biostudies-literatureHu BShannan Municipal Science and Technology Bureau ProjectClinical Medical Research Transformation Project of Anhui ProvinceNational Natural Science Foundation of ChinaMedical Professional Delegation Aid Tibet Program of Natural Science Foundation of Tibet Autonomous RegionAnhui Department of Education Research ProgramAnhui Provincial Health and Wellness Research Project2https://www.ebi.ac.uk/biostudies/studies/S-EPMC12777130biostudies-literatureUnknown25(1)<h4>Background</h4>The atherogenic index of plasma (AIP) is known to be associated with atherosclerotic burden. However, the prognostic value of AIP in patients with severe coronary artery calcification (CAC) undergoing rotational atherectomy (RA) remains unclear. This study aimed to evaluate the relationship between AIP and adverse outcomes in this patient population and to explore relevant risk factors using explainable machine learning methods.<h4>Methods</h4>This study included patients with severe CAC who underwent RA between January 2017 and October 2024, with a median follow-up of 40.55 months. Patients were divided into three groups according to baseline AIP tertiles. The primary endpoints were cardiovascular death or all-cause death; secondary endpoints included non-fatal myocardiCardiovascular diabetologyThe atherogenic index of plasma predicts long-term outcomes in patients with severe coronary artery calcification undergoing rotational atherectomy: a machine learning-based cohort study.PMC12777130202204295107020021AHWJ2024Aa10105082200282SNSBJKJJHXM2023016XZ2023ZR-ZY50(Z)2022AH040193Wu JLi ZMa LRen ZHu HWang YSun HHu BfalseThe atherogenic index of plasma predicts long-term outcomes in patients with severe coronary artery calcification undergoing rotational atherectomy: a machine learning-based cohort study.<h4>Background</h4>The atherogenic index of plasma (AIP) is known to be associated with atherosclerotic burden. However, the prognostic value of AIP in patients with severe coronary artery calcification (CAC) undergoing rotational atherectomy (RA) remains unclear. This study aimed to evaluate the relationship between AIP and adverse outcomes in this patient population and to explore relevant risk factors using explainable machine learning methods.<h4>Methods</h4>This study included patients with severe CAC who underwent RA between January 2017 and October 2024, with a median follow-up of 40.55 months. Patients were divided into three groups according to baseline AIP tertiles. The primary endpoints were cardiovascular death or all-cause death; secondary endpoints included non-fatal myocardi2025-01-01T00:00:00Z2025 Dec2026-06-06T12:32:42.266Z2026-05-30T03:11:11.68ZS-EPMC127771304132734410.1186/s12933-025-03027-6