Acute aortic dissection (AAD) is a rapid onset, highly lethal cardiovascular emergency. Despite recent advancements in diagnostic and therapeutic techniques that have resulted in a reduction in hospital mortality, the overall prognosis remains poor [1]. Mesenteric malperfusion (MMP), a rare complication of AAD, is associated with the extension of the aortic dissection into the mesenteric arterial circulation. This can precipitate intestinal ischemia, necrosis, and a systemic inflammatory response, ultimately leading to multi-organ failure [2]. Previous studies have demonstrated that AAD patients with concomitant MMP have a significantly increased hospital mortality (13%–95%) [3, 4]. Given the subtle and non-specific early symptoms of MMP, delayed diagnosis frequently leads to irreversible intestinal necrosis, an independent risk factor that adversely impacts prognosis [5].

The clinical diagnosis of MMP currently relies on clinical symptoms, biochemical monitoring, and radiological studies [6]. However, most indicators undergo significant changes only after intestinal ischemia has progressed to an advanced stage, and the delay in diagnosis may result in missing the optimal window for intervention. Furthermore, the sensitivity and specificity of existing laboratory markers are limited [7], and the predictive capability of individual markers is insufficient to facilitate rapid risk stratification in emergency settings.

Machine learning has exhibited distinct benefits in cardiovascular medicine by integrating multidimensional data and analyzing nonlinear relationships [8]. However, its application in risk prediction for AAD combined with MMP is still in the exploratory stage.

This study combined patient demographic information with preoperative initial laboratory tests and used machine learning algorithms to create a risk identification model. The model is designed to address the constraints of traditional univariate analysis and achieve precise, individualized identification of the risk of MMP in patients with AAD. We compared the predictive capabilities of various algorithms to further elucidate the potential relationships between laboratory markers and MMP. The establishment of this model will facilitate the precise identification of patients at elevated risk for MMP, refine therapeutic approaches, and ultimately improve the outcomes of patients with AAD.

This study sought to enhance the ability to identify the risk of AAD complicated by MMP by developing and validating a machine learning-based prediction model. A total of 435 AAD patients were included in the study. Five preoperative laboratory test indicators, which were the initial results obtained after admission, were selected through Lasso regression. Various machine learning models were developed, with the RF model ultimately demonstrating the best performance in predicting the risk of MMP. Multivariate logistic regression analysis identified WBC (OR 1.169, 95% CI 1.086, 1.258, p 0.001) and LDH (OR 1.001, 95% CI 1.000, 1.003, p = 0.008) as independent risk factors for concurrent MMP, while pH, NE, and lactate were determined to be non-independent predicting factors.

MMP is caused by dynamic obstruction resulting from compression of the true lumen by the false lumen of the dissection, or by static obstruction when the dissection extends into the mesenteric arteries. It is a condition induced by aortic dissection leading to acute mesenteric ischemia (AMI) [14]. The treatment of MMP presents several challenges. After repair of the aortic dissection, dynamic obstruction may improve; however, for static obstruction, restoring perfusion remains difficult. As a result, there is ongoing debate over whether to prioritize addressing the dissection or the perfusion deficit. Mortality rates vary widely depending on the dissection type and treatment strategy, ranging from 10% to 60% [3, 15].

Approximately 60% of patients with MMP present with gastrointestinal symptoms such as abdominal pain, bloating, gastrointestinal bleeding, and peritoneal irritation [4], consistent with the findings in this study (Table 1). However, these typical symptoms often appear late, usually indicating irreversible ischemic damage. Furthermore, MMP is more frequently associated with perfusion deficits in other organs, such as the lower limbs and kidneys [16, 17]. In this study, 58.62% of MMP patients had concurrent perfusion deficits in other organs.

Radiological examinations are crucial in the diagnosis of MMP [18]. Intestinal dilation, intestinal wall thickening or thinning, and reduced enhancement are signs of intestinal ischemia on gastrointestinal imaging studies [19]. However, due to the non-specific nature of these manifestations and their late onset, the sensitivity of early radiological imaging findings is only about 40% [20]. Lu et al. [9], identified that a decreased ratio of the true lumen to false lumen in the morphology of type B aortic dissection is an independent predictor of the risk for MMP. Radiological imaging is limited in providing dynamic assessments of the disease, making it challenging to detect MMP progression during dissection or to diagnose MMP caused by dynamic obstruction. Jonker et al. [21], reported that 20% of patients did not show abdominal vessel involvement on CTA.

Laboratory tests serve as a rapid assessment tool that is crucial for the diagnosis and real-time evaluation of MMP. In our study, we identified five key predictors associated with MMP risk, including elevated levels of LDH, NE, WBC, and lactate, as well as decreased pH values, all of which were correlated with an increased risk of MMP (Fig. 6). These findings are consistent with those reported in previous studies. It has been reported that intestinal mucosa is susceptible to damage under ischemic conditions, leading to disturbances in capillary blood flow and exacerbating local inflammatory responses, which in turn elevate inflammation [22]. Emile [23] found that an elevated WBC was closely related to the occurrence of intestinal necrosis (OR = 1.3, p 0.0001), suggesting its potential as one of the predictive factors. Additionally, due to impaired tissue perfusion, metabolic acidosis may accompany intestinal ischemia and trigger abnormalities in biomarkers that reflect the degree of tissue perfusion. Khan et al. [7] reported that LDH and serum lactate levels were significantly elevated in patients with intestinal ischemia, with diagnostic AUROCs of 0.46–0.89 for LDH and 0.5–0.7 for serum lactate. Moreover, pH is also utilized to assess the severity of acidosis. Feier et al. [24] found that a preoperative pH $\le$7.25 serves as an independent risk factor for the prognosis of patients with type A AAD complicated by organ malperfusion.

It is important to note that these indicators are usually elevated in all AAD patients, making it difficult to make the diagnosis of MMP. Blaser et al. [25], in a meta-analysis, reported that there is currently no single biomarker capable of accurately diagnosing mesenteric ischemia. Emerging biomarkers, such as intestinal fatty acid binding protein (I-FABP) and $\alpha$-glutathione S-transferase ($\alpha$-GST), are considered promising candidates [26], but their diagnostic value requires large-scale validation. Therefore, a comprehensive analysis and utilization of existing laboratory indicators are necessary for the diagnosis of MMP.

Machine learning has the ability to capture both linear and non-linear relationships within data, enabling better analysis of the intrinsic connections between target events and features. In recent years, machine learning-related research has gained popularity in the field of aortic dissection, covering various areas such as diagnosis, complications, and prognosis. Huo et al. [27] developed a diagnostic model for aortic dissection using 13 features and the NB model, achieving an AUROC of 0.81. Hata et al. [28] and Yi et al. [29] developed aortic dissection diagnostic models based on non-contrast-enhanced CT using convolutional neural networks, achieving AUROCs of 0.940 and 0.969, respectively. Dai et al. [30] utilized the XGBoost model to accurately predict acute kidney injury following aortic dissection repair, while Guo et al. [31] developed an in-hospital mortality prediction model for AAD using XGBoost.

However, there is a limited number of machine learning studies specifically focusing on MMP in patients with AAD. The majority of research has concentrated solely on AMI. Zhuang et al. [32] developed a logistic regression model utilizing variables such as WBC, BUN, neutrophil ratio, prothrombin time, and D-dimer, achieving an AUROC of 0.889 for predicting intestinal necrosis in AMI patients. While the variables included in this study are similar to ours, the performance of the model in the validation set was not reported, necessitating further validation. Additionally, Song et al. [33] developed a deep learning model that integrates CTA imaging features, albumin, and international normalized ratio, effectively identifying AMI patients from those suspected of an AMI with an AUROC of 0.96. These results suggest that deep learning methods may have a role in the detection and prognosis of MMP in AAD patients. Groesdonk et al. [34] created a diagnostic model for nonocclusive mesenteric ischemia based on clinical data from 865 patients undergoing elective cardiac surgery with extracorporeal circulation, using seven variables including postoperative serum lactate level, and achieved an accuracy rate of 93.9%. This study also focused on MMP as a cardiovascular complication, but in comparison, our study is more dedicated to the early and rapid identification of preoperative MMP risk in patients with AAD.

In this study, we constructed a total of six machine learning models to assess the risk of MMP. Among them, the RF model exhibited the best performance in the test set, achieving an AUROC of 0.797 (95% CI 0.794, 0.800), a sensitivity of 0.811 (95% CI 0.806, 0.816), and a specificity of 0.722 (95% CI 0.719, 0.725). In contrast, the XGBoost model demonstrated the highest AUROC in the training set, but there was a significant difference in performance between the training and test sets, suggesting the possibility of overfitting. To mitigate the risk of overfitting, we adopted measures such as class balancing, the introduction of regularization, and the restriction of model complexity. The results from the RF, NB, MLP, and LR models indicate that these measures were effective. However, excessive use of anti-overfitting measures may lead to the risk of underfitting, thus, further expansion of the sample size could be a highly effective method to enhance model performance. Additionally, the findings of our research reveal that RF outperforms XGBoost in overall performance, suggesting that RF may have advantages in small datasets and noisy data environments, which is consistent with the results of previous studies [35, 36, 37].

The primary objective of this study was to assess the risk of MMP based on preoperative laboratory test results obtained shortly after patient presented with an AAD. The results demonstrated that the RF model outperformed other machine learning models, achieving the highest AUROC of 0.797 in the test set. This model holds potential to provide personalized and accurate diagnostic and therapeutic decision-making data, thereby benefiting patients in clinical practice. Imaging tests remain critical in the diagnosis of MMP. Future research should explore the integration of deep learning and multimodal data approaches, incorporating more comprehensive information to construct more robust diagnostic prediction models.

This study found that WBC and LDH are high-risk factors for MMP. A machine learning model was developed, incorporating five preoperative laboratory test results, to identify high-risk populations for MMP, providing personalized and accurate information to support clinical decision-making.

The data regarding this article will be shared by the corresponding author upon reasonable request.

ZCJ: conceptualization, methodology, formal analysis, writing- original draft. JLD: conceptualization, methodology, data curation, formal analysis, writing-review and editing. JY: data curation, writing-review and editing. CXL: conceptualization, data curation, writing-review and editing. ZQL: conceptualization, data curation, writing-review and editing. ZFY: conceptualization, writing-review and editing. YYL: conceptualization, writing-review and editing. PL: conceptualization, methodology, writing-review and editing. YLL: conceptualization, methodology, writing-review and editing, project administration. ZLJ: conceptualization, methodology, funding acquisition, writing-review and editing, project administration. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

This study adhered to the Declaration of Helsinki and the medical ethics committee of Beijing Anzhen Hospital, Capital Medical University, approved this research protocol (approval number: KS2023020). Given the retrospective nature of this study, the requirement for obtaining patient consent was waived.

Dr. Zheli ji was funded by the National Natural Science Foundation of China (82170609, 81970545).

The authors declare no conflict of interest.

Supplementary material associated with this article can be found, in the online version, at https://doi.org/10.31083/RCM37827.