A machine learning approach for predicting complications in descending and thoracoabdominal aortic aneurysms.
J Thorac Cardiovasc Surg
; 166(4): 1011-1020.e3, 2023 10.
Article
em En
| MEDLINE
| ID: mdl-35120761
ABSTRACT
OBJECTIVE:
To use machine learning to predict rupture, dissection, and all-cause mortality for patients with descending and thoracoabdominal aortic aneurysms in an effort to improve on diameter-based surgical intervention criteria.METHODS:
Retrospective data from 1083 patients with descending aortic diameters 3.0 cm or greater were collected, with a mean follow-up time of 3.52 years and an average descending diameter of 4.13 cm. Six machine learning classifiers were trained using 44 variables to predict the occurrence of dissection, rupture, or all-cause mortality within 1, 2, or 5 years of initial patient encounter for a total of 54 (6 × 3 × 3) separate classifiers. Classifier performance was measured using area under the receiver operator curve.RESULTS:
Machine learning models achieved area under the receiver operator curves of 0.842 to 0.872 when predicting type B dissection, 0.847 to 0.856 when predicting type B dissection or rupture, and 0.820 to 0.845 when predicting type B dissection, rupture, or all-cause mortality. All models consistently outperformed descending aortic diameter across all end points (area under the receiver operator curve = 0.713-0.733). Feature importance inspection showed that other features beyond aortic diameter, such as a history of myocardial infarction, hypertension, and patient sex, play an important role in improving risk prediction.CONCLUSIONS:
This study provides surgeons with a more accurate, machine learning-based, risk-stratification metric to predict complications for patients with descending aortic aneurysms.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aneurisma da Aorta Torácica
/
Aneurisma da Aorta Toracoabdominal
/
Hipertensão
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article