To construct a risk model for discriminating abdominal aortic aneurysm (AAA) rupture and explore its potential mechanism.
Methods:
Clinical data of AAA patients were obtained from the MIMIC-IV database. The multivariable logistic analysis was performed to identify the independent risk factors associated with AAA rupture. The nomogram model was used, and its risk score was calculated. The clinical relevance of the model was assessed by receiver operating characteristic curve analysis and the Kaplan-Meier plotter. The potential mechanism was investigated by the enrichment and immune cell infiltration analyses using the GSE98278 dataset from the Gene Expression Omnibus (GEO) database.
Results:
A total of 309 AAA patients were divided into rupture (n=39) and non-rupture (n=270) groups. White blood cell (WBC), hematocrit (HCT), platelets, and glucose were associated with the AAA rupture (all p<0.05). The risk score of the nomogram model (area under the curve [AUC]=0.746) was a promising index in discriminating AAA rupture. Besides, the high-risk score was related to patients’ survival (1, 5 years) (HR1-year=2.19, 95% CI=1.05-4.56; HR5-year=1.75, 95% CI=1.04-2.93). Based on the 48 AAA tissue samples from the GSE98278 dataset, we found that AAA rupture was involved in inflammation and immune-related pathways mainly regulated by the T cells CD4 memory activated that were linked to the CX3CR1, ANGPTL4, F2RL3, and MT1A expressions (all Cor ≥0.35, all p<0.05).
Conclusion:
The risk score of the nomogram model could discriminate AAA rupture, and it was also linked to the poor prognosis of AAA patients. Moreover, T cells CD4 memory activated may be related to AAA rupture by involving the immune environment.
Clinical Impact
This study identified risk factors associated with AAA rupture, constructed a risk model, and explored its underlying mechanisms. High-risk scores derived from the nomogram model were negatively associated with patient outcomes, indicating that this risk model can serve as a stratification tool to guide individualized intervention strategies. The risk model utilizing fewer indicators can be employed for initial screening, followed by application of composite scores for high-risk patients to optimize clinical decision-making and enhance the efficiency of healthcare resource allocation.
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