Development and validation of a predictive model for postoperative hepatic dysfunction in Stanford type A aortic...

Area of Science:

• Cardiovascular Surgery
• Hepatic Dysfunction Research
• Surgical Risk Factor Analysis

Background:

• Acute Stanford type A aortic dissection (ATAAD) is a critical condition requiring complex surgical intervention.
• Postoperative hepatic dysfunction (HD) is a significant complication following ATAAD surgery, impacting patient outcomes.
• Predictive models for HD in ATAAD patients are crucial for risk stratification and management.

Purpose of the Study:

• To identify independent risk factors for postoperative hepatic dysfunction (HD) in patients undergoing acute Stanford type A aortic dissection (ATAAD) surgery.
• To develop and validate an individualized prediction model (nomogram) for HD risk in ATAAD patients.
• To assess the clinical utility and accuracy of the developed nomogram for predicting HD.

Main Methods:

• Retrospective analysis of ATAAD patients from January 2020 to March 2024, divided into training and validation cohorts.
• Utilized Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression to identify predictive factors for HD.
• Developed a nomogram prediction model and assessed its accuracy, calibration, and clinical utility using C-statistics, calibration plots, and decision curve analysis (DCA).
• Internal validation performed using 1000 Bootstrap resamples to ensure model stability and minimize overfitting.

Main Results:

• Key independent risk factors for HD identified: chronic kidney disease, preoperative creatinine, international normalized ratio (INR), red blood cell (RBC) transfusion volume, peak intraoperative lactate, aortic cross-clamping time >99 min, and reoperation.
• The developed nomogram demonstrated good model fit (Hosmer-Leme show p=0.952) and strong discriminatory power (AUC: training 0.856, validation 0.958).
• Internal validation confirmed stable model performance (AUC: 0.860 pre-Bootstrap, 0.858 post-Bootstrap) with minimal overfitting.
• Decision curve analysis indicated that the nomogram provides a greater net clinical benefit for predicting HD risk.

Conclusions:

• The internally validated prognostic nomogram is an effective tool for predicting the risk of HD in patients undergoing ATAAD surgery.
• The model exhibits excellent discriminative power, calibration, and clinical utility.
• This individualized risk assessment allows for optimized clinical management and improved patient outcomes following ATAAD surgery.