The Modified Pancreatitis Activity Scoring System Shows Distinct Trajectories in Acute Pancreatitis: An International Study

Evaluation of Chronic Pancreatitis Prognosis Score in an American Cohort

INTRODUCTION

Chronic Pancreatitis Prognosis Score (COPPS) was developed to discriminate disease severity and predict risk for future hospitalizations. In this cohort study, we evaluated if COPPS predicts the likelihood of hospitalization(s) in an American cohort.

METHODS

The Chronic Pancreatitis, Diabetes, and Pancreatic Cancer consortium provided data and serum from subjects with chronic pancreatitis (N = 279). COPPS was calculated with baseline data and stratified by severity (low, moderate, and high). Primary endpoints included number and duration of hospitalizations during 12-month follow-up.

RESULTS

The mean ± SD COPPS was 8.4 ± 1.6. COPPS correlated with all primary outcomes: hospitalizations for any reason (number: r = 0.15, P = 0.01; duration: r = 0.16, P = 0.01) and pancreas-related hospitalizations (number: r = 0.15, P = 0.02; duration: r = 0.13, P = 0.04). The severity distribution was 13.3% low, 66.0% moderate, and 20.8% high. 37.6% of subjects had ≥1 hospitalization(s) for any reason; 32.2% had ≥1 pancreas-related hospitalizations. All primary outcomes were significantly different between severity groups: hospitalizations for any reason (number, P = 0.004; duration, P = 0.007) and pancreas-related hospitalizations (number, P = 0.02; duration, P = 0.04). The prevalence of continued drinking at follow-up ( P = 0.04) was higher in the low and moderate groups. The prevalence of anxiety at enrollment ( P = 0.02) and follow-up ( P < 0.05) was higher in the moderate and high groups.

DISCUSSION

Statistically, COPPS significantly correlated with hospitalization outcomes, but the correlations were weaker than in previous studies, which may be related to the outpatient nature of the PROspective Evaluation of Chronic Pancreatitis for EpidEmiologic and Translational StuDies cohort and lower prevalence of high severity disease. Studies in other prospective cohorts are needed to understand the full utility of COPPS as a potential tool for clinical risk assessment and intervention.

No Effect of Methylnaltrexone on Acute Pancreatitis Severity: A Multicenter Randomized Controlled Trial

INTRODUCTION

Opioids used to manage severe pain in acute pancreatitis (AP) might exacerbate the disease through effects on gastrointestinal and immune functions. Methylnaltrexone, a peripherally acting µ-opioid receptor antagonist, may counteract these effects without changing analgesia.

METHODS

This double-blind, randomized, placebo-controlled trial included adult patients with AP and systemic inflammatory response syndrome at 4 Danish centers. Patients were randomized to receive 5 days of continuous intravenous methylnaltrexone (0.15 mg/kg/d) or placebo added to the standard of care. The primary end point was the Pancreatitis Activity Scoring System score after 48 hours of treatment. Main secondary outcomes included pain scores, opioid use, disease severity, and mortality.

RESULTS

In total, 105 patients (54% men) were randomized to methylnaltrexone (n = 51) or placebo (n = 54). After 48 hours, the Pancreatitis Activity Scoring System score was 134.3 points in the methylnaltrexone group and 130.5 points in the placebo group (difference 3.8, 95% confidence interval [CI] -40.1 to 47.6; P = 0.87). At 48 hours, we found no differences between the groups in pain severity (0.0, 95% CI -0.8 to 0.9; P = 0.94), pain interference (-0.3, 95% CI -1.4 to 0.8; P = 0.55), and morphine equivalent doses (6.5 mg, 95% CI -2.1 to 15.2; P = 0.14). Methylnaltrexone also did not affect the risk of severe disease (8%, 95% CI -11 to 28; P = 0.38) and mortality (6%, 95% CI -1 to 12; P = 0.11). The medication was well tolerated.

DISCUSSION

Methylnaltrexone treatment did not achieve superiority over placebo for reducing the severity of AP.

Application Value of the Automated Machine Learning Model Based on Modified Computed Tomography Severity Index Combined With Serological Indicators in the Early Prediction of Severe Acute Pancreatitis

BACKGROUND AND AIMS

Machine learning (ML) algorithms are widely applied in building models of medicine due to their powerful studying and generalizing ability. To assess the value of the Modified Computed Tomography Severity Index (MCTSI) combined with serological indicators for early prediction of severe acute pancreatitis (SAP) by automated ML (AutoML).

PATIENTS AND METHODS

The clinical data, of the patients with acute pancreatitis (AP) hospitalized in Hospital 1 and hospital 2 from January 2017 to December 2021, were retrospectively analyzed. Serological indicators within 24 hours of admission were collected. MCTSI score was completed by noncontrast computed tomography within 24 hours of admission. Data from the hospital 1 were adopted for training, and data from the hospital 2 were adopted for external validation. The diagnosis of AP and SAP was based on the 2012 revised Atlanta classification of AP. Models were built using traditional logistic regression and AutoML analysis with 4 types of algorithms. The performance of models was evaluated by the receiver operating characteristic curve, the calibration curve, and the decision curve analysis based on logistic regression and decision curve analysis, feature importance, SHapley Additive exPlanation Plot, and Local Interpretable Model Agnostic Explanation based on AutoML.

RESULTS

A total of 499 patients were used to develop the models in the training data set. An independent data set of 201 patients was used to test the models. The model developed by the Deep Neural Net (DL) outperformed other models with an area under the receiver operating characteristic curve (areas under the curve) of 0.907 in the test set. Furthermore, among these AutoML models, the DL and gradient boosting machine models achieved the highest sensitivity values, both exceeding 0.800.

CONCLUSION

The AutoML model based on the MCTSI score combined with serological indicators has good predictive value for SAP in the early stage.

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.

Predicting the severity of acute pancreatitis: Current approaches and future directions

Acute pancreatitis (AP) is a sudden-onset inflammatory disease of the pancreas. The severity of AP is classified into mild, moderate, and severe categories based on the presence and persistence of organ failure. Severe acute pancreatitis (SAP) can be associated with significant morbidity and mortality. It requires early recognition for appropriate timely management. Prognostic scores for predicting SAP incorporating many clinical, laboratory, and radiological parameters have been developed in the past. However, all of these prognostic scores have low positive predictive value for SAP and some of these scores require >24 h for assessment. There is a need to develop biomarkers that can accurately identify patients at risk for SAP early in the course of the presentation. In this review, we aim to provide a summary of the most commonly utilized prognostic scores for AP and discuss future directions.

Early prediction and prevention of infected pancreatic necrosis

Approximately 20%-30% of patients with acute necrotizing pancreatitis develop infected pancreatic necrosis (IPN), a highly morbid and potentially lethal complication. Early identification of patients at high risk of IPN may facilitate appropriate preventive measures to improve clinical outcomes. In the past two decades, several markers and predictive tools have been proposed and evaluated for this purpose. Conventional biomarkers like C-reactive protein, procalcitonin, lymphocyte count, interleukin-6, and interleukin-8, and newly developed biomarkers like angiopoietin-2 all showed significant association with IPN. On the other hand, scoring systems like the Acute Physiology and Chronic Health Evaluation II and Pancreatitis Activity Scoring System have also been tested, and the results showed that they may provide better accuracy. For early prevention of IPN, several new therapies were tested, including early enteral nutrition, antibiotics, probiotics, immune enhancement, etc., but the results varied. Taken together, several evidence-supported predictive markers and scoring systems are readily available for predicting IPN. However, effective treatments to reduce the incidence of IPN are still lacking apart from early enteral nutrition. In this editorial, we summarize evidence concerning early prediction and prevention of IPN, providing insights into future practice and study design. A more homogeneous patient population with reliable risk-stratification tools may help find effective treatments to reduce the risk of IPN, thereby achieving individualized treatment.

B Cells and Double-Negative B Cells (CD27-IgD-) Are Related to Acute Pancreatitis Severity

Acute pancreatitis (AP) is an increasingly frequent disease in which inflammation plays a crucial role. Fifty patients hospitalized with AP were included and peripheral blood samples were analyzed for B and T cell subpopulations at the time of hospitalization and 48 h after diagnosis. The Bedside Index of Severity in Acute Pancreatitis (BISAP) and length of hospital stay were also recorded. A healthy control (HC) group of 15 outpatients was included. AP patients showed higher neutrophil/lymphocyte (N/L) ratios and higher percentages of B cells than the HC group. The total B cell percentages were higher in patients with moderate/severe AP than in patients with mild AP. The percentages of B cells as well as the percentages of the CD27-IgD- B cell subset decreased from admission to 48 h after admission. The patients with higher BISAP scores showed lower percentages of peripheral lymphocytes but higher percentages of CD27-IgD- B cells. Higher BISAP scores, N/L ratios, and peripheral blood B cell levels emerged as predictors of hospital stay length in AP patients. Our findings underscore the importance of early markers for disease severity. Additionally, the N/L ratio along with the BISAP score and circulating B cell levels form a robust predictive model for hospital stay duration of AP patients.

Value of serum CRP and IL-6 Assays combined with Pancreatitis activity scoring system for assessing the severity of patients with acute pancreatitis

Objective

To evaluate the accuracy of serum CRP and IL-6 assays combined with the pancreatitis activity scoring system (PASS) in assessing the severity of patients with acute pancreatitis (AP).

Methods

This was a retrospective study of 223 patients with AP admitted to Baoding Lianchi District People's Hospital between February 2021 and 2023. They were classified into three categories: mild AP (MAP), moderate severe AP (MSAP) and severe AP (SAP). The differences, accuracy and sensitivity of the individual assays, and the three in combination, were compared and analysed in the three groups.

Results

PASS scores, IL-6 and CRP levels were significantly higher in the SAP and MSAP groups compared to those in the MAP group, with statistically significant differences between the three groups. Multi-factorial logistic regression analysis suggested that PASS, IL-6 and CRP were correlated indicators of AP severity. The combination of the three assays was higher than that of the PASS score, IL-6 and CRP alone, suggesting optimal diagnostic efficacy when the three assays were combined. Moreover, the levels of PASS score, IL-6 and CRP showed a positive correlation with the degree of disease severity.

Conclusions

The serum CRP, IL-6 and PASS scores were significantly elevated in AP patients and showed a positive correlation with disease severity, all of which are beneficial for the diagnosis of AP. PASS is superior to CRP and IL-6 in the assessment of AP. The combination of the three assays can achieve a far superior diagnostic efficacy to that of the individual index assays.

Exploring the value of early laboratory indicators combined with pancreatitis activity scoring system in assessing the severity and prognosis of acute pancreatitis

Objective

To investigate the value of early laboratory indicators combined with the pancreatitis activity scoring system in assessing the severity and prognosis of acute pancreatitis (AP).

Methods

This is a retrospective study. A total of 160 patients with AP admitted to the Affiliated Hospital of Hebei University from February 2021 to February 2023 were enrolled and classified into three categories: mild acute pancreatitis (MAP), moderate severe acute pancreatitis (MSAP), and severe acute pancreatitis (SAP), with 80 cases with MAP and MSAP as the control group and 80 cases with SAP as the experimental group. The differences of inflammatory markers, blood routine, biochemical markers, coagulation markers and PASS score within 24 hours after admission were compared between the two groups, and independent risk factors for predicting AP severity were analyzed. Moreover, the diagnostic efficacy and prognostic value of independent risk factors were evaluated.

Results

The PASS score as well as CRP, PCT, IL-6, WBC, N, AST, DD and PT were higher in the experimental group than in the control group. Logistic regression analysis suggested that PASS, IL-6, PCT and WBC were independent risk factors for predicting severity of AP. In addition, PASS had the highest diagnostic efficacy.

Conclusion

Early elevation of PASS, IL-6, PCT and WBC in patients suffering from AP is of great significance in predicting SAP. PASS score combined with IL-6, PCT and WBC has important value in evaluating the severity and prognosis of AP.

Acute Pancreatitis: Current Clinical Approaches, Molecular Pathophysiology, and Potential Therapeutics

OBJECTIVE

Severe acute pancreatitis (SAP), pancreatic inflammation leading to multiorgan failure, is associated with high morbidity and mortality. There is a critical need to identify novel therapeutic strategies to improve clinical outcomes for SAP patients.

MATERIALS AND METHODS

A comprehensive literature review was performed to identify current clinical strategies, known molecular pathophysiology, and potential therapeutic targets for SAP.

RESULTS

Current clinical approaches focus on determining which patients will likely develop SAP. However, therapeutic options are limited to supportive care and fluid resuscitation. The application of a novel 5-cytokine panel accurately predicting disease outcomes in SAP suggests that molecular approaches will improve impact of future clinical trials in AP.

CONCLUSIONS

Inflammatory outcomes in acute pancreatitis are driven by several unique molecular signals, which compound to promote both local and systemic inflammation. The identification of master cytokine regulators is critical to developing therapeutics, which reduce inflammation through several mechanisms.

Prediction of infected pancreatic necrosis in acute necrotizing pancreatitis by the modified pancreatitis activity scoring system

OBJECTIVES

Infected pancreatic necrosis (IPN) is a significant complication of acute necrotizing pancreatitis (ANP). Early identification of patients at high risk of IPN would enable appropriate treatment, but there is a lack of valid tools. This study aimed to assess the performance of the Pancreatitis Activity Scoring System (PASS) and its modifications (by removing or reducing the weight of opioid usage) in predicting IPN in a cohort of predicted severe ANP patients.

METHODS

Data was prospectively collected in the TRACE trial (2017-2020) involving 16 sites across China. The predictive performance of PASS, modified PASS (mPASS), and conventional indices were assessed by the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow Ĉ-test, Brier score, and Fagan's nomogram. Multivariate logistic regression analysis (MLRA) was used to define the relationship between the best-performing PASS/mPASS model and IPN.

RESULTS

A total of 508 subjects were enrolled (median age, 43 years; 62.8% males) in the original trial, and 122 developed IPN (24%) within 90 days after randomization. Compared with non-IPN patients, the scores of PASS and its modified models were significantly higher in the IPN patients (all p < 0.001). Among the PASS and its modifications, mPASS-4 had the largest AUC, the lowest Brier score, and good calibration. The mPASS-4 model demonstrated an AUC of 0.752 in predicting IPN (the optimal cut-off for the mPASS-4 was 292.5) and outperformed the conventional indices. The MLRA results showed that mPASS-4 >292.5 was an independent risk factor of IPN (OR: 3.6, 95% CI: 2.1-6.3).

CONCLUSION

The PASS and its modifications during the first week of ANP onset predict the development of IPN, with mPASS-4 performing best. The mPASS-4 model simplifies the original PASS, increasing the likelihood of clinical implementation.

Acute Pancreatitis: Diagnosis and Treatment

Acute pancreatitis is a common indication for hospital admission, increasing in incidence, including in children, pregnancy and the elderly. Moderately severe acute pancreatitis with fluid and/or necrotic collections causes substantial morbidity, and severe disease with persistent organ failure causes significant mortality. The diagnosis requires two of upper abdominal pain, amylase/lipase ≥ 3 ×upper limit of normal, and/or cross-sectional imaging findings. Gallstones and ethanol predominate while hypertriglyceridaemia and drugs are notable among many causes. Serum triglycerides, full blood count, renal and liver function tests, glucose, calcium, transabdominal ultrasound, and chest imaging are indicated, with abdominal cross-sectional imaging if there is diagnostic uncertainty. Subsequent imaging is undertaken to detect complications, for example, if C-reactive protein exceeds 150 mg/L, or rarer aetiologies. Pancreatic intracellular calcium overload, mitochondrial impairment, and inflammatory responses are critical in pathogenesis, targeted in current treatment trials, which are crucially important as there is no internationally licenced drug to treat acute pancreatitis and prevent complications. Initial priorities are intravenous fluid resuscitation, analgesia, and enteral nutrition, and when necessary, critical care and organ support, parenteral nutrition, antibiotics, pancreatic exocrine and endocrine replacement therapy; all may have adverse effects. Patients with local complications should be referred to specialist tertiary centres to guide further management, which may include drainage and/or necrosectomy. The impact of acute pancreatitis can be devastating, so prevention or reduction of the risk of recurrence and progression to chronic pancreatitis with an increased risk of pancreas cancer requires proactive management that should be long term for some patients.

Automated Machine Learning for the Early Prediction of the Severity of Acute Pancreatitis in Hospitals

Background

Machine learning (ML) algorithms are widely applied in building models of medicine due to their powerful studying and generalizing ability. This study aims to explore different ML models for early identification of severe acute pancreatitis (SAP) among patients hospitalized for acute pancreatitis.

Methods

This retrospective study enrolled patients with acute pancreatitis (AP) from multiple centers. Data from the First Affiliated Hospital and Changshu No. 1 Hospital of Soochow University were adopted for training and internal validation, and data from the Second Affiliated Hospital of Soochow University were adopted for external validation from January 2017 to December 2021. The diagnosis of AP and SAP was based on the 2012 revised Atlanta classification of acute pancreatitis. Models were built using traditional logistic regression (LR) and automated machine learning (AutoML) analysis with five types of algorithms. The performance of models was evaluated by the receiver operating characteristic (ROC) curve, the calibration curve, and the decision curve analysis (DCA) based on LR and feature importance, SHapley Additive exPlanation (SHAP) Plot, and Local Interpretable Model Agnostic Explanation (LIME) based on AutoML.

Results

A total of 1,012 patients were included in this study to develop the AutoML models in the training/validation dataset. An independent dataset of 212 patients was used to test the models. The model developed by the gradient boost machine (GBM) outperformed other models with an area under the ROC curve (AUC) of 0.937 in the validation set and an AUC of 0.945 in the test set. Furthermore, the GBM model achieved the highest sensitivity value of 0.583 among these AutoML models. The model developed by eXtreme Gradient Boosting (XGBoost) achieved the highest specificity value of 0.980 and the highest accuracy of 0.958 in the test set.

Conclusions

The AutoML model based on the GBM algorithm for early prediction of SAP showed evident clinical practicability.