LAUSR

INTRODUCTION: Existing laboratory markers and clinical scoring systems have shown suboptimal accuracies for early prediction of persistent organ failure (POF) in acute pancreatitis (AP). We used information theory and machine learning to select the best-performing panel of circulating cytokines for predicting POF early in the disease course and performed verification of the cytokine panel's prognostic accuracy in an independent AP cohort. METHODS: The derivation cohort included 60 subjects with AP with early serum samples collected between 2007 and 2010. Twenty-five cytokines associated with an acute inflammatory response were ranked by computing the mutual information between their levels and the outcome of POF; 5 high-ranking cytokines were selected. These cytokines were subsequently measured in early serum samples of an independent prospective verification cohort of 133 patients (2012–2016), and the results were trained in a Random Forest classifier. Cross-validated performance metrics were compared with the predictive accuracies of conventional laboratory tests and clinical scores. RESULTS: Angiopoietin 2, hepatocyte growth factor, interleukin 8, resistin, and soluble tumor necrosis factor receptor 1A were the highest-ranking cytokines in the derivation cohort; each reflects a pathologic process relevant to POF. A Random Forest classifier trained the cytokine panel in the verification cohort and achieved a 10-fold cross-validated accuracy of 0.89 (area under the curve 0.91, positive predictive value 0.89, and negative predictive value 0.90), which outperformed individual cytokines, laboratory tests, and clinical scores (all P ≤ 0.006). DISCUSSION: We developed a 5-cytokine panel, which accurately predicts POF early in the disease process and significantly outperforms the prognostic accuracy of existing laboratory tests and clinical scores.