Enhancing the Prediction of Cardiac Allograft Vasculopathy Using Intravascular Ultrasound and Machine Learning: A Proof of Concept.
Circ Heart Fail
; 17(2): e011306, 2024 02.
Article
em En
| MEDLINE
| ID: mdl-38314558
ABSTRACT
BACKGROUND:
Cardiac allograft vasculopathy (CAV) is the leading cause of late graft dysfunction in heart transplantation. Building on previous unsupervised learning models, we sought to identify CAV clusters using serial maximal intimal thickness and baseline clinical risk factors to predict the development of early CAV.METHODS:
This is a single-center retrospective study including adult heart transplantation recipients. A latent class mixed-effects model was used to identify patient clusters with similar trajectories of maximal intimal thickness posttransplant and pretransplant covariates associated with each cluster.RESULTS:
Among 186 heart transplantation recipients, we identified 4 patient phenotypes very low, low, moderate, and high risk. The 5-year risk (95% CI) of the International Society for Heart and Lung Transplantation-defined CAV in the high, moderate, low, and very low risk groups was 49.1% (35.2%-68.5%), 23.4% (13.3%-41.2%), 5.0% (1.3%-19.6%), and 0%, respectively. Only patients in the moderate to high risk cluster developed the International Society for Heart and Lung Transplantation CAV 2-3 at 5 years (P=0.02). Of the 4 groups, the low risk group had significantly younger female recipients, shorter ischemic time, and younger female donors compared with the high risk group.CONCLUSIONS:
We identified 4 clusters characterized by distinct maximal intimal thickness trajectories. These clusters were shown to discriminate against the development of angiographic CAV. This approach allows for the personalization of surveillance and CAV-directed treatment before the development of angiographically apparent disease.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doença da Artéria Coronariana
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Transplante de Coração
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Insuficiência Cardíaca
Tipo de estudo:
Observational_studies
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Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
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Female
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Humans
Idioma:
En
Revista:
Circ Heart Fail
/
Circ. Heart fail
/
Circulation. Heart failure
Assunto da revista:
ANGIOLOGIA
/
CARDIOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Canadá