The BISAP score, NLR, CRP, or BUN: Which marker best predicts the outcome of acute pancreatitis?

Immune markers of severe acute pancreatitis

PURPOSE OF REVIEW

Acute pancreatitis is a common acute inflammatory disorder of the pancreas, and its incidence has been increasing worldwide. Approximately 10% of acute pancreatitis progresses to severe acute pancreatitis (SAP), which carries significant morbidity and mortality. Disordered immune response to pancreatic injury is regarded as a key event that mediates systemic injury in SAP. In this article, we review recent developments in immune biomarkers of SAP and future directions for research.

RECENT FINDINGS

Given the importance of the NLRP3-inflammasome pathway in mediating systemic inflammatory response syndrome and systemic injury, recent studies have investigated associations of SAP with systemic levels of activators of NLRP3, such as the damage associated molecular patterns (DAMPs) for the first time in human SAP. For example, circulating levels of histones, mitochondrial DNAs, and cell free DNAs have been associated with SAP. A panel of mechanistically relevant immune markers (e.g., panel of Angiopoeitin-2, hepatocyte growth factor, interleukin-8 (IL-8), resistin and sTNF-α R1) carried higher predictive accuracies than existing clinical scores and individual immune markers. Of the cytokines with established relevance to SAP pathogenesis, phase 2 trials of immunotherapies, including tumor necrosis factor (TNF)-alpha inhibition and stimulation of IL-10 production, are underway to determine if altering the immunologic response can reduce the severity of acute pancreatitis (AP).

SUMMARY

Circulating systemic levels of various DAMPs and a panel of immune markers that possibly reflect activities of different pathways that drive SAP appear promising as predictive biomarkers for SAP. But larger multicenter studies are needed for external validation. Studies investigating immune cellular pathways driving SAP using immunophenotyping techniques are scarce. Interdisciplinary efforts are also needed to bring some of the promising biomarkers to the bedside for validation and testing for clinical utility. Studies investigating the role of and characterization of altered gut-lymph and gut-microbiota in severe AP are needed.

Radiomics models of contrast-enhanced computed tomography for predicting the activity and prognosis of acute pancreatitis

BACKGROUND

The modified pancreatitis activity scoring system (mPASS) was proposed to assess the activity of acute pancreatitis (AP) while it doesn't include indicators that directly reflect pathophysiology processes and imaging characteristics.

OBJECTIVES

To determine the threshold of admission mPASS and investigate radiomics and laboratory parameters to construct a model to predict the activity of AP.

METHODS

AP inpatients at institution 1 were randomly divided into training and validation groups based on a 5:5 ratio. AP inpatients at Institution 2 were served as test group. The cutoff value of admission mPASS scores in predicting severe AP was selected to divide patients into high and low level of disease activity group. LASSO was used in screening features. Multivariable logistic regression was used to develop radiomics model. Meaningful laboratory parameters were used to construct combined model.

RESULTS

There were 234 (48 years ± 10, 155 men) and 101 (48 years ± 11, 69 men) patients in two institutions. The threshold of admission mPASS score was 112.5 in severe AP prediction. The AUC of the radiomics model was 0.79, 0.72, and 0.76 and that of the combined model incorporating rad-score and white blood cell were 0.84, 0.77, and 0.80 in three groups for activity prediction. The AUC of the combined model in predicting disease without remission was 0.74.

CONCLUSIONS

The threshold of admission mPASS was 112.5 in predicting severe AP. The model based on CECT radiomics has the ability to predict AP activity. Its ability to predict disease without remission is comparable to mPASS.

CRITICAL RELEVANCE STATEMENT

This work is the first attempt to assess the activity of acute pancreatitis using contrast-enhanced CT radiomics and laboratory parameters. The model provides a new method to predict the activity and prognosis of AP, which could contribute to further management.

KEY POINTS

Radiomics features and laboratory parameters are associated with the activity of acute pancreatitis. The combined model provides a new method to predict the activity and prognosis of AP. The ability of the combined model is comparable to the modified Pancreatitis Activity Scoring System.

American College of Gastroenterology Guidelines: Management of Acute Pancreatitis

Acute pancreatitis (AP), defined as acute inflammation of the pancreas, is one of the most common diseases of the gastrointestinal tract leading to hospital admission in the United States. It is important for clinicians to appreciate that AP is heterogenous, progressing differently among patients and is often unpredictable. While most patients experience symptoms lasting a few days, almost one-fifth of patients will go on to experience complications, including pancreatic necrosis and/or organ failure, at times requiring prolonged hospitalization, intensive care, and radiologic, surgical, and/or endoscopic intervention. Early management is essential to identify and treat patients with AP to prevent complications. Patients with biliary pancreatitis typically will require surgery to prevent recurrent disease and may need early endoscopic retrograde cholangiopancreatography if the disease is complicated by cholangitis. Nutrition plays an important role in treating patients with AP. The safety of early refeeding and importance in preventing complications from AP are addressed. This guideline will provide an evidence-based practical approach to the management of patients with AP.

Clinical usefulness of scoring systems to predict severe acute pancreatitis: A systematic review and meta-analysis with pre and post-test probability assessment

BACKGROUND

Scoring systems for severe acute pancreatitis (SAP) prediction should be used in conjunction with pre-test probability to establish post-test probability of SAP, but data of this kind are lacking.

OBJECTIVE

To investigate the predictive value of commonly employed scoring systems and their usefulness in modifying the pre-test probability of SAP.

METHODS

Following PRISMA statement and MOOSE checklists after PROSPERO registration, PubMed was searched from inception until September 2022. Retrospective, prospective, cross-sectional studies or clinical trials on patients with acute pancreatitis defined as Revised Atlanta Criteria, reporting rate of SAP and using at least one score among Bedside Index for Severity in Acute Pancreatitis (BISAP), Acute Physiology and Chronic Health Examination (APACHE)-II, RANSON, and Systemic Inflammatory Response Syndrome (SIRS) with their sensitivity and specificity were included. Random effects model meta-analyses were performed. Pre-test probability and likelihood ratio (LR) were combined to estimate post-test probability on Fagan nomograms. Pooled severity rate was used as pre-test probability of SAP and pooled sensitivity and specificity to calculate LR and generate post-test probability. A priori hypotheses for heterogeneity were developed and sensitivity analyses planned.

RESULTS

43 studies yielding 14,116 acute pancreatitis patients were included: 42 with BISAP, 30 with APACHE-II, 27 with Ranson, 8 with SIRS. Pooled pre-test probability of SAP ranged 16.6%-25.3%. The post-test probability of SAP with positive/negative score was 47%/6% for BISAP, 43%/5% for APACHE-II, 48%/5% for Ranson, 40%/12% for SIRS. In 18 studies comparing BISAP, APACHE-II, and Ranson in 6740 patients with pooled pre-test probability of SAP of 18.7%, post-test probability when scores were positive was 48% for BISAP, 46% for APACHE-II, 50% for Ranson. When scores were negative, post-test probability dropped to 7% for BISAP, 6% for Ranson, 5% for APACHE-II. Quality, design, and country of origin of the studies did not explain the observed high heterogeneity.

CONCLUSIONS

The most commonly used scoring systems to predict SAP perform poorly and do not aid in decision-making.

NLR48 is Better Than CRP, and mCTSI, and Similar to BISAP and SOFA Scores for Mortality Prediction in Acute Pancreatitis: A Comparison of 6 Scores

Introduction

The neutrophil-lymphocyte ratio (NLR) has been suggested as a reliable marker for predicting inflammation progression and severity of acute pancreatitis, although the role of the NLR stratified by etiology is still insufficiently studied. However, the NLR's role in mortality prediction was poorly evaluated in the literature.

Patients and Methods

We performed a retrospective, cross-sectional study to analyze the role of NLR0 (at admission) and NLR48 (at 48 hours) in acute pancreatitis as compared with CRP, BISAP, SOFA, and modified CTSI (mCTSI) for the prediction of mortality and severe acute pancreatitis (SAP) in patients admitted into the Emergency Clinical County Hospital of Craiova during 48 months. The primary assessed outcomes were the rate of in-hospital mortality, the rate of persistent organ failure, and ICU admissions. We analyzed mortality prediction for all acute pancreatitis, for biliary, alcoholic, and hypertriglyceridemic acute pancreatitis, for severe forms, and for patients admitted to the ICU.

Results

A total of 725 patients were selected; 42.4% had biliary acute pancreatitis, 27.7% had alcoholic acute pancreatitis, and 8.7% had hypertriglyceridemia-induced acute pancreatitis. A total of 13.6% had POF during admission. The AUC for NLR48 in predicting mortality risk and SAP was 0.81 and 0.785, superior to NLR0, CRP48, and mCTSI but inferior to BISAP and SOFA scores. The NLR48/NLR0 ratio did not add significantly to the accuracy. NLR0 and NLR48 performed poorly for mortality prediction in severe forms and in patients admitted to the ICU. NLR48 has good accuracy in our study for predicting death risk in biliary and alcoholic acute pancreatitis but not in hypertriglyceridemic acute pancreatitis.

Conclusion

NLR48 was a good indicator in predicting mortality risk and severe forms in all patients with acute pancreatitis, but not of death in SAP and in patients admitted to ICU, with good accuracy for predicting death risk in biliary and alcoholic acute pancreatitis but not in hypertriglyceridemic acute pancreatitis.

GlycA: Evaluation of a New Biomarker of Acute Pancreatitis

BACKGROUND

Acute pancreatitis (AP) is a leading cause of gastrointestinal hospital admissions, with up to 40% mortality in patients with moderate-severe AP. Glycoprotein acetylation (GlycA) is measured as a nuclear magnetic resonance signal (NMR) of the post-translational modification of glycosylated acute-phase proteins released during inflammation. We aimed to investigate the role of GlycA as an inflammatory biomarker of AP.

METHODS

We prospectively enrolled 20 AP patients and 22 healthy controls and collected EDTA plasma samples at admission and discharge. NMR spectra were acquired from these samples using a 400 MHz Vantera® Clinical Analyzer, and GlycA concentrations were calculated (normal = 400 μmol/L). The GlycA NMR signal, at 2.00 ± 0.01 ppm in the NMR spectrum, is derived from the N-acetyl methyl group protons within the carbohydrate side chains of circulating glycoproteins such as α1-acid glycoprotein, haptoglobin, α1-antitrypsin, α1-antichymotrypsin, and transferrin. GlycA levels were then compared between AP patients and controls, as well as within the AP group, based on etiology and severity.

RESULTS

Demographic comparisons were similar, except for a higher BMI in AP patients compared to healthy controls (29.9 vs. 24.8 kg/m2; p < 0.001). AP was mild in 10 patients, moderate in 7, and severe in 3. GlycA levels were higher in AP patients than healthy controls on admission (578 vs. 376 μmol/L, p < 0.001) and at discharge (655 vs. 376 μmol/L, p < 0.001). GlycA levels were significantly higher in patients with moderate-severe AP than in those with mild AP at discharge (533 vs. 757 μmol/L, p = 0.023) but not at admission. After adjusting for BMI, multivariable regression indicated that patients with GlycA levels > 400 μmol/L had significantly higher odds of having AP of any severity (OR = 6.88; 95% CI, 2.07-32.2; p = 0.004) and mild AP (OR = 6.12; 95% CI, 1.48-42.0; p = 0.025) than controls.

CONCLUSION

Our pilot study highlights the use of GlycA as a novel diagnostic biomarker of inflammation in patients with AP. Our study shows that GlycA levels were significantly higher in hospitalized AP patients compared to healthy controls. Patients with moderate-to-severe AP had higher GlycA levels compared to patients with mild AP at the time of their hospital discharge, suggesting persistent inflammation in patients with severe disease.

C-Reactive Protein: Pathophysiology, Diagnosis, False Test Results and a Novel Diagnostic Algorithm for Clinicians

The current literature provides a body of evidence on C-Reactive Protein (CRP) and its potential role in inflammation. However, most pieces of evidence are sparse and controversial. This critical state-of-the-art monography provides all the crucial data on the potential biochemical properties of the protein, along with further evidence on its potential pathobiology, both for its pentameric and monomeric forms, including information for its ligands as well as the possible function of autoantibodies against the protein. Furthermore, the current evidence on its potential utility as a biomarker of various diseases is presented, of all cardiovascular, respiratory, hepatobiliary, gastrointestinal, pancreatic, renal, gynecological, andrological, dental, oral, otorhinolaryngological, ophthalmological, dermatological, musculoskeletal, neurological, mental, splenic, thyroid conditions, as well as infections, autoimmune-supposed conditions and neoplasms, including other possible factors that have been linked with elevated concentrations of that protein. Moreover, data on molecular diagnostics on CRP are discussed, and possible etiologies of false test results are highlighted. Additionally, this review evaluates all current pieces of evidence on CRP and systemic inflammation, and highlights future goals. Finally, a novel diagnostic algorithm to carefully assess the CRP level for a precise diagnosis of a medical condition is illustrated.

Establishment and Validation of a Nomogram Prediction Model for the Severe Acute Pancreatitis

Background

Severe acute pancreatitis (SAP) can progress to lung and kidney dysfunction, and blood clotting within 48 hours of its onset, and is associated with a high mortality rate. The aim of this study was to establish a reliable diagnostic prediction model for the early stage of severe pancreatitis.

Methods

The clinical data of patients diagnosed with acute pancreatitis from October 2017 to June 2022 at the Shangluo Central Hospital were collected. The risk factors were screened by least absolute shrinkage and selection operator (LASSO) regression analysis. A novel nomogram model was then established by multivariable logistic regression analysis.

Results

The data of 436 patients with acute pancreatitis, 45 (10.3%) patients had progressed to SAP. Through univariate and LASSO regression analyses, the neutrophils (P <0.001), albumin (P < 0.001), blood glucose (P < 0.001), serum calcium (P < 0.001), serum creatinine (P < 0.001), blood urea nitrogen (P < 0.001) and procalcitonin (P = 0.005) were identified as independent predictive factors for SAP. The nomogram built on the basis of these factors predicted SAP with sensitivity of 0.733, specificity of 0.9, positive predictive value of 0.458 and negative predictive value of 0.967. Furthermore, the concordance index of the nomogram reached 0.889 (95% CI, 0.837-0.941), and the area under the curve (AUC) in receiver operating characteristic curve (ROC) analysis was significantly higher than that of the APACHEII and ABISAP scoring systems. The established model was validated by plotting the clinical decision curve analysis (DCA) and clinical impact curve (CIC).

Conclusion

We established a nomogram to predict the progression of early acute pancreatitis to SAP with high discrimination and accuracy.

Early prediction of acute respiratory distress syndrome complicated by acute pancreatitis based on four machine learning models

BACKGROUND

Acute Respiratory Distress syndrome (ARDS) is a common complication of Acute Pancreatitis (AP) and is associated with high mortality. This study used Machine Learning (ML) to predict ARDS in patients with AP at admission.

METHODS

The authors retrospectively analyzed the data from patients with AP from January 2017 to August 2022. Clinical and laboratory parameters with significant differences between patients with and without ARDS were screened by univariate analysis. Then, Support Vector Machine (SVM), Ensembles of Decision Trees (EDTs), Bayesian Classifier (BC), and nomogram models were constructed and optimized after feature screening based on these parameters. Five-fold cross-validation was used to train each model. A test set was used to evaluate the predictive performance of the four models.

RESULTS

A total of 83 (18.04%) of 460 patients with AP developed ARDS. Thirty-one features with significant differences between the groups with and without ARDS in the training set were used for modeling. The Partial Pressure of Oxygen (PaO₂), C-reactive protein, procalcitonin, lactic acid, Ca²⁺, the neutrophil:lymphocyte ratio, white blood cell count, and amylase were identified as the optimal subset of features. The BC algorithm had the best predictive performance with the highest AUC value (0.891) than SVM (0.870), EDTs (0.813), and the nomogram (0.874) in the test set. The EDT algorithm achieved the highest accuracy (0.891), precision (0.800), and F1 score (0.615), but the lowest FDR (0.200) and the second-highest NPV (0.902).

CONCLUSIONS

A predictive model of ARDS complicated by AP was successfully developed based on ML. Predictive performance was evaluated by a test set, for which BC showed superior predictive performance and EDTs could be a more promising prediction tool for larger samples.

Comparing Prognostic Scores and Inflammatory Markers in Predicting the Severity and Mortality of Acute Pancreatitis

BACKGROUND

Acute pancreatitis is an emergency gastrointestinal condition for which severity prediction is crucial during hospitalization. This study aimed to compare the diagnostic accuracy of inflammatory markers with gold standard scoring systems in predicting pancreatitis severity.

MATERIALS AND METHODS

A prospective, hospital-based, cohort study was conducted, including 249 patients diagnosed with acute pancreatitis via clinical examination. Laboratory investigations and radiological investigations were conducted. The diagnostic accuracy of the inflammatory markers neutrophil/lymphocyte ratio (NLR), lymphocyte/monocyte ratio (LMR), red cell distribution width (RDW), and prognostic nutritional index (PNI) was compared with gold standard prognostic scores, namely, the Acute Physiology and Chronic Health Evaluation II (APACHE II), Simplified Acute Physiology Score II (SAPS II), Bedside Index of Severity in Acute Pancreatitis (BISAP), and Systemic Inflammatory Response Syndrome (SIRS), in predicting primary and secondary outcomes. All values were analyzed using mean and standard deviation (SD). Sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve for mortality prediction were calculated for NLR, LMR, RDW, and PNI.

RESULTS

Of 249 patients with acute pancreatitis (mean age: 39-43 years), 94 were classified as mild acute, 74 as moderately severe acute, and 81 as severe acute. The most common etiology was alcohol use (40.2%), followed by gallstones (29.7%), hypertriglyceridemia (6.4%), steroid use (4%), diabetic ketoacidosis (2.8%), hypercalcemia (2.8%), and complication of endoscopic retrograde cholangiopancreatography (2%). On day 1, mean values of NLR, LMR, RDW, and PNI were 8.23±5.11, 2.63±1.76, 15.93±3.64, and 32.84±8.13, respectively. Compared to APACHE II, SAPS II, BISAP, and SIRS on day 1, day 3, day 7, and day 14, the cutoff values for NLR were 4.06, 10.75, 8.75, and 13.75, respectively. Similarly, on day 1, the cutoff value of LMR was 1.95, and on day 1 and day 3, the cutoff values of RDW were 14.75% and 15%, respectively.

CONCLUSION

The results indicate that inflammatory biomarkers NLR, LMR, RDW, and PNI are comparable with gold standard scoring systems for predicting the severity and mortality of acute pancreatitis. NLR on day 7 was significantly associated with higher severity of illness. NLR on days 3, 7, and 14, LMR on day 1, and RDW on days 1 and 3 were significantly associated with mortality.

Aberrant blood cell division cycle 42 expression and its correlation with disease severity, inflammation and mortality risk in patients with acute pancreatitis

Cell division cycle 42 (CDC42) can inhibit inflammation by regulating the activity of macrophage and T cells, which contributes to the pathophysiology of acute pancreatitis (AP). Therefore, CDC42 may have application as a potential biomarker for AP. The present study aimed to explore this possibility. Peripheral blood mononuclear cells (PBMCs) were collected from 149 patients with AP and 50 healthy controls (HCs). Subsequently, CDC42 expression in the PBMCs was measured using RT-qPCR; C-reactive protein (CRP), TNF-α and IL-6 in the serum of patients with AP were measured using ELISA. Meanwhile, Mann-Whitney U test, Kruskal-Wallis test, and Spearman's rank correlation test were performed on the data. The CDC42 expression levels were lower in patients with AP compared with those in HCs (P<0.001). CDC42 expression was declined in patients with moderate-severe AP (MSAP) vs. patients with mild AP (MAP) (P=0.029), and in patients with severe AP (SAP) vs. patients with MAP (P=0.004). CDC42 expression correlated negatively with the Ranson's score (P<0.001), APACEH II score (P=0.011) and SOFA score (P<0.001) in patients with AP. CDC42 expression also correlated negatively with CRP (P<0.001) and TNF-α (P=0.004) levels but not with IL-6 levels (P=0.177). Furthermore, CDC42 expression was lower in deceased patients with AP vs. AP survivors (P<0.001) and in deceased patients with SAP vs. SAP survivors (P=0.026). CDC42 had good potential in predicting mortality from AP, with AUC of 0.829 and a 95% CI of 0.731-0.927, and it also had certain potential in predicting mortality from SAP and MSAP, with AUC (95% CI) of 0.794 (0.616-0.973) and 0.757 (0.558-0.956), respectively. In conclusion, data from the present study suggest that lower CDC42 expression levels correlate with higher disease susceptibility, disease severity, inflammation, and mortality risk in patients with AP.