ArticleClinics (Sao Paulo, Brazil)2026
Association between ePWV and delirium risk in icu sepsis patients: a retrospective analysis based on the MIMIC database and development of a predictive model.
Article in Clinics (Sao Paulo, Brazil), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundSepsis, frequently complicated by delirium with poor prognosis, is common in the Intensive Care Unit (ICU). Estimated Pulse Wave Velocity (ePWV), a non-invasive arterial stiffness marker, remains unexplored regarding delirium risk in ICU sepsis patients. This study investigated ePWV's association with sepsis-linked delirium and developed a predictive model.
methodsThis retrospective cohort study utilized MIMIC-IV 3.1 data. ICU patients with sepsis meeting the Sepsis-3.0 criteria were randomly allocated in a 7:3 to the training and validation cohorts.The exposure was ePWV derived using age and blood pressure. The outcome was delirium, defined by the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Features were selected via Boruta and LASSO.Associations were examined using Cox models, Kaplan-Meier curves, restricted cubic splines, and subgroup analyses. Five machine learning models were evaluated.
resultsAmong 24,889 patients (delirium incidence 44.4%), Kaplan-Meier analysis showed significant differences in delirium risk across ePWV levels (p < 0.0001). Cox regression indicated ePWV was positively associated with delirium (HR = 1.018, 95% CI: 1.008‒1.027), with the highest quartile showing 19% elevated risk versus the lowest (HR = 1.193). RCS analysis revealed a nonlinear relationship (p = 0.019). Subgroup analyses were significant except for cerebral infarction. K-Nearest Neighbor performed best with AUC = 0.736 in validation. SHAP analysis demonstrated respiratory failure, ARFand ACEI as key features.
conclusionePWV was independently associated with delirium risk in ICU sepsis patients. The machine learning model demonstrated good discriminative ability, facilitating early identification of high-risk patients.
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