Using the Super Learner algorithm to predict risk of major adverse cardiovascular events after percutaneous coronary intervention in patients with myocardial infarction.
BMC Med Res Methodol
; 24(1): 59, 2024 Mar 08.
Article
em En
| MEDLINE
| ID: mdl-38459490
ABSTRACT
BACKGROUND:
The primary treatment for patients with myocardial infarction (MI) is percutaneous coronary intervention (PCI). Despite this, the incidence of major adverse cardiovascular events (MACEs) remains a significant concern. Our study seeks to optimize PCI predictive modeling by employing an ensemble learning approach to identify the most effective combination of predictive variables. METHODS ANDRESULTS:
We conducted a retrospective, non-interventional analysis of MI patient data from 2018 to 2021, focusing on those who underwent PCI. Our principal metric was the occurrence of 1-year postoperative MACEs. Variable selection was performed using lasso regression, and predictive models were developed using the Super Learner (SL) algorithm. Model performance was appraised by the area under the receiver operating characteristic curve (AUC) and the average precision (AP) score. Our cohort included 3,880 PCI patients, with 475 (12.2%) experiencing MACEs within one year. The SL model exhibited superior discriminative performance, achieving a validated AUC of 0.982 and an AP of 0.971, which markedly surpassed the traditional logistic regression models (AUC 0.826, AP 0.626) in the test cohort. Thirteen variables were significantly associated with the occurrence of 1-year MACEs.CONCLUSION:
Implementing the Super Learner algorithm has substantially enhanced the predictive accuracy for the risk of MACEs in MI patients. This advancement presents a promising tool for clinicians to craft individualized, data-driven interventions to better patient outcomes.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Síndrome Coronariana Aguda
/
Intervenção Coronária Percutânea
/
Infarto do Miocárdio
Limite:
Humans
Idioma:
En
Revista:
BMC Med Res Methodol
Assunto da revista:
MEDICINA
Ano de publicação:
2024
Tipo de documento:
Article