Laboratory parameter-based machine learning model for excluding non-alcoholic fatty liver disease (NAFLD) in the general population.
Aliment Pharmacol Ther
; 46(4): 447-456, 2017 08.
Article
en En
| MEDLINE
| ID: mdl-28585725
ABSTRACT
BACKGROUND:
Non-alcoholic fatty liver disease (NAFLD) affects 20%-40% of the general population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Electronic medical records facilitate large-scale epidemiological studies, existing NAFLD scores often require clinical and anthropometric parameters that may not be captured in those databases.AIM:
To develop and validate a laboratory parameter-based machine learning model to detect NAFLD for the general population.METHODS:
We randomly divided 922 subjects from a population screening study into training and validation groups; NAFLD was diagnosed by proton-magnetic resonance spectroscopy. On the basis of machine learning from 23 routine clinical and laboratory parameters after elastic net regulation, we evaluated the logistic regression, ridge regression, AdaBoost and decision tree models. The areas under receiver-operating characteristic curve (AUROC) of models in validation group were compared.RESULTS:
Six predictors including alanine aminotransferase, high-density lipoprotein cholesterol, triglyceride, haemoglobin A1c , white blood cell count and the presence of hypertension were selected. The NAFLD ridge score achieved AUROC of 0.87 (95% CI 0.83-0.90) and 0.88 (0.84-0.91) in the training and validation groups respectively. Using dual cut-offs of 0.24 and 0.44, NAFLD ridge score achieved 92% (86%-96%) sensitivity and 90% (86%-93%) specificity with corresponding negative and positive predictive values of 96% (91%-98%) and 69% (59%-78%), and 87% of overall accuracy among 70% of classifiable subjects in the validation group; 30% of subjects remained indeterminate.CONCLUSIONS:
NAFLD ridge score is a simple and robust reference comparable to existing NAFLD scores to exclude NAFLD patients in epidemiological studies.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Alanina Transaminasa
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Enfermedad del Hígado Graso no Alcohólico
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Aprendizaje Automático
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Lipoproteínas HDL
Tipo de estudio:
Clinical_trials
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Diagnostic_studies
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Observational_studies
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Prevalence_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Aliment Pharmacol Ther
Asunto de la revista:
FARMACOLOGIA
/
GASTROENTEROLOGIA
/
TERAPIA POR MEDICAMENTOS
Año:
2017
Tipo del documento:
Article
País de afiliación:
Hong Kong