Predicting morbidity and mortality in acute pancreatitis in an Indian population: a comparative study of the BISAP score, Ranson’s score and CT severity index

TLDR: The BISAP score represents a simple way of identifying, within 24 hours of presentation, patients at greater risk of dying and the development of intermediate markers of severity, in a tertiary care centre in east central India.

Abstract: Objective: Our aim was to prospectively evaluate the accuracy of the bedside index for severity in acute pancreatitis (BISAP) score in predicting mortality, as well as intermediate markers of severity, in a tertiary care centre in east central India, which caters mostly for an economically underprivileged population. Methods: A total of 119 consecutive cases with acute pancreatitis were admitted to our institution between November 2012 and October 2014. BISAP scores were calculated for all cases, within 24 hours of presentation. Ranson’s score and computed tomography severity index (CTSI) were also established. The respective abilities of the three scoring systems to predict mortality was evaluated using trend and discrimination analysis. The optimal cut-off score for mortality from the receiver operating characteristics (ROC) curve was used to evaluate the development of persistent organ failure and pancreatic necrosis (PNec). Results: Of the 119 cases, 42 (35.2%) developed organ failure and were classified as severe acute pancreatitis (SAP), 47 (39.5%) developed PNec, and 12 (10.1%) died. The area under the curve (AUC) results for BISAP score in predicting SAP, PNec, and mortality were 0.962, 0.934 and 0.846, respectively. Ranson’s score showed a slightly lower accuracy for predicting SAP (AUC 0.956) and mortality (AUC 0.841). CTSI was the most accurate in predicting PNec, with an AUC of 0.958. The sensitivity and specificity of BISAP score, with a cut-off of � 3 in predicting mortality, were 100% and 69.2%, respectively. Conclusions: The BISAP score represents a simple way of identifying, within 24 hours of presentation, patients at greater risk of dying and the development of intermediate markers of severity. This risk stratification method can be utilized to improve clinical care and facilitate enrolment in clinical trials.