The integrated “biomarker+nursing” pathway improves early warning and outcomes in AMI patients and develops a metabolic‑biomarker‑based predictive system for AKI risk identification.
Abstract
Current methods lack high‑risk identification for acute kidney injury (AKI) after acute myocardial infarction (AMI). This study aimed to develop a metabolic‑biomarker‑based predictive system. 124 AMI patients (July 2023–October 2024) were enrolled prospectively. Logistic regression, ROC curves, and Pearson correlation were used to assess predictive values. Post-PCI kidney injury incidence was 19.39% (n = 19). The injury group showed higher LVEF, FFA, and Killip ≥ 2 rates (P < 0.05), but lower 5-MTP and UMOD (P < 0.05). FFA, 5-MTP, and UMOD were independent risk factors (P < 0.05), with combined AUC = 0.931 (superior to single markers, P < 0.05). BUN, UA, SCr, and eGFR correlated strongly with these metabolites (P < 0.05). LVEF, 5‑MTP, and UMOD are key metabolic indicators for early AKI risk identification. The integrated “biomarker+nursing” pathway improves early warning and outcomes in AMI patients.
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