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% Cl 0.79 to 0.84) versus APACHE II AUC of 0.83 (95% Cl 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.

The early prediction of mortality in acute pancreatitis : a large population-based study. Commentary

Introduction: Studying changes in the whole set of small molecules, final products of biochemical reactions in living systems or metabolites, is extremely appealing because they represent the best ...

A review of validated biomarkers obtained through metabolomics

Acute pancreatitis (AP) is a severe inflammation of the pancreas presented with sudden onset and severe abdominal pain with a high morbidity and mortality rate, if accompanied by severe local and systemic complications. Numerous studies have been published about the pathogenesis of AP; however, the precise mechanism behind this pathology remains unclear. Extensive research conducted over the last decades has demonstrated that the first 24 h after symptom onset are critical for the identification of patients who are at risk of developing complications or death. The identification of these subgroups of patients is crucial in order to start an aggressive approach to prevent mortality. In this sense and to avoid unnecessary overtreatment, thereby reducing the financial implications, the proper identification of mild disease is also important and necessary. A large number of multifactorial scoring systems and biochemical markers are described to predict the severity. Despite recent progress in understanding the pathophysiology of AP, more research is needed to enable a faster and more accurate prediction of severe AP. This review provides an overview of the available multifactorial scoring systems and biochemical markers for predicting severe AP with a special focus on their advantages and limitations.

https://www.mdpi.com/1422-0067/21/1/338/pdf

Multifactorial Scores and Biomarkers of Prognosis of Acute Pancreatitis: Applications to Research and Practice

While drugs remain the cornerstone of medicine, herbal medicine is an important comedication worldwide. Thus, precision medicine ought to face this clinical reality and develop "companion diagnostics" for drugs as well as herbal medicines. Yet, many are in denial with respect to the extent of use of traditional/herbal medicines, overlooking that a considerable number of contemporary therapeutic drugs trace their discovery from herbal medicines. This expert review underscores that absent such appropriate attention on both classical drug therapy and herbal medicines, precision medicine biomarkers will likely not stand the full test of clinical practice while patients continue to use both drugs and herbal medicines and, yet the biomarker research and applications focus only (or mostly) on drug therapy. This asymmetry in biomarker innovation strategy needs urgent attention from a wide range of innovation actors worldwide, including governments, research funders, scientists, community leaders, civil society organizations, herbal, pharmaceutical, and insurance industries, policymakers, and social/political scientists. We discuss the various dimensions of a future convergence map between herbal and conventional medicine, and conclude with a set of concrete strategies on how best to integrate biomarker research in a realm of both herbal and drug treatment. Africa, by virtue of its vast experience and exposure in herbal medicine and a "pregnant" life sciences innovation ecosystem, could play a game-changing role for the "birth" of biomarker-informed personalized herbal medicine in the near future. At this critical juncture when precision medicine initiatives are being rolled out worldwide, precision/personalized herbal medicine is both timely and essential for modern therapeutics, not to mention biomarker innovations that stand the test of real-life practices and implementation in the clinic and society.

Personalized Herbal Medicine? A Roadmap for Convergence of Herbal and Precision Medicine Biomarker Innovations

Objective Chronic pancreatitis (CP) is a fibroinflammatory syndrome leading to organ dysfunction, chronic pain, an increased risk for pancreatic cancer and considerable morbidity. Due to a lack of specific biomarkers, diagnosis is based on symptoms and specific but insensitive imaging features, preventing an early diagnosis and appropriate management. Design We conducted a type 3 study for multivariable prediction for individual prognosis according to the TRIPOD guidelines. A signature to distinguish CP from controls (n=160) was identified using gas chromatography-mass spectrometry and liquid chromatography‐tandem mass spectrometry on ethylenediaminetetraacetic acid (EDTA)-plasma and validated in independent cohorts. Results A Naive Bayes algorithm identified eight metabolites of six ontology classes. After algorithm training and computation of optimal cut-offs, classification according to the metabolic signature detected CP with an area under the curve (AUC) of 0.85 ((95% CI 0.79 to 0.91). External validation in two independent cohorts (total n=502) resulted in similar accuracy for detection of CP compared with non-pancreatic controls in EDTA-plasma (AUC 0.85 (95% CI 0.81 to 0.89)) and serum (AUC 0.87 (95% CI 0.81 to 0.95)). Conclusions This is the first study that identifies and independently validates a metabolomic signature in plasma and serum for the diagnosis of CP in large, prospective cohorts. The results could provide the basis for the development of the first routine laboratory test for CP.

https://gut.bmj.com/content/gutjnl/early/2021/02/18/gutjnl-2020-320723.full.pdf

Identification and validation of a multivariable prediction model based on blood plasma and serum metabolomics for the distinction of chronic pancreatitis subjects from non-pancreas disease control subjects

Xing-wen Zhang

Papers

Identification of potential diagnostic biomarkers of acute pancreatitis by serum metabolomic profiles

GC-MS based metabolomics strategy to distinguish three types of acute pancreatitis.