Identification of severe acute pancreatitis using an artificial neural network

Abstract

The aim of this study was to construct and validate an artificial neural network (ANN) model to identify severe acute pancreatitis (AP) and predict fatal outcome. All patients who presented with AP from January 2000 to September 2004 were reviewed. Presentation data on admission and at 48 hours were collected. Acute Physiology and Chronic Health Evaluation (APACHE) II and Glasgow severity (GS) score were calculated. A feed-forward ANN was created and trained to predict development of severe AP and mortality from AP; 25% of the data set was withheld from training and was used to evaluate the accuracy of the ANN. Accuracy of the ANN in predicting severity of AP was compared with APACHE II and GS scores. A total of 664 patients with AP were identified of whom 181 (27.3%) fulfilled the clinical and radiologic criteria for severe pancreatitis and 42 patients died (6.3%). Median APACHE II score at 48 hours was 4 (range, 0 to 23). ANN was more accurate than APACHE II or GS scoring systems at predicting progression to a severe course (P < .05 and P < .01, respectively), predicting development of multiorgan dysfunction syndrome (P < .05 and P < .01) and at predicting death from AP (P < .05). An ANN was able to predict progression to severe disease, development of organ failure and mortality from acute pancreatitis with considerable accuracy and outperformed other clinical risk scoring systems. Further studies are required to assess its utility in aiding management decisions in patients with AP.