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Background <p>This 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.</p> Methods <p>A 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.</p> Results <p>Seven 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.</p> Conclusion <p>The 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.</p>

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Development and validation of a nomogram for predicting acute kidney injury risks in patients undergoing acute stanford type A aortic dissection repair surgery

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月26日

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  • Wentao Li,
  • Weiguang Yu,
  • Ying Chen,
  • Wenyun Tan,
  • Fan Zhang,
  • Yingqi Zhang

摘要

Background

This 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.

Methods

A 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.

Results

Seven 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.

Conclusion

The 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.