Machine Learning Models to Predict Major Adverse Cardiovascular Events After Orthotopic Liver Transplantation: A Cohort Study.
J Cardiothorac Vasc Anesth
; 35(7): 2063-2069, 2021 Jul.
Article
em En
| MEDLINE
| ID: mdl-33750661
ABSTRACT
OBJECTIVE:
To develop machine learning models that can predict post-transplantation major adverse cardiovascular events (MACE), all-cause mortality, and cardiovascular mortality in patients undergoing liver transplantation (LT).DESIGN:
Retrospective cohort study.SETTING:
High-volume tertiary care center.PARTICIPANTS:
The study comprised 1,459 consecutive patients undergoing LT between January 2008 and December 2019.INTERVENTIONS:
None. MEASUREMENTS AND MAINRESULTS:
MACE, all-cause mortality, and cardiovascular mortality were modeled using logistic regression, least absolute shrinkage and selection surgery regression, random forests, support vector machine, and gradient-boosted modeling (GBM). All models were built by splitting data into training and testing cohorts, and performance was assessed using five-fold cross-validation based on the area under the receiver operating characteristic curve and Harrell's C statistic. A total of 1,459 patients were included in the final cohort; 1,425 (97.7%) underwent index transplantation, 963 (66.0%) were female, the median age at transplantation was 57 (11-70) years, and the median Model for End-Stage Liver Disease score was 20 (6-40). Across all outcomes, the GBM model XGBoost achieved the highest performance, with an area under the receiver operating curve of 0.71 (95% confidence interval [CI] 0.63-0.79) for MACE, a Harrell's C statistic of 0.64 (95% CI 0.57-0.73) for overall survival, and 0.72 (95% CI 0.59-0.85) for cardiovascular mortality over a mean follow-up of 4.4 years. Examination of Shapley values for the GBM model revealed that on the cohort-wide level, the top influential factors for postoperative MACE were age at transplantation, diabetes, serum creatinine, cirrhosis caused by nonalcoholic steatohepatitis, right ventricular systolic pressure, and left ventricular ejection fraction.CONCLUSION:
Machine learning models developed using data from a tertiary care transplantation center achieved good discriminant function in predicting post-LT MACE, all-cause mortality, and cardiovascular mortality. These models can support clinicians in recipient selection and help screen individuals who may be at elevated risk for post-transplantation MACE.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
6_ODS3_enfermedades_notrasmisibles
Base de dados:
MEDLINE
Assunto principal:
Doenças Cardiovasculares
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Transplante de Fígado
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Doença Hepática Terminal
Tipo de estudo:
Diagnostic_studies
/
Etiology_studies
/
Incidence_studies
/
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Female
/
Humans
Idioma:
En
Revista:
J Cardiothorac Vasc Anesth
Ano de publicação:
2021
Tipo de documento:
Article