Artificial neural network predicts the need for therapeutic ERCP in patients with suspected choledocholithiasis - 16/07/14
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.
Le texte complet de cet article est disponible en PDF.Abbreviations : ANN, ASGE, AUC, CBD, CDL, CI, ROC
Plan
| DISCLOSURE: The authors disclosed no financial relationships relevant to this publication. |
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| If you would like to chat with an author of this article, you may contact Dr Jovanovic at prredo@yahoo.com. |
Vol 80 - N° 2
P. 260-268 - août 2014 Retour au numéro
Article précédent
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