Background: Abdominal aortic aneurysm (AAA) poses a substantial risk of rupture and mortality, and endovascular aortic repair (EVAR) remains the predominant treatment. Individuals living at high altitude experience chronic hypoxia and unique physiological adaptations, yet the impact of altitude on AAA characteristics has not been systematically evaluated. No prior study has applied machine learning to identify features distinguishing high-altitude AAA patients.Methods: This dual-center study included 197 patients who underwent EVAR between 2016 and 2023. Clinical characteristics, medical history, medication use, and preoperative laboratory data were collected. Univariable logistic regression was used to identify variables associated with high-altitude residence. Least absolute shrinkage and selection operator (LASSO) regression and seven machine-learning algorithms were applied to select predictive features and develop classification models. Model performance was assessed using receiver operating characteristic (ROC) curves in training and validation sets.Results: Among 197 patients, 28 patients were from high-altitude regions. These patients were younger, predominantly female, and had markedly fewer traditional cardiovascular risk factors. They showed lower creatinine, uric acid, and albumin levels but higher hemoglobin, red blood cell count, and elevated RDW indices. LASSO identified 17 key variables, with RBC, RDW-SD, PLR, and HRR showing positive associations with high-altitude status. Machine-learning models, especially Random Forest, demonstrated strong discriminative performance. Even with age and sex alone, Logistic Regression, LDA, and Naïve Bayes maintained high predictive accuracy.Conclusion: High-altitude AAA patients exhibit distinct demographic, and inflammatory profiles. Machine-learning models effectively identify altitude-associated features and may support precision risk stratification for AAA patients living at high altitude.
Dajie et al. (Thu,) studied this question.