Artificial neural network predicts the need for therapeutic ERCP in patients with suspected choledocholithiasis.
Gastrointest Endosc
; 80(2): 260-8, 2014 Aug.
Article
em En
| MEDLINE
| ID: mdl-24593947
ABSTRACT
BACKGROUND:
Selection of patients with the highest probability for therapeutic ERCP remains an important task in a clinical workup of patients with suspected choledocholithiasis (CDL).OBJECTIVE:
To determine whether an artificial neural network (ANN) model can improve the accuracy of selecting patients with a high probability of undergoing therapeutic ERCP among those with strong clinical suspicion of CDL and to compare it with our previously reported prediction model.DESIGN:
Prospective, observational study.SETTING:
Single, tertiary-care endoscopy center. PATIENTS Between January 2010 and September 2012, we prospectively recruited 291 consecutive patients who underwent ERCP after being referred to our center with firm suspicion for CDL.INTERVENTIONS:
Predictive scores for CDL based on a multivariate logistic regression model and ANN model. MAIN OUTCOME MEASUREMENTS The presence of common bile duct stones confirmed by ERCP.RESULTS:
There were 80.4% of patients with positive findings on ERCP. The area under the receiver-operating characteristic curve for our previously established multivariate logistic regression model was 0.787 (95% CI, 0.720-0.854; P < .001), whereas area under the curve for the ANN model was 0.884 (95% CI, 0.831-0.938; P < .001). The ANN model correctly classified 92.3% of patients with positive findings on ERCP and 69.6% patients with negative findings on ERCP.LIMITATIONS:
Only those variables believed to be related to the outcome of interest were included. The majority of patients in our sample had positive findings on ERCP.CONCLUSIONS:
An ANN model has better discriminant ability and accuracy than a multivariate logistic regression model in selecting patients for therapeutic ERCP.
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Colangiopancreatografia Retrógrada Endoscópica
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Redes Neurais de Computação
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Seleção de Pacientes
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Coledocolitíase
Tipo de estudo:
Etiology_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Adult
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Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article