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

Admission hematocrit and fluctuating blood urea nitrogen levels predict the efficacy of blood purification treatment in severe acute pancreatitis patients

This study aimed to evaluate the prognostic significance of the levels of admission hematocrit (HCT) and the changes in the initial blood urea nitrogen (BUN) levels in predicting the efficacy of blood purification (BP) therapy in ameliorating severe acute pancreatitis (SAP) patients at admission. A retrospective study was conducted on 139 SAP patients from the People's Hospital of Guangxi Zhuang Autonomous Region from 2013 to 2022 and the data retrieved from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database for 346 SAP patients. The patients were stratified based on their HCT0 levels at admission; HCT < 44% (n = 93) and HCT ≥ 44% (n = 46) and ΔBUN levels within the first 24 h post-admission; ΔBUN ≤ 0 (n = 78) and ΔBUN > 0 (n = 61). Propensity score matching (PSM) was performed on factors such as age and gender to control for differences among the strata. The clinical outcomes of the patients receiving or not receiving BP therapy were compared based on the mentioned criteria. Patients with HCT0 ≥ 44%, who were treated with BP showed no significant difference in the 28-day mortality. However, a significant increase in hospital expenses and prolonged ICU stays was observed (P < 0.05). Conversely, patients with ΔBUN ≤ 0 who received BP therapy demonstrated relatively high 28-day mortality rates, prolonged ICU stays, increased hospital expenses, and low SOFA scores (P < 0.05). The analyses of MIMIC-IV database data corroborated these findings. The predictive efficacy of BP therapy in SAP patients was significantly influenced by the changes in BUN levels at 24 h post-admission compared to the initial levels of HCT on admission. Selecting SAP patients suitable for BP treatment should be based on the changes in BUN levels to enhance effective therapeutic outcomes.

Advances in the clinical application of machine learning in acute pancreatitis: a review

Traditional disease prediction models and scoring systems for acute pancreatitis (AP) are often inadequate in providing concise, reliable, and effective predictions regarding disease progression and prognosis. As a novel interdisciplinary field within artificial intelligence (AI), machine learning (ML) is increasingly being applied to various aspects of AP, including severity assessment, complications, recurrence rates, organ dysfunction, and the timing of surgical intervention. This review focuses on recent advancements in the application of ML models in the context of AP.

Usefulness of Dynamic Assessment of Clinical and Laboratory Factors in Severe Acute Pancreatitis

Background/Objectives: Early identification of patients at risk of developing severe acute pancreatitis (SAP) is still an issue. Dynamic assessment of clinical and laboratory parameters within the first 48 h of admission may offer valuable insights into the prediction of unfavorable outcomes such as SAP and death. Methods: A prospective observational study was conducted on a cohort of patients admitted for AP at a tertiary referral hospital. Clinical and laboratory data were collected on admission and at 48 h. Patients were classified based on the Revised Atlanta classification. Logistic regression analysis was performed to identify independent risk factors for SAP. Likelihood ratios and post-test probabilities were calculated to assess the clinical usefulness of predictive markers. Results: 227 patients were included, with biliary etiology being the most common and a prevalence of SAP and death of 10.7% and 5.7%, respectively. BISAP ≥ 2 on admission, presence of SIRS after 48 h, rise in heart rate over 20 bpm, and any increase in BUN after 48 h were independent risk factors for SAP. The combination of these factors increased the post-test probability of SAP and death, with BISAP ≥ 2 combined with the presence of SIRS after 48 h showing the highest probability (82% and 73%, respectively). Conclusions: Dynamic assessment of BUN, heart rate, and SIRS within the first 48 h of admission can aid in predicting the development of SAP and death in patients with AP. These findings underscore the importance of continuous monitoring, although multicenter studies are warranted to refine predictive models for SAP.

Construction and alidation of a severity prediction model for acute pancreatitis based on CT severity index: A retrospective case-control study

To construct and internally and externally validate a nomogram model for predicting the severity of acute pancreatitis (AP) based on the CT severity index (CTSI).A retrospective analysis of clinical data from 200 AP patients diagnosed at the Hefei Third Clinical College of Anhui Medical University from June 2019 to June 2022 was conducted. Patients were classified into non-severe acute pancreatitis (NSAP, n = 135) and severe acute pancreatitis (SAP, n = 65) based on final clinical diagnosis. Differences in CTSI, general clinical features, and laboratory indicators between the two groups were compared. The LASSO regression model was used to select variables that might affect the severity of AP, and these variables were analyzed using multivariate logistic regression. A nomogram model was constructed using R software, and its AUC value was calculated. The accuracy and practicality of the model were evaluated using calibration curves, Hosmer-Lemeshow test, and decision curve analysis (DCA), with internal validation performed using the bootstrap method. Finally, 60 AP patients treated in the same hospital from July 2022 to December 2023 were selected for external validation.LASSO regression identified CTSI, BUN, D-D, NLR, and Ascites as five predictive factors. Unconditional binary logistic regression analysis showed that CTSI (OR = 2.141, 95%CI:1.369-3.504), BUN (OR = 1.378, 95%CI:1.026-1.959), NLR (OR = 1.370, 95%CI:1.016-1.906), D-D (OR = 1.500, 95%CI:1.112-2.110), and Ascites (OR = 5.517, 95%CI:1.217-2.993) were independent factors influencing SAP. The established prediction model had a C-index of 0.962, indicating high accuracy. Calibration curves demonstrated good consistency between predicted survival rates and actual survival rates. The C-indexes for internal and external validation were 0.935 and 0.901, respectively, with calibration curves close to the ideal line.The model based on CTSI and clinical indicators can effectively predict the severity of AP, providing a scientific basis for clinical decision-making by physicians.

Multi-dimensional metabolomic profiling reveals dysregulated ornithine metabolism hallmarks associated with a severe acute pancreatitis phenotype

To reveal dysregulated metabolism hallmark that was associated with a severe acute pancreatitis (SAP) phenotype. In this study, LC-MS/MS-based targeted metabolomics was used to analyze plasma samples from 106 acute pancreatitis (AP) patients (34 mild, 38 moderate, and 34 severe) admitted within 48 hours from abdominal pain onset and 41 healthy controls. Temporal metabolic profiling was performed on days 1, 3, and 7 after admission. A random forest (RF) was performed to significantly determine metabolite differences between SAP and non-SAP (NSAP) groups. Mass spectrometry imaging (MSI) and immunohistochemistry were conducted for the examination of pancreatic metabolite and metabolic enzyme alterations, respectively, on necrosis and paracancerous tissues. Simultaneously determination of serum and pancreatic tissue metabolic alterations using an L-ornithine-induced AP model to discover metabolic commonalities. Twenty-two significant differential metabolites screened by RF were selected to build an accurate model for the prediction of SAP from NSAP (AUC = 0.955). Six of 22 markers were found by MSI with significant alterations in pancreatic lesions, reduced ornithine-related metabolites were also identified. The abnormally expressed arginase2 and ornithine transcarboxylase were further discovered in combination with time-course metabolic profiling in the SAP animal models, the decreased ornithine catabolites were found at a late stage of inflammation, but ornithine-associated metabolic enzymes were activated during the inflammatory process. The plasma metabolome of AP patients is distinctive, which shows promise for early SAP diagnosis. AP aggravation is linked to the activated ornithine metabolic pathway and its inadequate levels of catabolites in in-situ lesion.

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.

Changes in coagulation indices and D-dimer levels in hypertriglyceridemic acute pancreatitis and their value in predicting disease severity

OBJECTIVES

Hypertriglyceridemic acute pancreatitis (HTG-AP) is one of the common acute and severe cases of digestive system. Incidence of HTG-AP is increasing year by year, and there is a trend of younger and severe cases. Early identification of severe patients and timely intervention are conducive to improve the prognosis. Dysfunction of coagulation and fibrinolysis systems plays an important role in the development of HTG-AP. Prothrombin time (PT) and activated partial thromboplastin time (APTT) are sensitive indicators of exogenous and endogenous coagulation system, respectively. Fibrinogen (FIB) is an acute reactive protein with coagulation function. D-dimer is a sensitive index to judge the abnormality of coagulation and fibrinolysis system. This study aims to investigate the changes of coagulation parameters and D-dimer level in patients with HTG-AP and their value in predicting the severity of the disease.

METHODS

The clinical data of 189 HTG-AP patients admitted to Jiangjin Hospital Affiliated to Chongqing University (Jiangjin District Central Hospital of Chongqing) from January 2019 to December 2020 were collected, including gender, age, medical history, complications, causes, and acute physiology and chronic health evaluation II (APACHE II) scores. According to the severity of the disease, the patients were divided into a mild HTG-AP group and a severe HTG-AP group. The changes of coagulation indexes (PT, APTT and FIB), D-dimer and C-reactive protein (CRP) levels were detected. Coagulation indexes, D-dimer level and disease severity (CRP level, APACHE II scores) were compared between the 2 groups. Spearman correlation analysis was used to analyze the correlation between the above indexes. Univariate and multivariate binary logistic regression analysis were used to identify the independent risk factors for severe HTG-AP. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to analyze the predictive value of PT, FIB, and D-dimer for the severity of HTG-AP.

RESULTS

There were significant differences in gender between the mild HTG-AP group and the severe HTG-AP group (P<0.05). There was no significant difference in age, recurrence rate and incidence of complications between the 2 groups (all P>0.05). The basic conditions of the 2 groups were similar and comparable. PT, FIB and D-dimer levels in the severe HTG-AP group were significantly higher than those in the mild HTG-AP group (all P<0.001). There was no significant difference in APTT between the 2 groups (P>0.05). The CRP level and APACHE II scores in the severe HTG-AP group were significantly higher than those in the mild HTG-AP group (all P<0.05). Spearman correlation analysis showed that PT, FIB and D-dimer were positively correlated with CRP and APACHE II scores (all P<0.05), while APTT was not correlated with CRP and APACHE II scores (all P>0.05). Logistic regression analysis showed that prolonged PT and elevated D-dimer level were independent risk factors for severe HTG-AP (both P<0.05). ROC curve analysis showed that the AUC of PT for predicting severe HTG-AP was 0.764 (95% CI 0.690 to 0.837, P<0.001), when PT>14.40 s, the sensitivity, specificity, positive predictive value, and negative predictive value were 63.07%, 79.03%, 59.42%, and 80.00%, respectively. TheAUC of FIB for predicting severe HTG-AP was 0.669 (95% CI 0.588 to 0.751, P<0.001), when FIB>4.18 g/L, the sensitivity, specificity, positive predictive value, and negative predictive value were 61.53%, 70.17%, 49.38%, and 76.85%, respectively. The AUC of D-dimer for predicting severe HTG-AP was 0.753 (95% CI 0.680 to 0.826, P<0.001), when D-dimer>1.28 μg/mL, the sensitivity, specificity, positive predictive value, and negative predictive value were 73.84%, 66.12%, 53.33%, and 82.82%, respectively. The AUC of PT combined with D-dimer for predicting severe HTG-AP was 0.797.

CONCLUSIONS

There are abnormalities in coagulation and fibrinolytic system in patients with HTG-AP in the early stage. PT, FIB and D-dimer levels are increased with the aggravation of the AP, and have a positively correlation with it. Elevated PT and D-dimer level are independent risk factors for severe HTG-AP. PT combined with D-dimer may be a sensitive indicator for prediction of the severity of HTG-AP in early phase.

Decreased HDL-C Levels as a Predictor of Organ Failure in Acute Pancreatitis in the Emergency Department

High-density lipoprotein cholesterol (HDL-C) is reported as a biomarker of systemic inflammation and multi-organ failure (MOF), which has been rarely investigated in acute pancreatitis (AP), a frequent condition in the emergency department (ED). The objective was to study the predictive capacity of the decrease in HDL-C to the progression of MOF in AP in the ED; analyzing 114 patients with AP for one year in a longitudinal and prospective study, AP severity was obtained by the Atlanta classification, in relation to modified Marshall and Bedside Index for Severity in Acute Pancreatitis (BISAP) scores, and clinical and laboratory parameters in a 48 h hospital stay. The area under the receiver operating characteristic (ROC) curve was used to estimate the validity of the predictor and define optimal cut-off points. It was found that AP was classified as severe in 24.5%, mainly for biliary etiology (78.9%) and female sex (73.6%). As a biomarker, HDL-C decreased from 31.6 to 29.5 mg/dL in a 48 h stay (p < 0.001), correlating negatively with the increase in severity index > 2 and the modified Marshall (p < 0.032) and BISAP (p < 0.009) scores, finding an area under the ROC curve with a predictive capacity of 0.756 (95% CI, 0.614-0.898; p < 0.004) and a cut-off point of 28.5 mg/dL (sensitivity: 79%, specificity: 78%), demonstrating that the decrease in HDL-C levels serves as a useful indicator with a predictive capacity for MOF in mild to severe AP, during a 48 h hospital stay in the ED.

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.

Home care/outpatient versus hospital admission in mild acute pancreatitis: protocol of a multicentre, randomised controlled trial (PADI_2 trial)

INTRODUCTION

Acute pancreatitis (AP) is the third most common gastrointestinal disease resulting in hospital admission, with over 70% of AP admissions being mild cases. In the USA, it costs 2.5 billion dollars annually. The most common standard management of mild AP (MAP) still is hospital admission. Patients with MAP usually achieve complete recovery in less than a week and the severity predictor scales are reliable. The aim of this study will be to compare three different strategies for the management of MAP.

METHODS/DESIGN

This is a randomised, controlled, three-arm multicentre trial. Patients with MAP will be randomly assigned to group A (outpatient), B (home care) or C (hospital admission). The primary endpoint of the trial will be the treatment failure rate of the outpatient/home care management for patients with MAP compared with that of hospitalised patients. The secondary endpoints will be pain relapse, diet intolerance, hospital readmission, hospital length of stay, need for intensive care unit admission, organ failure, complications, costs and patient satisfaction. The general feasibility, safety and quality checks required for high-quality evidence will be adhered to.

ETHICS AND DISSEMINATION

The study (version 3.0, 10/2022) has been approved by the Scientific and Research Ethics Committee of the 'Institut d'Investigació Sanitaria Pere Virgili-IISPV' (093/2022). This study will provide evidence as to whether outpatient/home care is similar to usual management of AP. The conclusions of this study will be published in an open-access journal.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT05360797).

Update on the strategy for intravenous fluid treatment in acute pancreatitis

Fluid therapy/resuscitation is mandatory in acute pancreatitis due to the pathophysiology of fluid loss as a consequence of the inflammatory process. For many years, without clear evidence, early and aggressive fluid resuscitation with crystalloid solutions (normal saline solution or Ringer lactate solution) was recommended. Recently, many randomized control trials and meta-analyses on fluid therapy have revealed that high fluid rate infusion is associated with increased mortality and severe adverse events compared to those resulting from moderate fluid rates, and this has triggered a paradigm shift in fluid management strategies. Meanwhile, there is evidence to show that Ringer lactate solution is superior to normal saline solutions in this context. The purpose of this review is to provide an update on the strategies for intravenous fluid treatment in acute pancreatitis, including the type, optimal amount, rate of infusion, and monitoring guides. Recommendations from recent guidelines are critically evaluated for this review in order to reach the authors' recommendations based on the available evidence.

A New Risk Score to Predict Intensive Care Unit Admission for Patients with Acute Pancreatitis 48 Hours After Admission: Multicenter Study

AIMS

The objective of this study was to develop and validate an easy-to-use risk score (APRS) to predict which patients with acute pancreatitis (AP) will need intensive care unit (ICU) treatment within 48 h post-hospitalization on the basis of the ubiquitously available clinical records.

METHODS

Patients with acute pancreatitis were retrospectively included from three independent institutions (RM cohort, 5280; TJ cohort, 262; SN cohort, 196), with 56 candidate variables collected within 48 h post-hospitalization. The RM cohort was randomly divided into a training set (N = 4220) and a test set (N = 1060). The most predictive features were extracted by LASSO from the RM cohort and entered into multivariate analysis. APRS was constructed using the coefficients of the statistically significant variables weighted by the multivariable logistic regression model. The APRS was validated by RM, TJ, and SN cohorts. The C-statistic was employed to evaluate the APRS's discrimination. DeLong test was used to compare area under the receiver operating characteristic curve (AUC) differences.

RESULTS

A total of 5738 patients with AP were enrolled. Eleven variables were selected by LASSO and entered into multivariate analysis. APRS was inferred using the above five factors (pleural effusion, ALT/AST, ALB/GLB, urea, and glucose) weighted by their regression coefficients in the multivariable logistic regression model. The C-statistics of APRS were 0.905 (95% CI 0.82-0.98) and 0.889 (95% CI 0.81-0.96) in RM and TJ validation. An online APRS web-based calculator was constructed to assist the clinician to earlier assess the clinical outcomes of patients with AP.

CONCLUSION

APRS could effectively stratify patients with AP into high and low risk of ICU admission within 48 h post-hospitalization, offering clinical value in directing management and personalize therapeutic selection for patients with AP.

Impact of comorbidities on hospital mortality in patients with acute pancreatitis: a population-based study of 110,021 patients

BACKGROUND

The impact of pre-existing comorbidities on acute pancreatitis (AP) mortality is not clearly defined. Our study aims to determine the trend in AP hospital mortality and the role of comorbidities as a predictor of hospital mortality.

METHODS

We analyzed patients aged ≥ 18 years hospitalized with AP diagnosis between 2016 and 2019. The data have been extracted from the Spanish National Hospital Discharge Database of the Spanish Ministry of Health. We performed a univariate and multivariable analysis of the association of age, sex, and comorbidities with hospital mortality in patients with AP. The role of the Charlson and Elixhauser comorbidity indices as predictors of mortality was evaluated.

RESULTS

A total of 110,021 patients diagnosed with AP were hospitalized during the analyzed period. Hospital mortality was 3.8%, with a progressive decrease observed in the years evaluated. In multivariable analysis, age ≥ 65 years (OR: 4.11, p < 0.001), heart disease (OR: 1.73, p < 0.001), renal disease (OR: 1.99, p < 0.001), moderate-severe liver disease (OR: 2.86, p < 0.001), peripheral vascular disease (OR: 1.43, p < 0.001), and cerebrovascular disease (OR: 1.63, p < 0.001) were independent risk factors for mortality. The Charlson > 1.5 (OR: 2.03, p < 0.001) and Elixhauser > 1.5 (OR: 2.71, p < 0.001) comorbidity indices were also independently associated with mortality, and ROC curve analysis showed that they are useful for predicting hospital mortality.

CONCLUSIONS

Advanced age, heart disease, renal disease, moderate-severe liver disease, peripheral vascular disease, and cerebrovascular disease before admission were independently associated with hospital mortality. The Charlson and Elixhauser comorbidity indices are useful for predicting hospital mortality in AP patients.

Development and evaluation of machine learning models and nomogram for the prediction of severe acute pancreatitis

BACKGROUND AND AIM

Severe acute pancreatitis (SAP) in patients progresses rapidly and can cause multiple organ failures associated with high mortality. We aimed to train a machine learning (ML) model and establish a nomogram that could identify SAP, early in the course of acute pancreatitis (AP).

METHODS

In this retrospective study, 631 patients with AP were enrolled in the training cohort. For predicting SAP early, five supervised ML models were employed, such as random forest (RF), K-nearest neighbors (KNN), and naive Bayes (NB), which were evaluated by accuracy (ACC) and the areas under the receiver operating characteristic curve (AUC). The nomogram was established, and the predictive ability was assessed by the calibration curve and AUC. They were externally validated by an independent cohort of 109 patients with AP.

RESULTS

In the training cohort, the AUC of RF, KNN, and NB models were 0.969, 0.954, and 0.951, respectively, while the AUC of the Bedside Index for Severity in Acute Pancreatitis (BISAP), Ranson and Glasgow scores were only 0.796, 0.847, and 0.837, respectively. In the validation cohort, the RF model also showed the highest AUC, which was 0.961. The AUC for the nomogram was 0.888 and 0.955 in the training and validation cohort, respectively.

CONCLUSIONS

Our findings suggested that the RF model exhibited the best predictive performance, and the nomogram provided a visual scoring model for clinical practice. Our models may serve as practical tools for facilitating personalized treatment options and improving clinical outcomes through pre-treatment stratification of patients with AP.

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.

Elevated Serum Triglyceride Levels in Acute Pancreatitis: A Parameter to be Measured and Considered Early

BACKGROUND

The value of serum triglycerides (TGs) related to complications and the severity of acute pancreatitis (AP) has not been clearly defined. Our study aimed to analyze the association of elevated levels of TG with complications and the severity of AP.

METHODS

The demographic and clinical data of patients with AP were prospectively analyzed. TG levels were measured in the first 24 h of admission. Patients were divided into two groups: one with TG values of<200 mg/dL and another with TG≥200 mg/dL. Data on the outcomes of AP were collected.

RESULTS

From January 2016 to December 2019, 247 cases were included: 200 with TG<200 mg/dL and 47 with TG≥200 mg/dL. Triglyceride levels≥200 mg/dL were associated with respiratory failure (21.3 vs. 10%, p=0.033), renal failure (23.4 vs. 12%, p=0.044), cardiovascular failure (19.1 vs. 7.5%, p=0.025), organ failure (34 vs. 18.5%, p=0.02), persistent organ failure (27.7 vs. 9.5%, p=0.001), multiple organ failure (19.1 vs. 8%, p=0.031), moderately severe and severe AP (68.1 vs. 40.5%, p=0.001), pancreatic necrosis (63.8 vs. 34%, p<0.001), and admission to the intensive care unit (27.7 vs. 9.5%, p=0.003). In the multivariable analysis, a TG level of≥200 mg/dL was independently associated with respiratory, renal, and cardiovascular failure, organ failure, persistent organ failure, multiple organ failure, pancreatic necrosis, severe pancreatitis, and admission to the intensive care unit (p<0.05).

CONCLUSIONS

In our cohort, TG≥200 mg/dL was related to local and systemic complications. Early determinations of TG levels in AP could help identify patients at risk of complications.

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.

Analysis of the Value of the Blood Urea Nitrogen to Albumin Ratio as a Predictor of Mortality in Patients with Sepsis

Purpose

At present, simple, accurate, and efficient prognostic tools for the evaluation of cases with early-stage sepsis in the emergency department (ED) are lacking. An increased blood urea nitrogen to albumin ratio (BAR) has previously been shown to be a valuable biomarker with predictive utility in several diseases. The relationship between BAR and sepsis patient outcomes, however, is not well-understood. This exploration was thus developed for the exploration of the link between BAR values and the short-term prognosis of cases with sepsis.

Methods

This was a retrospective cohort research of sepsis cases admitted to the West China Hospital of Sichuan University ED from July 2015 to June 2016. Laboratory data were collected upon ED admission, and 7-day all-cause mortality was the primary study endpoint. Relationships between BAR values and APACE II and SOFA scores were generated assessed with correlation coefficient heatmaps. Independent risk factors were identified through multivariate analyses, with the curves of receiver operating characteristic (ROC) being employed to gauge the value of BAR as a predictor of the risk of mortality in sepsis cases.

Results

In sum, 801 patients participated in the present investigation. BAR values were strongly correlated with APACHE II and SOFA scores. In a multivariate logistic regression assessment, BAR was identified as an independent predictor of mortality among patients with sepsis (HR=1.032, 95% CI: 1.010–1.055, P=0.004). BAR exhibited an AUC of 0.741 (95% CI: 0.688–0.793, P<0.001) when used to predict patient mortality risk, with 5.27 being the optimal BAR cut-off.

Conclusion

We found that BAR can be used as a reliable biomarker to predict mortality in patients with sepsis.

Clinical and simple laboratory data associated with fatal outcomes in patients with acute pancreatitis

Aims. The aim of the study was to evaluate the association of mortality in acute pancreatitis with clinical and simple laboratory data received on the day of admission. Patients and methods. In our retrospective study, the clinical and laboratory parameters of 99 patients with moderate and severe acute pancreatitis were analyzed. All patients were divided into two groups: deceased and survivors. Results. We did not find a significant difference in age and gender distribution between the comparison groups. However, a significant predominance of alcoholic etiology of acute pancreatitis, early hospitalization (up to 6 hours from the onset of the disease) of patients, and the number of necrotizing infected type in the deceased group were found. Concomitant pathology did not significantly differ in comparison groups. In patients from the deceased group, the total number of all complications was significantly higher than in the group of survivors-21 (100%) and 42 (53.8%) (p = 0.0001), respecting. Among the laboratory parameters determined on the day of admission, in the deceased group, there was a significant increase in stabs to 19.8 ± 9.8 and ESR, AST to 225.3 ± 47.5 U/L, urea to 11.2 ± 7.7 mmol/L, and creatinine to 173.6 ± 26.1 mmol/L. Conclusion. The alcoholic genesis of acute pancreatitis, necrotizing infected type of inflammation of the pancreas, presence of late complications, and comorbidities were significantly higher in the deceased group. The levels of stabs, ESR, AST, urea, and creatinine determined on the admission significantly dominated in the deceased group, which requires further study for the prediction of mortality of acute pancreatitis.