Prediction model for cardiac allograft vasculopathy: Comparison of three multivariable methods.
Clin Transplant
; 31(4)2017 04.
Article
em En
| MEDLINE
| ID: mdl-28181298
ABSTRACT
BACKGROUND:
Cardiac allograft vasculopathy (CAV) remains an important cause of graft failure after heart transplantation (HT). Although many risk factors for CAV have been identified, there are no clinical prediction models that enable clinicians to determine each recipient's risk of CAV.METHODS:
We studied a cohort of 14 328 heart transplant recipients whose data were reported to the International Society for Heart and Lung Transplantation Registry between 2000 and 2010. The cohort was divided into training (75%) and test (25%) sets. Multivariable modeling was performed in the test set using variables available at the time of heart transplant using threemethods:
(i) stepwise Cox proportional hazard, (ii) regularized Cox proportional hazard, and (iii) Bayesian network.RESULTS:
Cardiac allograft vasculopathy developed in 4259 recipients (29.7%) at a median time of 3.0 years after HT. The regularized Cox proportional hazard model yielded the optimal performance and was also the most parsimonious. We deployed this model as an Internet-based risk calculator application.CONCLUSIONS:
We have developed a clinical prediction model for assessing a recipient's risk of CAV using variables available at the time of HT. Application of this model may allow clinicians to determine which recipients will benefit from interventions to reduce the risk of development and progression of CAV.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Complicações Pós-Operatórias
/
Modelos de Riscos Proporcionais
/
Transplante de Coração
/
Rejeição de Enxerto
/
Cardiopatias
Tipo de estudo:
Diagnostic_studies
/
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
Clin Transplant
Assunto da revista:
TRANSPLANTE
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
Estados Unidos