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.