Clinics (Sao Paulo, Brazil) · 2026
Predicting acute kidney injury in type B aortic dissection: development and validation of a clinical nomogram.
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Chen Weijian, Li Zhengfei, Shu Xiaojun
Аннотация
OBJECTIVES: Acute Kidney Injury (AKI) is a frequent and serious complication in patients with Type B Aortic Dissection (TBAD). Existing prediction models are mainly derived from type A or mixed cohorts and do not account for the distinct pathophysiology of TBAD. This study aimed to develop and validate a nomogram for individualized AKI risk prediction in TBAD patients.
METHODS: Data were obtained from the eICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care IV (MIMIC-IV). The eICU-CRD cohort was used for model development and internal validation, while MIMIC-IV served for external validation. A three-step variable selection strategy combining Least Absolute Shrinkage and Selection Operator (LASSO), univariate, and multivariate logistic regression analyses was applied. Model performance was evaluated using the Area Under the receiver operating characteristic Curve (AUC), calibration curves, and Decision Curve Analysis (DCA).
RESULTS: Six independent predictors were identified: ICU length of stay, minimum glucose, minimum sodium, maximum bicarbonate, minimum creatinine, and minimum diastolic blood pressure. The nomogram showed excellent discrimination, with AUCs of 0.926 (95% CI: 0.893-0.956), 0.922 (95% CI: 0.869-0.967), and 0.921 (95% CI: 0.895-0.944) in the training, internal validation, and external validation cohorts, respectively. Calibration analysis demonstrated good agreement, and DCA indicated clinical utility within a threshold probability range of 0-70%.
CONCLUSIONS: This study developed a practical nomogram based on six readily available variables for dynamic in-hospital prediction of AKI in TBAD patients. The model may assist early risk stratification, although further prospective validation is warranted.
METHODS: Data were obtained from the eICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care IV (MIMIC-IV). The eICU-CRD cohort was used for model development and internal validation, while MIMIC-IV served for external validation. A three-step variable selection strategy combining Least Absolute Shrinkage and Selection Operator (LASSO), univariate, and multivariate logistic regression analyses was applied. Model performance was evaluated using the Area Under the receiver operating characteristic Curve (AUC), calibration curves, and Decision Curve Analysis (DCA).
RESULTS: Six independent predictors were identified: ICU length of stay, minimum glucose, minimum sodium, maximum bicarbonate, minimum creatinine, and minimum diastolic blood pressure. The nomogram showed excellent discrimination, with AUCs of 0.926 (95% CI: 0.893-0.956), 0.922 (95% CI: 0.869-0.967), and 0.921 (95% CI: 0.895-0.944) in the training, internal validation, and external validation cohorts, respectively. Calibration analysis demonstrated good agreement, and DCA indicated clinical utility within a threshold probability range of 0-70%.
CONCLUSIONS: This study developed a practical nomogram based on six readily available variables for dynamic in-hospital prediction of AKI in TBAD patients. The model may assist early risk stratification, although further prospective validation is warranted.