Background: Acute kidney injury (AKI) is prevalent among hospitalized patients and is frequently complicated by hyperkalemia (HyperK), kidney replacement therapy (KRT), and major adverse kidney events (MAKE). Early prediction of these outcomes remains a clinical priority.
Objective: To develop and internally validate nomograms using routinely collected clinical variables to predict the risk of HyperK, KRT, and MAKE, including death and ≥25 mL/min/1.73 m² reduction in eGFR in hospitalized AKI patients.
Methods: This retrospective cohort study included 753 adult AKI patients without initial HyperK, evaluated at a tertiary referral center from 2020 to 2024. Logistic regression models identified predictors of HyperK and MAKE, stratified by sex. Model performance was assessed via AUC, calibration, and predictive metrics. Nomograms were constructed based on final multivariate models.
Results: During follow-up, 24% of patients developed HyperK. Independent predictors included vasopressor use, shock, urinary obstruction, low hemoglobin, and higher baseline potassium. The HyperK model demonstrated moderate discrimination (AUC 0.68) but a high negative predictive value (97%). Sex-stratified nomograms for MAKE, KRT, and mortality showed strong performance (AUCs 0.74–0.98), with highest accuracy observed in KRT models for both sexes (AUC 0.96). Predictors varied by sex but commonly included volume overload, acid–base disorders, uremia, and elevated creatinine.
Conclusion: We developed pragmatic and accessible nomograms capable of predicting HyperK, KRT, and MAKE in AKI patients using standard clinical data. These tools offer timely, personalized risk stratification and may support clinical decision-making in diverse hospital settings.
