Predicting the Severity of Acute Pancreatitis: Choose the Right Horse Before Hitching the Cart

Abstract

Prediction of disease severity in patients with acute pancreatitis is an important clinical goal. An accurate predictive tool allows early identification of those patients who would require treatment in a high dependency or intensive care unit and transfer to a referral centre [1]. In addition, it allows the selection of patients who definitely need early enteral tube feeding and possibly other (yet to be determined) early treatments. The research on predictors of severity in acute pancreatitis has traditionally been based on the premise of quest for an ideal predictive tool. The characteristics of such a tool are well established: it would be available on admission to hospital, be easily repeated for monitoring purposes, be quick and reproducible [1]. The pioneering work, which investigated the relationship between 43 early measurements and ‘‘overall morbidity and mortality’’ in 100 patients with acute pancreatitis, was published by Ranson nearly four decades ago [2]. Since then, the scientific framework of the concept of prediction in acute pancreatitis has been unquestionable. Moreover, the number of followers has been constantly growing with the research on predictors of severity being arguably the most prolific area in the literature on acute pancreatitis for many years. A recent systematic review of the literature found 184 original studies that reported on 196 different predictors of severity in acute pancreatitis [3]. Strikingly, 144 of 184 (78%) studies reported a statistically significant result for at least one predictor. It is also worth noting that the search was limited to studies indexed only in MEDLINE and published only in English. Further, it also only focused on novel (non-routine) molecular markers, which effectively means that many routine markers (urea, creatinine, lactate dehydrogenase, C-reactive protein, hematocrit, blood gases, etc.) as well as several modern computer-based predictive tools in acute pancreatitis (artificial neural network, kernel-based modelling, linear discriminant analysis [4–6]) were not counted. Collectively, these indicate that the literature is replete with dozens, if not hundreds, of presumably effective ways to predict the severity of acute pancreatitis, but it appears that very few have entered clinical practice. There are several legitimate reasons for this lack of penetration: the predictive tools are often complex, cumbersome, expensive, and not available commercially [7]. But the most important reason is that they are notoriously inaccurate when it comes to prediction of an individual patient’s severity. In this issue of the journal, Dr. Hong et al. [8] report on a novel computer-based predictive tool in acute pancreatitis—classification and regression tree (CART) analysis. CART is a non-parametric technique that can select from among a large set of variables, those that individually, or in combination, best predict the endpoint of interest by splitting the initial cohort sequentially into smaller subsets. The method has the potential to become a valuable tool in the field of acute pancreatitis because it can not only assess which individual variables are most accurate in predicting the severity but also define their optimal combination and order (so-called ‘‘decision tree’’). The study by Dr. Hong and colleagues advocates three variables for severity prediction, namely blood urea nitrogen, pleural effusion, and serum calcium, and suggests a particular order in which to use them. It is reported that the CART model has a high M. S. Petrov (&) Department of Surgery, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand e-mail: max.petrov@gmail.com