Relationship between atherogenic index of plasma and length of stay in critically ill patients with atherosclerotic cardiovascular disease: a retrospective cohort study and predictive modeling based on machine learning

作者：Yu Guo, Fuxu Wang, Shiyin Ma, Zhi Mao, Shuangmei Zhao, Liutao Sui, Chucheng Jiao, R. Lu, Xiaoyan Zhu, Xudong Pan · 发表于：Cardiovascular Diabetology · 年份：2025 · DOI：10.1186/s12933-025-02654-3 · 被引用次数：28 · 研究领域：Sepsis Diagnosis and Treatment、Hemodynamic Monitoring and Therapy、Inflammatory Biomarkers in Disease Prognosis

BACKGROUND: The atherogenic index of plasma (AIP) is considered an important marker of atherosclerosis and cardiovascular risk. However, its potential role in predicting length of stay (LOS), especially in patients with atherosclerotic cardiovascular disease (ASCVD), remains to be explored. We investigated the effect of AIP on hospital LOS in critically ill ASCVD patients and explored the risk factors affecting LOS in conjunction with machine learning. METHODS: Using data from the Medical Information Mart for Intensive Care (MIMIC)-IV. AIP was calculated as the logarithmic ratio of TG to HDL-C, and patients were stratified into four groups based on AIP values. We investigated the association between AIP and two key clinical outcomes: ICU LOS and total hospital LOS. Multivariate logistic regression models were used to evaluate these associations, while restricted cubic spline (RCS) regressions assessed potential nonlinear relationships. Additionally, machine learning (ML) techniques, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGB), were applied, with the Shapley additive explanation (SHAP) method used to determine feature importance. RESULTS: The study enrolled a total of 2423 patients with critically ill ASCVD, predominantly male (54.91%), and revealed that higher AIP values were independently associated with longer ICU and hospital stays. Specifically, for each unit increas...