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Updated: Sep 16, 2025
Multimodality Diagnosis of Mesenteric Ischemia
Published on: July 21, 2023
Application of Machine Learning in the Prediction of the Acute Aortic Dissection Risk Complicated by Mesenteric
Zhechuan Jin1, Jiale Dong1, Jian Yang1
1Department of General Surgery, Beijing Anzhen Hospital, Capital Medical University, 100029 Beijing, China.
Background:
Mesenteric malperfusion (MMP) represents a severe complication of acute aortic dissection (AAD). Research on risk identification models for MMP is currently limited.
Methods:
Based on a retrospective study of medical records from the Beijing Anzhen Hospital spanning from January 2016 to June 2022, we included 435 patients with AAD and allocated their data to training and testing sets at a ratio of 7:3. Key preoperative predictive variables were identified through the least absolute shrinkage and selection operator (LASSO) regression. Subsequently, six machine learning algorithms were used to develop and validate an MMP risk identification model: logistic regression (LR), support vector classification (SVC), random forest (RF), extreme gradient boosting (XGBoost), naive Bayes (NB), and multilayer perceptron (MLP). To determine the optimal model, the performance of the model was evaluated using various metrics, including the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and the Brier score.
Results:
LASSO regression identified white blood cell count (WBC), neutrophil count (NE), lactate dehydrogenase (LDH), serum lactate levels, and arterial blood pH as key predictive variables. Among these, the WBC (OR 1.169, 95% confidence interval [CI] 1.086, 1.258; p < 0.001) and LDH levels (OR 1.001, 95% CI 1.000, 1.003; p = 0.008) were identified as independent risk factors for MMP. Among the six assessed machine learning algorithms, the RF model exhibited the best predictive capabilities, yielding AUROCs of 0.888 (95% CI 0.887, 0.889) and 0.797 (95% CI 0.794, 0.800) in the training and testing datasets, respectively, as well as sensitivities of 0.864 (95% CI 0.862, 0.867) and 0.811 (95% CI 0.806, 0.816), respectively, in the corresponding datasets.
Conclusions:
This study employed machine learning algorithms to develop a model capable of identifying MMP risk based on initial preoperative laboratory test results. This model can serve as a basis for making decisions in the treatment and diagnosis of MMP.
Insights
This study developed a machine learning model to predict mesenteric malperfusion (MMP) risk in acute aortic dissection (AAD) patients using preoperative lab results. The Random Forest model showed strong predictive performance, aiding clinical decision-making.
Area of Science:
- Cardiology
- Medical Informatics
- Surgical Complications
Background:
- Mesenteric malperfusion (MMP) is a severe complication of acute aortic dissection (AAD).
- Limited research exists on predictive models for MMP risk.
- Early identification of MMP is crucial for timely intervention and improved patient outcomes.
Purpose of the Study:
- To develop and validate a machine learning-based model for identifying patients at risk of MMP following AAD.
- To identify key preoperative laboratory variables predictive of MMP.
- To compare the performance of six different machine learning algorithms in predicting MMP.
Main Methods:
- Retrospective analysis of 435 AAD patients' medical records (January 2016 - June 2022).
- Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify predictive variables.
- Developed and validated MMP risk models using six machine learning algorithms: logistic regression, support vector classification, random forest, extreme gradient boosting, naive Bayes, and multilayer perceptron.
- Evaluated model performance using AUROC, accuracy, sensitivity, specificity, and Brier score.
Main Results:
- White blood cell count (WBC), neutrophil count (NE), lactate dehydrogenase (LDH), serum lactate, and arterial blood pH were identified as key predictive variables.
- WBC and LDH levels were independent risk factors for MMP.
- The Random Forest (RF) model demonstrated the best predictive performance, achieving an AUROC of 0.888 in the training set and 0.797 in the testing set.
Conclusions:
- Machine learning algorithms can effectively predict MMP risk in AAD patients using preoperative laboratory data.
- The developed RF model provides a valuable tool for early MMP risk assessment.
- This model can support clinical decision-making in the diagnosis and treatment of MMP.
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