Machine Learning Prediction Models for In-Hospital Mortality After Transcatheter Aortic Valve Replacement.
JACC Cardiovasc Interv
; 12(14): 1328-1338, 2019 07 22.
Article
en En
| MEDLINE
| ID: mdl-31320027
ABSTRACT
OBJECTIVES:
This study sought to develop and compare an array of machine learning methods to predict in-hospital mortality after transcatheter aortic valve replacement (TAVR) in the United States.BACKGROUND:
Existing risk prediction tools for in-hospital complications in patients undergoing TAVR have been designed using statistical modeling approaches and have certain limitations.METHODS:
Patient data were obtained from the National Inpatient Sample database from 2012 to 2015. The data were randomly divided into a development cohort (n = 7,615) and a validation cohort (n = 3,268). Logistic regression, artificial neural network, naive Bayes, and random forest machine learning algorithms were applied to obtain in-hospital mortality prediction models.RESULTS:
A total of 10,883 TAVRs were analyzed in our study. The overall in-hospital mortality was 3.6%. Overall, prediction models' performance measured by area under the curve were good (>0.80). The best model was obtained by logistic regression (area under the curve 0.92; 95% confidence interval 0.89 to 0.95). Most obtained models plateaued after introducing 10 variables. Acute kidney injury was the main predictor of in-hospital mortality ranked with the highest mean importance in all the models. The National Inpatient Sample TAVR score showed the best discrimination among available TAVR prediction scores.CONCLUSIONS:
Machine learning methods can generate robust models to predict in-hospital mortality for TAVR. The National Inpatient Sample TAVR score should be considered for prognosis and shared decision making in TAVR patients.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Técnicas de Apoyo para la Decisión
/
Mortalidad Hospitalaria
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Reemplazo de la Válvula Aórtica Transcatéter
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Aprendizaje Automático
Tipo de estudio:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Aged
/
Aged80
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Female
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Humans
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Male
País/Región como asunto:
America do norte
Idioma:
En
Año:
2019
Tipo del documento:
Article