Using machine learning to improve survival prediction after heart transplantation.
J Card Surg
; 36(11): 4113-4120, 2021 Nov.
Article
em En
| MEDLINE
| ID: mdl-34414609
ABSTRACT
BACKGROUND:
This study investigates the use of modern machine learning (ML) techniques to improve prediction of survival after orthotopic heart transplantation (OHT).METHODS:
Retrospective study of adult patients undergoing primary, isolated OHT between 2000 and 2019 as identified in the United Network for Organ Sharing (UNOS) registry. The primary outcome was 1-year post-transplant survival. Patients were randomly divided into training (80%) and validation (20%) sets. Dimensionality reduction and data re-sampling were employed during training. Multiple machine learning algorithms were combined into a final ensemble ML model. The discriminatory capability was assessed using the area under receiver-operating-characteristic curve (AUROC), net reclassification index (NRI), and decision curve analysis (DCA).RESULTS:
A total of 33,657 OHT patients were evaluated. One-year mortality was 11% (n = 3738). In the validation cohort, the AUROC of singular logistic regression was 0.649 (95% CI, 0.628-0.670) compared to 0.691 (95% CI, 0.671-0.711) with random forest, 0.691 (95% CI, 0.671-0.712) with deep neural network, and 0.653 (95% CI, 0.632-0.674) with Adaboost. A final ensemble ML model was created that demonstrated the greatest improvement in AUROC 0.764 (95% CI, 0.745-0.782) (p < .001). The ensemble ML model improved predictive performance by 72.9% ±3.8% (p < .001) as assessed by NRI compared to logistic regression. DCA showed the final ensemble method improved risk prediction across the entire spectrum of predicted risk as compared to all other models (p < .001).CONCLUSIONS:
Modern ML techniques can improve risk prediction in OHT compared to traditional approaches. This may have important implications in patient selection, programmatic evaluation, allocation policy, and patient counseling and prognostication.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Transplante de Coração
/
Aprendizado de Máquina
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
/
Humans
Idioma:
En
Revista:
J Card Surg
Assunto da revista:
CARDIOLOGIA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos