Lipids in health and disease · 2025
Association between atherogenic index of plasma and sepsis in critically ill patients with ischemic stroke: a retrospective cohort study using propensity score and machine learning approaches.
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Yao Fengwei, Liu Lei, Zhang Jian, Chen Xiaolan, He Zhijun
Аннотация
BACKGROUND: Sepsis is a severe and frequent complication among ischemic stroke patients during hospitalization. The atherogenic index of plasma (AIP), as metabolism-related markers, are closely linked to inflammation. However, their relationship with ischemic stroke sepsis remains unclear.
METHODS: We examined the potential association between sepsis and elevated AIP Levels in 1,531 ischemic stroke intensive care unit patients from the MIMIC-IV database. Logistic regression, restricted cubic splines, and propensity score matching were used to assess associations. Machine learning models were additionally applied to evaluate model performance with and without AIP and to assess its incremental value.
RESULTS: Higher AIP levels were significantly associated with the development of sepsis. In the fully adjusted model, AIP (OR = 1.75, 95% CI: 1.12-2.71) were independently associated with an increased likelihood of sepsis. Analysis showed that patients in the high AIP group (Q3) exhibited the strongest association with sepsis (OR = 1.51, 95% CI: 1.09-2.09), and this association remained robust after propensity score matching (PSM) (OR = 1.35, 95% CI: 1.01-1.82). In machine learning models, AIP was retained as a contributing feature, and its inclusion improved model discrimination.
CONCLUSION: AIP were independently associated with sepsis risk and may complement risk stratification in clinical practice.
METHODS: We examined the potential association between sepsis and elevated AIP Levels in 1,531 ischemic stroke intensive care unit patients from the MIMIC-IV database. Logistic regression, restricted cubic splines, and propensity score matching were used to assess associations. Machine learning models were additionally applied to evaluate model performance with and without AIP and to assess its incremental value.
RESULTS: Higher AIP levels were significantly associated with the development of sepsis. In the fully adjusted model, AIP (OR = 1.75, 95% CI: 1.12-2.71) were independently associated with an increased likelihood of sepsis. Analysis showed that patients in the high AIP group (Q3) exhibited the strongest association with sepsis (OR = 1.51, 95% CI: 1.09-2.09), and this association remained robust after propensity score matching (PSM) (OR = 1.35, 95% CI: 1.01-1.82). In machine learning models, AIP was retained as a contributing feature, and its inclusion improved model discrimination.
CONCLUSION: AIP were independently associated with sepsis risk and may complement risk stratification in clinical practice.