Abstract
Background Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of in-hospital mortality in AP. Methods Patients with AP were identified from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. Clinical and laboratory data were utilized to perform a predictive model by back propagation ANN approach. Results A total of 337 patients with AP were analyzed in the study, and the in-hospital mortality rate was 11.2%. A total of 12 variables that differed between patients in survivor group and nonsurvivor group were applied to construct ANN model. Three independent variables were identified as risk factors associated with in-hospital mortality by multivariate logistic regression analysis. The predictive performance based on the area under the receiver operating characteristic curve (AUC) was 0.769 for ANN model, 0.607 for logistic regression, 0.652 for Ranson score, and 0.401 for SOFA score. Conclusion An ANN predictive model for in-hospital mortality in patients with AP in MIMIC-III database was first performed. The patients with high risk of fatal outcome can be screened out easily in the early stage of AP by our model.
Highlights
And accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant
An early accurate evaluation of severity and prognosis of AP, especially at the time of admission is significant for physicians to take many attentions and more effective managements to the patients whose physical condition may be likely to getting worse
Previous studies illuminated that some laboratory variables such as red cell distribution width (RDW) [2] and hematocrit (HCT) [3] and several scoring systems including Ranson [4] and sequential organ failure assessment (SOFA) [5] were applied to evaluate the prognosis of AP
Summary
And accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of in-hospital mortality in AP. An ANN predictive model for in-hospital mortality in patients with AP in MIMIC-III database was first performed. An early accurate evaluation of severity and prognosis of AP, especially at the time of admission is significant for physicians to take many attentions and more effective managements to the patients whose physical condition may be likely to getting worse. Due to the fluctuation in accuracy of single laboratory variable, the predictive performance could be affected. Both Ranson and SOFA scores include around ten variables and need to be recorded dynamically; the availability of Ranson and SOFA scores in early prediction has been limited. It is necessary to construct an early predictive model with better accuracy
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