B U Wu1, R S Johannes, X Sun, Y Tabak, D L Conwell, P A Banks. 1. Brigham & Women's Hospital, Division of Gastroenterology, Center for Pancreatic Disease, Harvard Medical School, Boston, Massachusetts 02115, USA. buwu@partners.org
Abstract
BACKGROUND: Identification of patients at risk for mortality early in the course of acute pancreatitis (AP) is an important step in improving outcome. METHODS: Using Classification and Regression Tree (CART) analysis, a clinical scoring system was developed for prediction of in-hospital mortality in AP. The scoring system was derived on data collected from 17,992 cases of AP from 212 hospitals in 2000-2001. The new scoring system was validated on data collected from 18,256 AP cases from 177 hospitals in 2004-2005. The accuracy of the scoring system for prediction of mortality was measured by the area under the receiver operating characteristic curve (AUC). The performance of the new scoring system was further validated by comparing its predictive accuracy with that of Acute Physiology and Chronic Health Examination (APACHE) II. RESULTS: CART analysis identified five variables for prediction of in-hospital mortality. One point is assigned for the presence of each of the following during the first 24 h: blood urea nitrogen (BUN) >25 mg/dl; impaired mental status; systemic inflammatory response syndrome (SIRS); age >60 years; or the presence of a pleural effusion (BISAP). Mortality ranged from >20% in the highest risk group to <1% in the lowest risk group. In the validation cohort, the BISAP AUC was 0.82 (95% CI 0.79 to 0.84) versus APACHE II AUC of 0.83 (95% CI 0.80 to 0.85). CONCLUSIONS: A new mortality-based prognostic scoring system for use in AP has been derived and validated. The BISAP is a simple and accurate method for the early identification of patients at increased risk for in-hospital mortality.
BACKGROUND: Identification of patients at risk for mortality early in the course of acute pancreatitis (AP) is an important step in improving outcome. METHODS: Using Classification and Regression Tree (CART) analysis, a clinical scoring system was developed for prediction of in-hospital mortality in AP. The scoring system was derived on data collected from 17,992 cases of AP from 212 hospitals in 2000-2001. The new scoring system was validated on data collected from 18,256 AP cases from 177 hospitals in 2004-2005. The accuracy of the scoring system for prediction of mortality was measured by the area under the receiver operating characteristic curve (AUC). The performance of the new scoring system was further validated by comparing its predictive accuracy with that of Acute Physiology and Chronic Health Examination (APACHE) II. RESULTS: CART analysis identified five variables for prediction of in-hospital mortality. One point is assigned for the presence of each of the following during the first 24 h: blood ureanitrogen (BUN) >25 mg/dl; impaired mental status; systemic inflammatory response syndrome (SIRS); age >60 years; or the presence of a pleural effusion (BISAP). Mortality ranged from >20% in the highest risk group to <1% in the lowest risk group. In the validation cohort, the BISAP AUC was 0.82 (95% CI 0.79 to 0.84) versus APACHE II AUC of 0.83 (95% CI 0.80 to 0.85). CONCLUSIONS: A new mortality-based prognostic scoring system for use in AP has been derived and validated. The BISAP is a simple and accurate method for the early identification of patients at increased risk for in-hospital mortality.
Authors: Wolfgang Huber; Viktoria Kemnitz; Veit Phillip; Roland M Schmid; Andreas Faltlhauser Journal: Intensive Care Med Date: 2015-08-20 Impact factor: 17.440