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Purpose <p>This study aims to identify risk factors for postoperative complications in patients with acute Stanford A type aortic dissection and construct a predictive model for postoperative mortality risk. The ultimate goal is to more accurately assess patient prognosis and optimize treatment strategies.</p> Methods <p>A retrospective analysis was conducted on the medical records of 182 patients who underwent acute Stanford A type aortic dissection surgery at the Department of Critical Care Medicine, the First Affiliated Hospital of Zhengzhou University, from January 2020 to June 2023. Patients were categorized into survival and death groups based on their 30-day postoperative survival status. Logistic regression analysis was performed on biochemical indicators with statistical significance. The R language's RStudio tool was used to construct the model, dividing the dataset into training and validation sets in a 7:3 ratio to assess the model's effectiveness. External validation of the model was carried out using data from 131 patients who met the inclusion criteria and were treated at our hospital from July 2023 to July 2024.</p> Results <p>(1) This study enrolled 182 patients with Stanford type A aortic dissection, with a 30-day survival rate of 52.75% and mortality rate of 47.25%. (2) Logistic regression analysis revealed that postoperative lac, urea, CREA, and hs-cTnI were significantly associated with elevated odds ratios of 1.20774146, 1.04752061, 1.00076529, and 1.05987368, respectively. (3) A nomogram was established to predict postoperative death, with an AUC of 0.8242. When the optimal cut-off value was 0.354, sensitivity was 74.6% and specificity was 83.7%. Bootstrap validation, K-tenfold Cross validation and Jackknife validation had C-indexes of 0.823999996, 0.814180543 and 0.8025354 respectively. The model would benefit when the threshold was &gt; 0.08. (4) The relative influence of independent variables was Lac(35) &gt; CREA (25) &gt; hs-cTnI(23) &gt; Urea(17).</p> Conclusions <p>This study establishes a nomogram to quantify the risk indicators in patients post-surgery for acute Stanford type A aortic dissection. This tool aids in predicting in-hospital mortality and allows for timely interventions to improve patient outcomes.</p>

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Analysis of Postoperative Mortality Risk in Acute Stanford Type a Aortic Dissection and Development of a Clinical Prediction Model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月1日

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  • Yunwei Zhang,
  • Yuming Du

摘要

Purpose

This study aims to identify risk factors for postoperative complications in patients with acute Stanford A type aortic dissection and construct a predictive model for postoperative mortality risk. The ultimate goal is to more accurately assess patient prognosis and optimize treatment strategies.

Methods

A retrospective analysis was conducted on the medical records of 182 patients who underwent acute Stanford A type aortic dissection surgery at the Department of Critical Care Medicine, the First Affiliated Hospital of Zhengzhou University, from January 2020 to June 2023. Patients were categorized into survival and death groups based on their 30-day postoperative survival status. Logistic regression analysis was performed on biochemical indicators with statistical significance. The R language's RStudio tool was used to construct the model, dividing the dataset into training and validation sets in a 7:3 ratio to assess the model's effectiveness. External validation of the model was carried out using data from 131 patients who met the inclusion criteria and were treated at our hospital from July 2023 to July 2024.

Results

(1) This study enrolled 182 patients with Stanford type A aortic dissection, with a 30-day survival rate of 52.75% and mortality rate of 47.25%. (2) Logistic regression analysis revealed that postoperative lac, urea, CREA, and hs-cTnI were significantly associated with elevated odds ratios of 1.20774146, 1.04752061, 1.00076529, and 1.05987368, respectively. (3) A nomogram was established to predict postoperative death, with an AUC of 0.8242. When the optimal cut-off value was 0.354, sensitivity was 74.6% and specificity was 83.7%. Bootstrap validation, K-tenfold Cross validation and Jackknife validation had C-indexes of 0.823999996, 0.814180543 and 0.8025354 respectively. The model would benefit when the threshold was > 0.08. (4) The relative influence of independent variables was Lac(35) > CREA (25) > hs-cTnI(23) > Urea(17).

Conclusions

This study establishes a nomogram to quantify the risk indicators in patients post-surgery for acute Stanford type A aortic dissection. This tool aids in predicting in-hospital mortality and allows for timely interventions to improve patient outcomes.