Does a LASSO regression-based nomogram predict all-cause mortality in patients undergoing endovascular aneurysm repair for abdominal aortic aneurysms?
A LASSO regression-based nomogram incorporating clinical and laboratory factors effectively predicts 1- and 3-year all-cause mortality in patients post-EVAR, aiding in risk stratification.
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.
Yu et al. (Sat,) studied this question.
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