Blood Urea Nitrogen as a Predictor of Severe Acute Pancreatitis Based on the Revised Atlanta Criteria: Timing of Measurement and Cutoff Points

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.

Blood Urine Nitrogen Trajectories of Acute Pancreatitis Patients in Intensive Care Units

Objective

To identify subclasses of acute pancreatitis (AP) patients in the intensive care unit (ICU) by analyzing blood urea nitrogen (BUN) trajectories.

Methods

AP patients in West China Hospital System (development cohort) and three public databases in the United States (validation cohort) were included. Latent class trajectory modelling was used to identify subclasses based on BUN trajectories within the first 21 days after ICU admission. Clinical characteristics and outcomes were compared, and results were externally validated.

Results

The study comprised 2971 and 930 patients in the development and validation cohorts, respectively, with five subclasses: Class 1 ("Moderate-azotemia, slow decreasing"), Class 2 ("Non-azotemia"), Class 3 ("Severe-azotemia, slow decreasing"), Class 4 ("Moderate-azotemia, rapid increasing"), and Class 5 ('Moderate-azotemia, slow increasing) identified. Azotemia patients showed significantly higher 30-day mortality risk in development and validation cohorts. Specifically, Class 4 patients exhibited notably highest mortality risk in both the development cohort (HR 5.32, 95% CI 2.62-10.82) and validation cohort (HR 6.23, 95% CI 2.93-13.22). Regarding clinical characteristics, AP patients in Class 4 showed lower mean arterial pressure and a higher proportion of renal disease. We also created an online early classification model to further identify Class 4 patients among all patients with moderate azotemia at baseline.

Conclusion

This multinational study uncovers heterogeneity in BUN trajectories among AP patients. Patients with "Moderate-azotemia, rapid increasing" trajectory, had a higher mortality risk than patients with severe azotemia at baseline. This finding complements studies that solely rely on baseline BUN for risk stratification and enhanced our understanding of longitudinal progression of AP.

Automated machine learning for early prediction of acute kidney injury in acute pancreatitis

BACKGROUND

Acute kidney injury (AKI) represents a frequent and grave complication associated with acute pancreatitis (AP), substantially elevating both mortality rates and the financial burden of hospitalization. The aim of our study is to construct a predictive model utilizing automated machine learning (AutoML) algorithms for the early prediction of AKI in patients with AP.

METHODS

We retrospectively analyzed patients who were diagnosed with AP in our hospital from January 2017 to December 2021. These patients were randomly allocated into a training set and a validation set at a ratio of 7:3. To develop predictive models for each set, we employed the least absolute shrinkage and selection operator (LASSO) algorithm along with AutoML. A nomogram was developed based on multivariate logistic regression analysis outcomes. The model's efficacy was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Additionally, the performance of the model constructed via AutoML was evaluated using decision curve analysis (DCA), feature importance, SHapley Additive exPlanations (SHAP) plots, and locally interpretable model-agnostic explanations (LIME).

RESULTS

This study incorporated a total of 437 patients who met the inclusion criteria. Out of these, 313 were assigned to the training cohort and 124 to the validation cohort. In the training and validation cohorts, AKI occurred in 68 (21.7%) and 29(23.4%) patients, respectively. Comparative analysis revealed that the AutoML models exhibited enhanced performance over traditional logistic regression (LR). Furthermore, the deep learning (DL) model demonstrated superior predictive accuracy, evidenced by an area under the ROC curve of 0.963 in the training set and 0.830 in the validation set, surpassing other comparative models. The key variables identified as significant in the DL model within the training dataset included creatinine (Cr), urea (Urea), international normalized ratio (INR), etiology, smoking, alanine aminotransferase (ALT), hypertension, prothrombin time (PT), lactate dehydrogenase (LDH), and diabetes.

CONCLUSION

The AutoML model, utilizing DL algorithm, offers considerable clinical significance in the early detection of AKI among patients with AP.

Fabp5 is a common gene between a high-cholesterol diet and acute pancreatitis

Background and aims

Hypercholesterolemia has been identified as risk factor for severe acute pancreatitis (AP). We aimed to identify the common differentially expressed genes (DEGs) between a high-cholesterol diet and AP.

Methods

We retrived gene expression profiles from the GEO database. DEGs were assessed using GEO2R. For AP hub genes, we conducted functional enrichment analysis and protein-protein interaction (PPI) analysis. GeneMANIA and correlation analysis were employed to predict potential DEG mechanisms. Validation was done across various healthy human tissues, pancreatic adenocarcinoma, peripheral blood in AP patients, and Sprague-Dawley rats with AP.

Results

The gene "Fabp5" emerged as the sole common DEG shared by a high-cholesterol diet and AP. Using the 12 topological analysis methods in PPI network analysis, Rela, Actb, Cdh1, and Vcl were identified as hub DEGs. GeneMANIA revealed 77.6% physical interactions among Fabp5, TLR4, and Rela, while genetic correlation analysis indicated moderate associations among them. Peripheral blood analysis yielded area under the ROC curve (AUC) values of 0.71, 0.63, 0.74, 0.64, and 0.91 for Fabp5, TLR4, Actb, Cdh1 genes, and artificial neural network (ANN) model respectively, in predicting severe AP. In vivo immunohistochemical analysis demonstrated higher Fabp5 expression in the hyperlipidemia-associated AP group compared to the AP and control groups.

Conclusion

Fabp5 emerged as the common DEG connecting a high-cholesterol diet and AP. Rela was highlighted as a crucial hub gene in AP. Genetic interactions were observed among Fabp5, TLR4, and Rela. An ANN model consisting of Fabp5, TLR4, Actb, and Cdh1 was helpful in predicting severe AP.

Establishment and validation of early prediction model for hypertriglyceridemic severe acute pancreatitis

BACKGROUND

The prevalence of hypertriglyceridaemia-induced acute pancreatitis (HTG-AP) is increasing due to improvements in living standards and dietary changes. However, currently, there is no clinical multifactor scoring system specific to HTG-AP. This study aimed to screen the predictors of HTG-SAP and combine several indicators to establish and validate a visual model for the early prediction of HTG-SAP.

METHODS

The clinical data of 266 patients with HTG-SAP were analysed. Patients were classified into severe (N = 42) and non-severe (N = 224) groups according to the Atlanta classification criteria. Several statistical analyses, including one-way analysis, least absolute shrinkage with selection operator (LASSO) regression model, and binary logistic regression analysis, were used to evaluate the data.

RESULTS

The univariate analysis showed that several factors showed no statistically significant differences, including the number of episodes of pancreatitis, abdominal pain score, and several blood diagnostic markers, such as lactate dehydrogenase (LDH), serum calcium (Ca2+), C-reactive protein (CRP), and the incidence of pleural effusion, between the two groups (P < 0.000). LASSO regression analysis identified six candidate predictors: CRP, LDH, Ca2+, procalcitonin (PCT), ascites, and Balthazar computed tomography grade. Binary logistic regression multivariate analysis showed that CRP, LDH, Ca2+, and ascites were independent predictors of HTG-SAP, and the area under the curve (AUC) values were 0.886, 0.893, 0.872, and 0.850, respectively. The AUC of the newly established HTG-SAP model was 0.960 (95% confidence interval: 0.936-0.983), which was higher than that of the bedside index for severity in acute pancreatitis (BISAP) score, modified CT severity index, Ranson score, and Japanese severity score (JSS) CT grade (AUC: 0.794, 0.796, 0.894 and 0.764, respectively). The differences were significant (P < 0.01), except for the JSS prognostic indicators (P = 0.130). The Hosmer-Lemeshow test showed that the predictive results of the model were highly consistent with the actual situation (P > 0.05). The decision curve analysis plot suggested that clinical intervention can benefit patients when the model predicts that they are at risk for developing HTG-SAP.

CONCLUSIONS

CRP, LDH, Ca2+, and ascites are independent predictors of HTG-SAP. The prediction model constructed based on these indicators has a high accuracy, sensitivity, consistency, and practicability in predicting HTG-SAP.

Predictive Risk Factors of Pancreatic Exocrine Insufficiency Developed After Acute Pancreatitis: A Retrospective Cohort Study

Purpose

The aim of this study was to compare the clinical characteristics of acute pancreatitis (AP) patients between those who developed pancreatic exocrine insufficiency (PEI) and those who did not, and to investigate the predictive factors of PEI.

Patients and Methods

From October 1st 2019 to July 30th 2021, AP patients admitted at our center were included. The fecal elastase-1 assay was adopted for PEI diagnosis. The clinical characteristics, treatments, and outcomes between the patients with and without PEI were analyzed.

Results

In total, 63 males and 42 females were included. There were 27 patients with mild AP, 54 with moderately severe AP, and 24 with severe AP. The median modified computed tomography severity index (MCTSI) was 6.000(4.000, 8.000). During the follow-up, 38 patients developed PEI after AP. The univariate analysis showed that higher ASA grade (P = 0.006), more severe AP (P = 0.000), the presence of multiple organ dysfunction syndrome (P = 0.030), higher MCTSI (P = 0.000), the development of infected pancreatic necrosis (P = 0.002) and local complications (P = 0.000), higher levels of triacylglycerol (P = 0.022), video-assisted retroperitoneal debridement intervention (P = 0.015), and longer intensive care unit stay (P = 0.044) were correlated with PEI development. Furthermore, the logistic regression analyses showed that MCTSI during hospitalization is an independent risk factor for PEI development during the AP recovery period.

Conclusion

ASA grade, severity of AP, multiple organ dysfunction syndrome, MCTSI, infected pancreatic necrosis, local complications, higher levels of triacylglycerol, video-assisted retroperitoneal debridement intervention, and longer intensive care unit stay were potentially associated with PEI development during the AP recovery period. High MCTSI was independently associated with the development of PEI during the AP recovery period, which may help alert to the possibility of PEI to help with its early detection and treatment.

Medical imaging for pancreatic diseases: Prediction of severe acute pancreatitis complicated with acute respiratory distress syndrome

In this editorial we comment on the article published in the recent issue of the World Journal of Gastroenterology [2022; 28 (19): 2123-2136]. We pay attention to how to construct a simpler and more reliable new clinical predictive model to early identify patients at high risk of acute respiratory distress syndrome (ARDS) associated with severe acute pancreatitis (SAP), and to early predict the severity of organ failure from chest computed tomography (CT) findings in SAP patients. As we all know, SAP has a sudden onset, is a rapidly changing condition, and can be complicated with ARDS and even multiple organ dysfunction syndrome, and its mortality rate has remained high. At present, there are many clinical scoring systems for AP, including the bedside index for severity in AP, acute physiology and chronic health evaluation II, systemic inflammatory response syndrome, Japanese severe score, quick sepsis-related organ failure assessment, etc. However, some of these scoring systems are complex and require multiple and difficult clinical parameters for risk stratification. Although the aforementioned biomarkers are readily available, their ability to predict ARDS varies. Accor-dingly, it is extremely necessary to establish a simple and valuable novel model to predict the development of ARDS in AP. In addition, the extra-pancreatic manifestations of AP patients often involve the chest, among which pleural effusion and pulmonary consolidation are the more common complications. Therefore, by measuring the semi-quantitative indexes of chest CT in AP patients, such as the amount of pleural effusion and the number of lobes involved as pulmonary consolidation, it has important reference value for the early diagnosis of SAP complicated with ARDS and is expected to provide a basis for the early treatment of ARDS.

Impact of multiple drug-resistant Gram-negative bacterial bacteraemia on infected pancreatic necrosis patients

Introduction

Multiple drug-resistant Gram-negative bacterial (MDR-GNB) bacteraemia poses a serious threat to patients in hospital. Infected pancreatic necrosis (IPN) patients are a vulnerable population to infectious complications during hospitalization. This study aims to evaluate the impact of MDR Gram-negative bacteraemia on IPN patients.

Methods

A case-control study was performed with data collected from 1 January 2016 to 1 July 2022 in a Chinese tertiary teaching hospital. Clinical data of the IPN patients with MDR-GNB bacteraemia were analyzed and compared to those of a matched control group without MDR-GNB bacteraemia (case-control ratio of 1:2). Comparisons were performed between with/without MDR-GNB bacteraemia and different severities of acute pancreatitis (AP). Independent predictors of overall mortality were identified via univariate and multivariate binary logistic regression analyses.

Results

MDR-GNB bacteraemia was related to a higher mortality rate (62.5% vs. 8.3%, p < 0.001). Severe AP combined with MDR-GNB bacteraemia further increased mortality up to 81.3% (p = 0.025). MDR-GNB bacteraemia (odds ratio (OR) = 8.976, 95% confidence interval (CI) = 1.805 -44.620, p = 0.007) and severe AP (OR = 9.414, 95% CI = 1.742 -50.873, p = 0.009) were independent predictors of overall mortality. MDR- Klebsiella pneumoniae was the most common causative pathogen.

Conclusion

A higher mortality rate in IPN patients was related to MDR-GNB bacteraemia and further increased in severe AP patients combined with MDR-GNB bacteraemia.

Clinical utility of the pancreatitis activity scoring system in severe acute pancreatitis

Objective: To analyze clinical utility of pancreatitis activity scoring system (PASS) in prediction of persistent organ failure, poor prognosis, and in-hospital mortality in patients with moderately severe acute pancreatitis (MSAP) or severe acute pancreatitis (SAP) admitted to the intensive care unit (ICU).

Methods: The study included a total of 140 patients with MSAP and SAP admitted to the ICU of Shandong Provincial Hospital from 2015 to 2021. The general information, biochemical indexes and PASS scores of patients at ICU admission time were collected. Independent risk factors of persistent organ failure, poor prognosis and in-hospital mortality were analyzed by binary logistic regression. Through receiver operating characteristic curve (ROC), the predictive ability of lactic acid, procalcitonin, urea nitrogen, PASS, and PASS in combination with urea nitrogen for the three outcomes was compared. The best cut-off value was determined.

Results: Binary logistic regression showed that PASS might be an independent risk factor for patients with persistent organ failure (odds ratio [OR]: 1.027, 95% confidence interval [CI]: 1.014–1.039), poor prognosis (OR: 1.008, 95% CI: 1.001–1.014), and in-hospital mortality (OR: 1.009, 95% CI: 1.000–1.019). PASS also had a good predictive ability for persistent organ failure (area under the curve (AUC) = 0.839, 95% CI: 0.769–0.910) and in-hospital mortality (AUC = 0.780, 95% CI: 0.669–0.891), which was significantly superior to lactic acid, procalcitonin, urea nitrogen and Ranson score. PASS (AUC = 0.756, 95% CI: 0.675–0.837) was second only to urea nitrogen (AUC = 0.768, 95% CI: 0.686–0.850) in the prediction of poor prognosis. Furthermore, the predictive power of urea nitrogen in combination with PASS was better than that of each factor for persistent organ failure (AUC = 0.849, 95% CI: 0.779–0.920), poor prognosis (AUC = 0.801, 95% CI: 0.726–0.876), and in-hospital mortality (AUC = 0.796, 95% CI: 0.697–0.894).

Conclusion: PASS was closely correlated with the prognosis of patients with MSAP and SAP. This scoring system may be used as a common clinical index to measure the activity of acute pancreatitis and evaluate disease prognosis.

Development and external validation of models to predict acute respiratory distress syndrome related to severe acute pancreatitis

BACKGROUND

Acute respiratory distress syndrome (ARDS) is a major cause of death in patients with severe acute pancreatitis (SAP). Although a series of prediction models have been developed for early identification of such patients, the majority are complicated or lack validation. A simpler and more credible model is required for clinical practice.

AIM

To develop and validate a predictive model for SAP related ARDS.

METHODS

Patients diagnosed with AP from four hospitals located at different regions of China were retrospectively grouped into derivation and validation cohorts. Statistically significant variables were identified using the least absolute shrinkage and selection operator regression method. Predictive models with nomograms were further built using multiple logistic regression analysis with these picked predictors. The discriminatory power of new models was compared with some common models. The performance of calibration ability and clinical utility of the predictive models were evaluated.

RESULTS

Out of 597 patients with AP, 139 were diagnosed with SAP (80 in derivation cohort and 59 in validation cohort) and 99 with ARDS (62 in derivation cohort and 37 in validation cohort). Four identical variables were identified as independent risk factors for both SAP and ARDS: heart rate [odds ratio (OR) = 1.05; 95%CI: 1.04-1.07; P < 0.001; OR = 1.05, 95%CI: 1.03-1.07, P < 0.001], respiratory rate (OR = 1.08, 95%CI: 1.0-1.17, P = 0.047; OR = 1.10, 95%CI: 1.02-1.19, P = 0.014), serum calcium concentration (OR = 0.26, 95%CI: 0.09-0.73, P = 0.011; OR = 0.17, 95%CI: 0.06-0.48, P = 0.001) and blood urea nitrogen (OR = 1.15, 95%CI: 1.09-1.23, P < 0.001; OR = 1.12, 95%CI: 1.05-1.19, P < 0.001). The area under receiver operating characteristic curve was 0.879 (95%CI: 0.830-0.928) and 0.898 (95%CI: 0.848-0.949) for SAP prediction in derivation and validation cohorts, respectively. This value was 0.892 (95%CI: 0.843-0.941) and 0.833 (95%CI: 0.754-0.912) for ARDS prediction, respectively. The discriminatory power of our models was improved compared with that of other widely used models and the calibration ability and clinical utility of the prediction models performed adequately.

CONCLUSION

The present study constructed and validated a simple and accurate predictive model for SAP-related ARDS in patients with AP.

Blood Urea Nitrogen as a Prognostic Marker in Severe Acute Pancreatitis

Objectives

To explore independent risk factors with good and early predictive power for SAP severity and prognosis.

Methods

Patients with SAP were enrolled at Central South University Xiangya Hospital between April 2017 and May 2021 and used as the training cohort. From June 2021 to February 2022, all patients with SAP were defined as external patients for validation. Patients were grouped by survival status at a 30-day posthospital admission and then compared in terms of basic information and laboratory tests to screen the independent risk factors.

Results

A total of 249 patients with SAP were enrolled in the training cohort. The all-cause mortality rate at a 30-day postadmission was 25.8% (51/198). Blood urea nitrogen (BUN) levels were significantly higher in the mortality group (20.45 [interquartile range (IQR), 19.7] mmol/L) than in the survival group (6.685 [IQR, 6.3] mmol/L; P < 0.001). After propensity score matching (PSM), the BUN level was still higher in the mortality group than in the survival group (18.415 [IQR, 19.555] mmol/L vs. 10.63 [IQR, 6.03] mmol/L; P = 0.005). The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of BUN was 0.820 (95% confidence interval, 0.721–0.870; P < 0.001). The optimal BUN level cut-off for predicting a 30-day all-cause mortality was 10.745 mmol/L. Moreover, patients with SAP were grouped according to BUN levels and stratified according to optimal cut-off value. Patients with high BNU levels were associated with significantly higher rates of invasive mechanical ventilation (before PSM: 61.8% vs. 20.6%, P < 0.001; after PSM: 71.1% vs. 32%, P = 0.048) and a 30-day all-cause mortality (before PSM: 44.9% vs. 6.9%, P < 0.001; after PSM: 60% vs. 34.5%, P = 0.032) than those with low BNU levels before or after PSM. The effectiveness of BUN as a prognostic marker was further validated using ROC curves for the external validation set (n = 49). The AUC of BUN was 0.803 (95% CI, 0.655–0.950; P = 0.011). It showed a good ability to predict a 30-day all-cause mortality in patients with SAP. We also observed similar results regarding disease severity, including the Acute Physiology and Chronic Health Evaluation II score (before PSM: 16 [IQR, 8] vs. 8 [IQR, 6], P < 0.001; after PSM: 18 [IQR, 10] vs. 12 [IQR, 7], P < 0.001), SOFA score (before PSM: 7 [IQR, 5] vs. 3 [IQR, 3], P < 0.001; after PSM: 8 [IQR, 5] vs. 5 [IQR, 3.5], P < 0.001), and mMarshall score (before PSM: 4 [IQR, 3] vs. 3 [IQR, 1], P < 0.001; after PSM: 5 [IQR, 2.5] vs. 3 [IQR, 1], P < 0.001). There was significant increase in intensive care unit occupancy in the high BUN level group before PSM (93.3% vs. 73.1%, P < 0.001), but not after PSM (97.8% vs. 86.2%, P = 0.074).

Conclusions

Our results showed that BUN levels within 24 h after hospital admission were independent risk factors for a 30-day all-cause death in patients with SAP.

Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis

BACKGROUND AND AIMS

This study aimed to develop an interpretable random forest model for predicting severe acute pancreatitis (SAP).

METHODS

Clinical and laboratory data of 648 patients with acute pancreatitis were retrospectively reviewed and randomly assigned to the training set and test set in a 3:1 ratio. Univariate analysis was used to select candidate predictors for the SAP. Random forest (RF) and logistic regression (LR) models were developed on the training sample. The prediction models were then applied to the test sample. The performance of the risk models was measured by calculating the area under the receiver operating characteristic (ROC) curves (AUC) and area under precision recall curve. We provide visualized interpretation by using local interpretable model-agnostic explanations (LIME).

RESULTS

The LR model was developed to predict SAP as the following function: -1.10-0.13×albumin (g/L) + 0.016 × serum creatinine (μmol/L) + 0.14 × glucose (mmol/L) + 1.63 × pleural effusion (0/1)(No/Yes). The coefficients of this formula were utilized to build a nomogram. The RF model consists of 16 variables identified by univariate analysis. It was developed and validated by a tenfold cross-validation on the training sample. Variables importance analysis suggested that blood urea nitrogen, serum creatinine, albumin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, calcium, and glucose were the most important seven predictors of SAP. The AUCs of RF model in tenfold cross-validation of the training set and the test set was 0.89 and 0.96, respectively. Both the area under precision recall curve and the diagnostic accuracy of the RF model were higher than that of both the LR model and the BISAP score. LIME plots were used to explain individualized prediction of the RF model.

CONCLUSIONS

An interpretable RF model exhibited the highest discriminatory performance in predicting SAP. Interpretation with LIME plots could be useful for individualized prediction in a clinical setting. A nomogram consisting of albumin, serum creatinine, glucose, and pleural effusion was useful for prediction of SAP.

Dynamic Nomogram for Predicting Thrombocytopenia in Adults with Acute Pancreatitis

Objective

Thrombocytopenia increases the risk of hemorrhage in patients with acute pancreatitis (AP), leading to poor clinical outcomes. Currently, there is no reliable tool for the early assessment of thrombocytopenia in these patients. We aimed to develop a nomogram based on available clinical parameters and validate its efficacy in predicting thrombocytopenia.

Methods

This was a retrospective study. All the data were extracted from an electronic database from May 2018 to May 2019. Patients with a diagnosis of AP and staying in the intensive care unit for more than 3 days were retrospectively analyzed. A clinical signature was built based on reproducible features, using the least absolute shrinkage and selection operator method (LASSO), and logistic regression established the model (P < 0.05). Nomogram performance was determined by its discrimination, calibration, and clinical usefulness.

Results

A total of 594 eligible patients were enrolled, of whom 399 were allocated to the training sets and the 195 in the test sets. The clinical features, including blood urea nitrogen (BUN), fibrinogen (FIB), and antithrombase III, were significantly associated with the incidence of thrombocytopenia after acute pancreatitis (p < 0.05) in training sets. The individualized nomogram showed good discrimination in the training sample (area under the receiver operating characteristic curve [AUC], 0.881) and in the validation sample (AUC, 0.883) with good calibration.

Conclusion

The proposed nomogram has good performance for predicting thrombocytopenia in patients with acute pancreatitis and may facilitate clinical decision-making.

Accurate prediction of acute pancreatitis severity based on genome-wide cell free DNA methylation profiles

Background

Patients with severe acute pancreatitis (SAP) have a high mortality, thus early diagnosis and interventions are critical for improving survival. However, conventional tests are limited in acute pancreatitis (AP) stratification. We aimed to assess AP severity by integrating the informative clinical measurements with cell free DNA (cfDNA) methylation markers.

Methods

One hundred and seventy-five blood samples were collected from 61 AP patients at multiple time points, plus 24 samples from healthy individuals. Genome-wide cfDNA methylation profiles of all samples were characterized with reduced representative bisulfite sequencing. Clinical blood tests covering 93 biomarkers were performed on AP patients within 24 h. SAP predication models were built based on cfDNA methylation and conventional blood biomarkers separately and in combination.

Results

We identified 565 and 59 cfDNA methylation markers informative for acute pancreatitis and its severity. These markers were used to develop prediction models for AP and SAP with area under the receiver operating characteristic of 0.92 and 0.81, respectively. Twelve blood biomarkers were systematically screened for a predictor of SAP with a sensitivity of 87.5% for SAP, and a specificity of 100% in mild acute pancreatitis, significantly higher than existing blood tests. An expanded model integrating 12 conventional blood biomarkers with 59 cfDNA methylation markers further improved the SAP prediction sensitivity to 92.2%.

Conclusions

These findings have demonstrated that accurate prediction of SAP by the integration of conventional and novel blood molecular markers, paving the way for early and effective SAP intervention through a non-invasive rapid diagnostic test.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01217-z.

Prevalence and impact of acute kidney injury in hospitalized pediatric patients with acute pancreatitis

BACKGROUND

The incidence of acute pancreatitis (AP) in pediatric patients is rising with accompanying increased hospitalizations. Acute kidney injury (AKI) is associated with worse clinical outcomes in adults, and similar data in the pediatric age group is limited.

METHODS

We analyzed non-overlapping years of National Inpatient Sample (NIS) and Kids Inpatient Database (KID) to include all patients less than 21 years old with primary diagnosis of AP between 2003 and 2016. Patients with concomitant diagnosis of AKI were compared with patients without AKI for demographics, comorbid/etiologic conditions, procedures, complications, and mortality. Length of stay and inflation-adjusted hospitalization charges were used to compare health care resource utilization.

RESULTS

In total, 123,185 AP-related hospitalizations were analyzed. Overall prevalence of AKI among AP patients was 1.5% during the study period. The prevalence rate of AKI increased almost five-fold from 0.6% (2003) to a peak rate of 2.9% (2016), P < 0.001. Patients with AKI were older, more often male and had either more systemic diseases or chronic comorbid conditions such as malignancies, systemic lupus erythematosus, solid organ transplantation, hypertriglyceridemia, and hypercalcemia. Multivariate analysis demonstrated AP-related hospitalizations with AKI were 1.97 (CI 1.27-3.08, P < 0.001) times more likely to be associated with in-hospital mortality and contributed to 4.3 additional days of hospitalization (CI 4.02-4.6, P < 0.001), also incurring an additional $51,830 (CI 48571-55088, P < 0.001) in hospital charges.

CONCLUSION

The prevalence of AKI is increasing steadily among pediatric patients with AP and is associated with increased risk of mortality and higher health care resource expenditure.

Acute necrotising pancreatitis: measurements of necrosis volume and mean CT attenuation help early prediction of organ failure and need for intervention

OBJECTIVES

This study explored the early predictive value of volume and mean CT density of necrosis for adverse outcomes in patients with acute necrotising pancreatitis (ANP).

METHODS

A total of 155 patients with ANP who underwent CECT within 7 days of symptom onset were included. The necrosis volume, mean CT density, and modified CT severity index (mCTSI) were calculated. C-reactive protein (CRP) and blood urea nitrogen (BUN) levels both 48 h after symptom onset were reviewed. Adverse outcomes were recorded. The predictive value of each indicator was assessed using ROC curve analysis.

RESULTS

There were significant associations between necrosis volume and mean CT density and organ failure (OF), persistent OF (POF), and need for intervention (p < 0.001 for all). For predicting OF, the area under the curve (AUC) was significantly higher for necrosis volume than for mCTSI and BUN (AUC: 0.84 vs 0.67, p = 0.0011; 0.84 vs 0.71, p = 0.0193, respectively). For predicting POF and need for intervention, the AUCs for necrosis volume were significantly higher than those for mCTSI (AUC: 0.79 vs 0.66, p = 0.0045; 0.77 vs 0.61, p = 0.0019, respectively), but did not significantly differ from those for CRP and BUN. For predicting OF, a significantly better predictive value was achieved with mean CT density than with mCTSI (AUC: 0.79 vs 0.67, p = 0.0163). There were no significant differences in predictive value between mean CT density, CRP, and BUN.

CONCLUSIONS

The volume and mean CT density of necrosis based on CECT can provide early prediction of OF, POF, and need for intervention.

KEY POINTS

• Compared to mCTSI, necrosis volume might be used to more accurately diagnose organ failure and persistent organ failure and might be better associated with the need for intervention. • Necrosis volume and mean CT density based on CECT are reliable quantitative predictors for organ failure, persistent organ failure, and intervention in acute pancreatitis.

Early Changes in Blood Urea Nitrogen (BUN) Can Predict Mortality in Acute Pancreatitis: Comparative Study between BISAP Score, APACHE-II, and Other Laboratory Markers-A Prospective Observational Study

BACKGROUND

Changes in BUN have been proposed as a risk factor for complications in acute pancreatitis (AP). Our study aimed to compare changes in BUN versus the Bedside Index for Severity in Acute Pancreatitis (BISAP) score and the Acute Physiology and Chronic Health Evaluation-II score (APACHE-II), as well as other laboratory tests such as haematocrit and its variations over 24 h and C-reactive protein, in order to determine the most accurate test for predicting mortality and severity outcomes in AP.

METHODS

Clinical data of 410 AP patients, prospectively enrolled for study at our institution, were analyzed. We define AP according to Atlanta classification (AC) 2012. The laboratory test's predictive accuracy was measured using area-under-the-curve receiver-operating characteristics (AUC) analysis and sensitivity and specificity tests.

RESULTS

Rise in BUN was the only score related to mortality on the multivariate analysis (p=0.000, OR: 12.7; CI 95%: 4.2-16.6). On the comparative analysis of AUC, the rise in BUN was an accurate test in predicting mortality (AUC: 0.842) and persisting multiorgan failure (AUC: 0.828), similar to the BISAP score (AUC: 0.836 and 0.850) and APACHE-II (AUC: 0.756 and 0.741). The BISAP score outperformed both APACHE-II and rise in BUN at 24 hours in predicting severe AP (AUC: 0.873 vs. 0.761 and 0.756, respectively).

CONCLUSION

Rise in BUN at 24 hours is a quick and reliable test in predicting mortality and persisting multiorgan failure in AP patients.

Who's at Risk? A Prognostic Model for Severity Prediction in Pediatric Acute Pancreatitis

OBJECTIVES

The aim of the study was to validate and optimize a severity prediction model for acute pancreatitis (AP) and to examine blood urea nitrogen (BUN) level changes from admission as a severity predictor.

STUDY DESIGN

Patients from 2 hospitals were included for the validation model (Children's Hospital of the King's Daughters and Children's National Hospital). Children's Hospital of the King's Daughters and Cincinnati Children's Hospital Medical Center data were used for analysis of BUN at 24 to 48 hours.

RESULTS

The validation cohort included 73 patients; 22 (30%) with either severe or moderately severe AP, combined into the all severe AP (SAP) group. Patients with SAP had higher BUN (P = 0.002) and lower albumin (P = 0.005). Admission BUN was confirmed as a significant predictor (P = 0.005) of SAP (area under the receiver operating characteristic [AUROC] 0.73, 95% confidence interval [CI] 0.60-0.86). Combining BUN (P = 0.005) and albumin (P = 0.004) resulted in better prediction for SAP (AUROC 0.83, 95% CI 0.72-0.94). A total of 176 AP patients were analyzed at 24-48 hours; 39 (22%) met criteria for SAP. Patients who developed SAP had a significantly higher BUN (P < 0.001) after 24 hours. Elevated BUN levels within 24 to 48 hours were independently predictive of developing SAP (AUROC: 0.76, 95% CI: 0.66-0.85). Patients who developed SAP had a significantly smaller percentage decrease in BUN from admission to 24 to 48 hours (P = 0.002).

CONCLUSION

We externally validated the prior model with admission BUN levels and further optimized it by incorporating albumin. We also found that persistent elevation of BUN is associated with development of SAP. Our model can be used to risk stratify patients with AP on admission and again at 24 to 48 hours.

The Synergy of Thermally Reduced Graphene Oxide in Amperometric Urea Biosensor: Application for Medical Technologies

Thermally reduced graphene oxide (TRGO) is a graphene-based nanomaterial that has been identified as promising for the development of amperometric biosensors. Urease, in combination with TRGO, allowed us to create a mediator-free amperometric biosensor with the intention of precise detection of urea in clinical trials. Beyond simplicity of the technology, the biosensor exhibited high sensitivity (2.3 ± 0.1 µA cm⁻² mM⁻¹), great operational and storage stabilities (up to seven months), and appropriate reproducibility (relative standard deviation (RSD) about 2%). The analytical recovery of the TRGO-based biosensor in urine of 101 ÷ 104% with RSD of 1.2 ÷ 1.7% and in blood of 92.7 ÷ 96.4%, RSD of 1.0 ÷ 2.5%, confirmed that the biosensor is acceptable and reliable. These properties allowed us to apply the biosensor in the monitoring of urea levels in samples of urine, blood, and spent dialysate collected during hemodialysis. Accuracy of the biosensor was validated by good correlation (R = 0.9898 and R = 0.9982) for dialysate and blood, utilizing approved methods. The advantages of the proposed biosensing technology could benefit the development of point-of-care and non-invasive medical instruments.

Impact of Clinical, Laboratory and Fluid Therapy Variables on Hospital Length of Stay for Children with Acute Pancreatitis

PURPOSE

There have been many efforts to develop generalizable severity markers in children with acute pancreatitis (AP). Expert opinion panels have developed consensus guidelines on management but it is unclear if these are sufficient or valid. Our study aims to assess the effect of clinical and laboratory variables, in addition to treatment modality on hospital length of stay (LOS) as a proxy variable for severity in pediatric patients admitted with AP.

METHODS

We conducted a retrospective chart review of patients between ages of 0-18 years, who were admitted with AP at 2 institutions between 2013-2018, John R. Oishei Children's Hospital (Buffalo, NY, USA) and Medical University of South Carolina Children's Hospital (Charleston, SC, USA). We constructed three linear regression models to analyze the effect of clinical signs of organ dysfunction, laboratory markers and fluid intake on hospital LOS.

RESULTS

Ninety-two patients were included in the study. The mean age was 12 years (range, 7.6-17.4 years), 55% were females, and median LOS was 3 days. The most frequent cause of AP was idiopathic. Our study showed that elevated blood urea nitrogen (BUN) on admission (p<0.005), tachycardia that lasted for ≥48 hours (p<0.001) and need for fluid resuscitation were associated with increase LOS. Total daily fluid intake above maintenance did not have a significant effect on the primary outcome (p=0.49).

CONCLUSION

Elevated serum BUN on admission, persistent tachycardia and need for fluid resuscitation were associated with increase LOS in pediatric AP. Daily total fluid intake above recommended maintenance did not reduce LOS.

Is aggressive intravenous fluid resuscitation beneficial in acute pancreatitis? A meta-analysis of randomized control trials and cohort studies

BACKGROUND

There is conflincting evidence on the intravenous fluid (IVF) strategy for acute pancreatitis (AP). We perform a metaanalysis of the available evidence.

AIM

To investigate if aggressive IVF therapy in AP patients is beneficial to decrease mortality and improve outcomes.

METHODS

Metaanalysis of available randomized controlled trials and cohort studies comparing aggressive IVF vs non-aggressive IVF resuscitation.

RESULTS

There was no significant difference in mortality between the aggressive (n = 1229) and non-aggressive IVF (n = 1397) patients. Patients receiving aggressive IVF therapy had higher risk for acute kidney injury and acute respiratory distress syndrome. There also was no significant difference in the overall incidence of systemic inflammatory response syndrome, persistent organ failure, pancreatic necrosis when comparing both study groups.

CONCLUSION

Early aggressive IVF therapy did not improve mortality. Moreover, aggressive IVF therapy could potentially increase the risk for acute kidney injury and pulmonary edema leading to respiratory failure and mechanical ventilation. Studies are needed to investigate which subset of AP patients could benefit from aggressive IVF therapy.

Severity stratification and prognostic prediction of patients with acute pancreatitis at early phase: A retrospective study

Severity stratification and prognostic prediction at early stage is crucial for reducing the rates of mortality of patients with acute pancreatitis (AP). We aim to investigate the predicting performance of neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and red-cell distribution width (RDW) combined with severity scores (sequential organ failure assessment [SOFA], bed-side index for severity of AP [BISAP], Ranson criteria, and acute physiology and chronic health evaluation II [APACHE II]) for severe AP (SAP) and mortality.A total of 406 patients diagnosed with AP admitted in a tertiary teaching hospital were enrolled. Demographic information and clinical parameters were retrospectively collected and analyzed. NLR, PLR, RDW, blood urea nitrogen (BUN), and AP severity scores (SOFA, BISAP, Ranson, and APACHE II) were compared between different severity groups and the survival and death group. Receiver-operating characteristic (ROC) curves for SAP and 28-day mortality were calculated for each predictor using cut-off values. Area under the curve (AUC) analysis and logistic regression models were performed to compare the performance of laboratory biomarkers and severity scores.Our results showed that NLR, PLR, RDW, glucose, and BUN level of the SAP group were significantly increased compared to the mild acute pancreatitis (MAP) group on admission (P < .001). The severity of AP increased as the NLR, SOFA, BISAP, and Ranson increased (P < .01). The AUC values of NLR, PLR, RDW, BUN, SOFA, BISAP, Ranson, and APACHE II to predict SAP were 0.722, 0.621, 0.787, 0.677, 0.806, 0.841, 0.806, and 0.752, respectively, while their AUC values to predict 28-day mortality were 0.851, 0.693, 0.885, 0.765, 0.968, 0.929, 0.812, and 0.867, respectively. BISAP achieved the highest AUC, sensitivity and NPV in predicting SAP, while SOFA is the most superior in predicting mortality. The combination of BISAP + RDW achieved the highest AUC (0.872) in predicting SAP and the combination of SOFA + RDW achieved the highest AUC (0.976) in predicting mortality. RDW (OR = 1.739), SOFA (OR = 1.554), BISAP (OR = 2.145), and Ranson (OR = 1.434) were all independent risk factors for predicting SAP, while RDW (OR = 7.361) and hematocrit (OR = 0.329) were independent risk factors for predicting mortality by logistic regression model.NLR, PLR, RDW, and BUN indicated good predictive value for SAP and mortality, while RDW had the highest discriminatory capacity. RDW is a convenient and reliable indicator for prediction not only SAP, but also mortality.

Blood Urea Nitrogen Elevation Is a Marker for Pediatric Severe Acute Pancreatitis

OBJECTIVE

The natural course of patients who develop acute pancreatitis (AP) can range from mild to severe acute pancreatitis (SAP). The aim of this study was to evaluate for early predictors of developing SAP during the first episode of AP in a prospective pediatric cohort.

METHODS

Clinical data were prospectively collected and subsequently analyzed for pediatric patients presenting with their first episode of AP between March 2013 and January 2017 to Cincinnati Children's Hospital Medical Center.

RESULTS

A total of 118 patients were included in the analysis, and 22 (18.6%) developed SAP. Patients who developed SAP had significantly higher values of blood urea nitrogen (BUN) (P = 0.007), magnesium (P = 0.04), glucose (P = 0.03), sodium (P = 0.03), and C-reactive protein (P = 0.02). A logistic regression model with BUN as a predictor of SAP was superior to any other combination of variables (area under the receiver operating characteristic curve, 0.75; 95% confidence interval, 0.61-0.89; sensitivity, 63%; specificity, 81%; positive predictive value, 43%; negative predictive value, 91%).

CONCLUSIONS

This study generated a predictive model using elevated BUN as a significant predictor of SAP. The findings are useful for early identification of pediatric patients at higher risk of developing SAP.

A prediction model for hypoxemia during routine sedation for gastrointestinal endoscopy

OBJECTIVES

The current study was designed to assess the clinical predictors of hypoxemia and to develop a multivariable, predictive model for hypoxemia during routine gastrointestinal endoscopy.

METHODS

In total, 308 patients were enrolled in the analysis. Demographic data, concurrent chronic disease information, anesthetic dose and Modified Observer's Assessment of Alertness/Sedation (MOAA/S) scores were collected and analyzed statistically.

RESULTS

Multivariate logistic regression indicated that age (OR: 1.04; 95%CI 1.01-1.08), body mass index (BMI) (OR: 1.12; 95%CI: 1.02-1.21) and habitual snoring (OR: 3.71; 95%CI: 1.62-8.48) were independently associated with hypoxemia. A logistic regression function (LR model) was developed to predict hypoxemia considering the parameters of -7.73+0.04 age (years), +0.11 BMI, and +1.31 habitual snoring (yes or no). The area under the receiver operating characteristic (ROC) curve for the LR model was 0.76.

CONCLUSIONS

The LR model, consisting of age, BMI and habitual snoring, was a useful predictor of hypoxemia during routine sedation for gastrointestinal endoscopy.

Relationship between low-density lipoprotein cholesterol and severe acute pancreatitis ("the lipid paradox")

BACKGROUND AND AIM

The aim of this study was to investigate the association between low-density lipoprotein cholesterol (LDL-C) and the development of severe acute pancreatitis (SAP).

PATIENTS AND METHODS

A total of 674 patients with acute pancreatitis were enrolled. Non-linearity in the relationship between LDL-C and SAP was assessed by restricted cubic spline analysis. Univariable and multivariable regression analyses were used to identify independent risk factors of SAP.

RESULTS

The restricted cubic spline analysis suggested a nonlinear association between high-density lipoprotein cholesterol (HDL-C), LDL-C and triglyceride levels and incidence of SAP. The incidence of SAP in patients with low LDL-C (<90 mg/dL), moderate LDL-C (90-150 mg/dL) and high LDL-C (>150 mg/dL) levels was 15.1%, 3.7% and 9.8%, respectively. Multivariable analysis confirmed that low LDL-C levels (odds ratio [OR] 3.05; 95% confidence interval [CI] 1.35-6.90), high LDL-C levels (OR 4.42; 95% CI 1.41-13.87) and low HDL-C levels (OR 6.90; 95% CI 2.61-18.23) but not high triglyceride levels (OR 1.05; 95% CI 0.40-2.72) were associated with the development of SAP.

CONCLUSION

Both low LDL-C (<90 mg/dL) and high LDL-C (>150 mg/dL) levels within 24 hours from admission are independently associated with an increased risk of SAP.

Predictors of acute pancreatitis with low elevation of serum amylase

Background and aims

Serum amylase is a traditional measure used to establish the diagnosis of acute pancreatitis (AP). The current study aimed to assess the predictors and clinical outcome of AP with low serum amylase.

Methods

All patients were divided into two groups, based on their serum amylase level within the first 2 days after hospital admission: group 1 (amylase ≥300 U/L) and group 2 (amylase <300 U/L). Clinical outcomes were compared between the two groups before and after 1:1 propensity score matching. Clinical and biochemical parameters were collected and evaluated as potential predictors of AP with low serum amylase.

Results

A total of 464 patients were enrolled. After propensity score matching according to age, gender, time interval before admission, hematocrit, blood urea nitrogen and creatinine, 108 matched pairs of patients were selected. There was no significant statistical difference between group 2 and group 1 with respect to severity of AP, median days of stay in hospital and death. Multivariate analysis indicated that biliary etiology (odds ratio [OR]: 0.499; 95% confidence interval [CI]: 0.265-0.942; P=0.003), low-density lipoprotein cholesterol (LDL-C) (OR: 1.009; 95% CI: 1.002-1.017; P=0.017) and triglyceride levels (OR: 1.001; 95% CI: 1.0001-1.001; P=0.015) were independently associated with development of AP along with low serum amylase.

Conclusion

Serum amylase level was not related to the severity of AP, median hospital stay (days) and death. Biliary etiology, LDL-C and triglyceride levels were independently associated with the development of AP with lower elevation of serum amylase.

High-Density Lipoprotein Cholesterol, Blood Urea Nitrogen, and Serum Creatinine Can Predict Severe Acute Pancreatitis

BACKGROUND AND AIMS

Early prediction of disease severity of acute pancreatitis (AP) would be helpful for triaging patients to the appropriate level of care and intervention. The aim of the study was to develop a model able to predict Severe Acute Pancreatitis (SAP).

METHODS

A total of 647 patients with AP were enrolled. The demographic data, hematocrit, High-Density Lipoprotein Cholesterol (HDL-C) determinant at time of admission, Blood Urea Nitrogen (BUN), and serum creatinine (Scr) determinant at time of admission and 24 hrs after hospitalization were collected and analyzed statistically.

RESULTS

Multivariate logistic regression indicated that HDL-C at admission and BUN and Scr at 24 hours (hrs) were independently associated with SAP. A logistic regression function (LR model) was developed to predict SAP as follows: -2.25-0.06 HDL-C (mg/dl) at admission + 0.06 BUN (mg/dl) at 24 hours + 0.66 Scr (mg/dl) at 24 hours. The optimism-corrected c-index for LR model was 0.832 after bootstrap validation. The area under the receiver operating characteristic curve for LR model for the prediction of SAP was 0.84.

CONCLUSIONS

The LR model consists of HDL-C at admission and BUN and Scr at 24 hours, representing an additional tool to stratify patients at risk of SAP.

Serum Albumin Is Independently Associated with Persistent Organ Failure in Acute Pancreatitis

BACKGROUND AND AIMS

To investigate the association between serum albumin levels within 24 hrs of patient admission and the development of persistent organ failure in acute pancreatitis.

METHODS

A total of 700 patients with acute pancreatitis were enrolled. Multivariate logistic regression and subgroup analysis determined whether decreased albumin was independently associated with persistent organ failure and mortality. The diagnostic performance of serum albumin was evaluated by the area under Receiver Operating Characteristic (ROC) curves.

RESULTS

As levels of serum albumin decrease, the risk of persistent organ failure significantly increases (P_trend < 0.001). The incidence of organ failure was 3.5%, 10.6%, and 41.6% in patients with normal albumin and mild and severe hypoalbuminaemia, respectively. Decreased albumin levels were also proportionally associated with prolonged hospital stay (P_trend < 0.001) and the risk of death (P_trend < 0.001). Multivariate analysis suggested that biliary etiology, chronic concomitant diseases, hematocrit, blood urea nitrogen, and the serum albumin level were independently associated with persistent organ failure. Blood urea nitrogen and the serum albumin level were also independently associated with mortality. The area under ROC curves of albumin for predicting organ failure and mortality were 0.78 and 0.87, respectively.

CONCLUSION

A low serum albumin is independently associated with an increased risk of developing of persistent organ failure and death in acute pancreatitis. It may also be useful for the prediction of the severity of acute pancreatitis.