Use of an artificial neural network to predict persistent organ failure in patients with acute pancreatitis

Abstract

One-third to one-half of the deaths that occur due to acute pancreatitis (AP) during the first week after the onset of AP symptoms are primarily due to progressive organ failure (OF). Deaths that occur more than one week after admission to the hospital are often associated with local complications, such as infected pancreatic necrosis, and these patients often present symptoms of sepsis and multiple organ dysfunction syndromes (1). A recent meta-analysis indicated that organ failure is the key determinant of severity, regardless of the presence or absence of local pancreatic complications (2). Early identification of patients who are likely to develop organ failure would assist physicians in selecting the patients who would benefit from close surveillance or aggressive intervention. Many prognostic models or single predictors have been developed to predict mortality or the severity of acute pancreatitis, which is primarily defined based on the Atlanta classification (3-6). However, information regarding the early prediction of persistent organ failure in patients with acute pancreatitis is not widely available (7). As one of the clinical prediction rules (8), an artificial neural network (ANN) is composed of a series of interconnecting parallel nonlinear processing elements (nodes) with limited numbers of inputs and outputs (9). A systematic review suggested that artificial neural network analysis is potentially more successful than conventional statistical techniques at predicting clinical outcomes when the relationship between the variables that determine the prognosis is complex, multidimensional and non-linear (10). The aim of this study was to develop an artificial neural network to predict persistent organ failure in patients with acute pancreatitis.