ArticleClinical interventions in aging2025
Developing a LASSO Regression-Based Nomogram to Predict Outcomes in Abdominal Aortic Aneurysm Patients Post-Endovascular Aneurysm Repair.
Article in Clinical interventions in aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: This study aimed to develop a prognostic model to predict outcomes in patients undergoing endovascular aneurysm repair (EVAR) for abdominal aortic aneurysms (AAA). Methods: 304 participants were divided into training and validation sets in a 7:3 ratio. Six risk factors were identified using LASSO regression, univariate, and multivariate Cox regression analyses: history of stroke, CIA atherosclerosis, age, hemoglobin levels, monocyte count, and large AAA. A nomogram was constructed to predict 1-year and 3-year all-cause mortality (ACM). Results: A total of 304 AAA patients who underwent EVAR were included in this study (84.87% male; median age 72 [IQR: 65-77] years). The model showed good predictive performance, with area under the curve (AUC) values of 0.84 (95% CI: 0.79-0.89) and 0.81 (95% CI: 0.76-0.86) for 1-year and 3-year mortality in the training set, and 0.71 (95% CI: 0.62-0.80) and 0.80 (95% CI: 0.73-0.87) in the validation set. Discussion: These results suggest the model's effectiveness in aiding clinicians with risk stratification and tailoring treatment strategies for post-EVAR patients.
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