A new logistic regression model for early prediction of severity of acute pancreatitis using magnetic resonance imaging and Acute Physiology and Chronic Health Evaluation II scoring systems

BACKGROUND: The aim of this study was to develop a new model constructed by logistic regression for the early prediction of the severity of acute pancreatitis (AP) using magnetic resonance imaging (MRI) and the Acute Physiology and Chronic Health Evaluation II (APACHE II) scoring system. METHODS: This retrospective study included 363 patients with AP. The severity of AP was evaluated by MRI and the APACHE II scoring system, and some subgroups of AP severity were constructed based on a combination of these two scoring systems. The length of stay and occurrence of organ dysfunction were used as clinical outcome indicators and were compared across the different subgroups. We combined the MRI and APACHE II scoring system to construct the regression equations and evaluated the diagnostic efficacy of these models. RESULTS: In the 363 patients, 144 (39.67%) had systemic inflammatory response syndrome (SIRS), 58 (15.98%) had organ failure, and 17 (4.68%) had severe AP. The AP subgroup with a high MRI score and a simultaneously high APACHE II score was more likely to develop SIRS and had a longer hospitalization. The model, which predicted the severity AP by combining extrapancreatic inflammation on magnetic resonance (EPIM) and APACHE II, was successful, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.912, which was higher than that of any single parameter. Other models that predicted SIRS complications by combining MRI parameters and APACHE II scores were also successful (all P<0.05), and these models based on EPIM and APACHE II scores were superior to other models in predicting outcome. CONCLUSIONS: The combination of MRI and clinical scoring systems to assess the severity of AP is feasible, and these models may help to develop personalized treatment and management.