMC12874424Zheng YLi JYang KShang LLai FZhang XXu THu KHou SHuang RTian PSu GfalseLarge language models for predicting one-year major adverse cardiovascular events in acute coronary syndrome.Effective risk stratification is crucial for managing acute coronary syndrome (ACS). This study evaluated whether general-purpose large language models (LLMs) can reliably execute the complex clinical reasoning required for cardiovascular prognosis. We quantitatively assessed three LLMs-ChatGPT 4o, DeepSeek R1, and Grok 3-for predicting one-year major adverse cardiovascular events (MACEs), using 29 guideline-recommended features from 903 participants in the LM-ACS cohort and 64 participants in the MIMIC database. All models demonstrated significant risk overestimation and substantial output variability across ten independent runs. Although Grok 3 showed the highest initial accuracy, its average performance over all runs showed no significant difference. ROC analysis indicated that none of 2026-01-01T00:00:00Z2026 Feb2026-07-15T13:24:42.845Z2026-07-05T03:08:23.549ZS-EPMC128744244166025010.1016/j.isci.2026.114644