ArticleBMC nephrology2025
Development and validation of a nomogram for predicting acute kidney injury risks in patients undergoing acute stanford type A aortic dissection repair surgery.
Article in BMC nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
backgroundThis study aims to construct and internally validate a comprehensive nomogram designed for accurately predicting the incidence of acute kidney injury (AKI) in patients undergoing repair surgery for acute Stanford Type A aortic dissection (ATAAD), thereby enhancing postoperative risk management and patient care strategies.
methodsA retrospective analysis of 1471 consecutive patients diagnosed with ATAAD through computed tomography angiography (CTA) and confirmed by surgery at four tertiary medical centers from February 2010 to July 2023 was conducted. The study involved a comprehensive evaluation of 36 variables, categorizing patients into non-AKI and AKI groups. Advanced statistical techniques, including LASSO regression and Logistic regression, were employed. A sophisticated nomogram prediction model was developed using R language, and its efficacy was assessed using the concordance index (C-index), area under the receiver operating characteristic curve (AUC-ROC), and decision curve analysis.
resultsSeven key factors independently predicting AKI were identified, including heart failure (a condition where the heart can't pump blood as well), hyperlipidemia (high levels of fats in the blood), arterial dissection (a serious condition where there is a tear in the wall of a blood vessel), renal insufficiency, blood urea nitrogen (BUN), abnormal electrocardiogram (ECG), and total cholesterol (TC). The AUC-ROC, a measure of the model's ability to distinguish between classes, was 0.850 (95% CI: 0.823-0.877) for the training set, with high sensitivity (76%) and specificity (99%). For the validation set, the AUC-ROC was 0.840 (95% CI: 0.798-0.833), with sensitivity and specificity of 78% and 94%, respectively. The nomogram demonstrated a recalibrated C-index of 0.854 for the training set and 0.752 for the validation set. Decision curve analysis revealed the nomogram's significant net benefit across various clinical threshold probabilities.
conclusionThe AKI nomogram exhibits robust predictive capabilities, establishing itself as a crucial clinical tool for the early identification of patients at risk for AKI following ATAAD repair surgery. By delivering personalized risk assessments, this nomogram not only optimizes postoperative management strategies but also plays a vital role in enhancing patient outcomes through timely and proactive interventions.
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