Artificial neural networks accurately predict intra-abdominal infection in moderately severe and severe acute pancreatitis

Objective The aim of this study was to evaluate the efficacy of artificial neural networks (ANN) in predicting intra-abdominal infection in moderately severe (MASP) and severe acute pancreatitis (SAP) compared with that of a logistic regression model (LRM). Methods Patients suffering from MSAP or SAP from July 2014 to June 2017 in three affiliated hospitals of the Army Medical University in Chongqing, China, were enrolled in this study. A univariate analysis was used to determine the different parameters between patients with and without intra-abdominal infection. Subsequently, these parameters were used to build LRM and ANN. Results Altogether 263 patients with MSAP or SAP were enrolled in this retrospective study. A total of 16 parameters that differed between patients with and without intra-abdominal infection were used to construct both models. The sensitivity of ANN and LRM was 80.99% (95% confidence interval [CI] 72.63-87.33) and 70.25% (95% CI 61.15-78.04), respectively (P > 0.05), whereas the specificity was 89.44% (95% CI 82.89-93.77) and 77.46% (95% CI 69.54-83.87), respectively (P < 0.05). ANN predicted the risk of intra-abdominal infection better than LRM (area under the receiver operating characteristic curve: 0.923 [0.883-0.952] vs 0.802 [0.749-0.849], P < 0.001). Conclusions ANN accurately predicted intra-abdominal infection in MSAP and SAP and is an ideal tool for predicting intra-abdominal infection in such patients. Coagulation parameters played an important role in such prediction.