Deceased serum bilirubin and albumin levels in the assessment of severity and mortality in patients with acute pancreatitis

Total bilirubin level is associated with acute kidney injury in neonates admitted to the neonatal intensive care units: based on MIMIC-III database

OBJECTIVE

The objective of this study was to investigate the association between total bilirubin and acute kidney injury (AKI) in neonates admitted to neonatal intensive care units (NICU).

METHODS

All data utilized were extracted from Medical Information Mart for Intensive Care-III (MIMIC-III) in this retrospective cohort study. The primary outcome was the occurrence of AKI during hospitalization in the NICU, and the exposure was the initial measurement of total bilirubin levels within 24 h of neonatal admission to the NICU. The relationship between serum total bilirubin and AKI was evaluated by employing univariate and multivariate logistic regression models. Additionally, subgroup analyses were conducted based on birth weight, sepsis, and mechanical ventilation.

RESULTS

This retrospective cohort study included a population of 1,726 neonates, and 95 neonates developed AKI. Total bilirubin, as a continuous variable, was linked with decreased AKI risk among neonates admitted to the NICU [odds ratio (OR) = 0.77, 95% confidence interval (CI): 0.64-0.92]. Similarly, when total bilirubin levels were categorized by tertiles, tertiles 3 showed a significant association with decreased AKI risk (OR = 0.39, 95%CI: 0.19-0.83). The relationship of total bilirubin level and AKI was also existent among neonates admitted to the NICU who were underweight, had not sepsis, and received mechanical ventilation.

CONCLUSION

Total bilirubin level may be a protective factor for the risk of developing AKI.

Construction and validation of a nomogram for predicting survival in elderly patients with severe acute pancreatitis: a retrospective study from a tertiary center

PURPOSE

There is a lack of adequate models specifically designed for elderly patients with severe acute pancreatitis (SAP) to predict the risk of death. This study aimed to develop a nomogram for predicting the overall survival of SAP in elderly patients.

METHODS

Elderly patients diagnosed with SAP between January 1, 2017 and December 31, 2022 were included in the study. Risk factors were identified through least absolute shrinkage and selection operator regression analysis. Subsequently, a novel nomogram model was developed using multivariable logistic regression analysis. The predictive performance of the nomogram was evaluated using metrics such as the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA).

RESULTS

A total of 326 patients were included in the analysis, with 260 in the survival group and 66 in the deceased group. Multivariate logistic regression indicated that age, respiratory rate, arterial pH, total bilirubin, and calcium were independent prognostic factors for the survival of SAP patients. The nomogram demonstrated a performance comparable to sequential organ failure assessment (P = 0.065). Additionally, the calibration curve showed satisfactory predictive accuracy, and the DCA highlighted the clinical application value of the nomogram.

CONCLUSION

We have identified key demographic and laboratory parameters that are associated with the survival of elderly patients with SAP. These parameters have been utilized to create a precise and user-friendly nomogram, which could be an effective and valuable clinical tool for clinicians.

Prediction of in-hospital Mortality of Intensive Care Unit Patients with Acute Pancreatitis Based on an Explainable Machine Learning Algorithm

BACKGROUND AND AIM

Acute pancreatitis (AP) is potentially fatal. Therefore, early identification of patients at a high mortality risk and timely intervention are essential. This study aimed to establish an explainable machine-learning model for predicting in-hospital mortality of intensive care unit (ICU) patients with AP.

METHODS

Data on patients with AP, including demographics, vital signs, laboratory tests, comorbidities, treatment, complication, and severity scores, were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU collaborative research database (eICU-CRD). Based on the data from MIMIC-IV, we used the least absolute shrinkage and selection operator algorithm to select variables and then established 9 machine-learning models and screened the optimal model. Data from the eICU-CRD were used for external validation. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, decision curve, and calibration plots were used to assess the models' efficacy. Shapley's additive explanation values were used to explain the model.

RESULTS

Gaussian naive Bayes (GNB) model had the best performance on the data from MIMIC-IV, with an AUC, accuracy, sensitivity, and specificity of 0.840, 0.787, 0.839, and 0.792, respectively. The GNB model also performed well on the data from the eICU-CRD, with an AUC, accuracy, sensitivity, and specificity of 0.862, 0.833, 0.848, and 0.763, respectively. According to Shapley's additive explanation values, the top 4 predictive factors were maximum red cell distribution width, minimum saturation of blood oxygen, maximum blood urea nitrogen, and the Sequential Organ Failure Assessment score.

CONCLUSION

The GNB model demonstrated excellent performance and generalizability in predicting mortality in ICU patients with AP. Therefore, it can identify patients at a high mortality risk.

Development and Validation of a Nomogram for Predicting the Severity of the First Episode of Hyperlipidemic Acute Pancreatitis

Purpose

Early detection of hyperlipidemic acute pancreatitis (HLAP) with exacerbation tendency is crucial for clinical decision-making and improving prognosis. The aim of this study was to establish a reliable model for the early prediction of HLAP severity.

Patients and Methods

A total of 225 patients with first-episode HLAP who were admitted to Fujian Medical University Union Hospital from June 2012 to June 2023 were included. Patients were divided into mild acute pancreatitis (MAP) or moderate-severe acute pancreatitis and severe acute pancreatitis (MSAP+SAP) groups. Independent predictors for progression to MSAP or SAP were identified through univariate analysis and least absolute shrinkage and selection operator regression. A nomogram was established through multivariate logistic regression analysis to predict this progression. The calibration, receiver operating characteristic(ROC), and clinical decision curves were employed to evaluate the model's consistency, differentiation, and clinical applicability. Clinical data of 93 patients with first-episode HLAP who were admitted to the First Affiliated Hospital of Fujian Medical University from October 2015 to October 2022 were collected for external validation.

Results

White blood cell count, lactate dehydrogenase, albumin, serum creatinine, serum calcium, D-Dimer were identified as independent predictors for progression to MSAP or SAP in patients with HLAP and used to establish a predictive nomogram. The internally verified Harrell consistency index (C-index) was 0.908 (95% CI 0.867-0.948) and the externally verified C-index was 0.950 (95% CI 0.910-0.990). The calibration, ROC, and clinical decision curves showed this nomogram's good predictive ability.

Conclusion

We have established a nomogram that can help identify HLAP patients who are likely to develop MSAP or SAP at an early stage, with high discrimination and accuracy.

Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis

Background

Predicting the severity of acute pancreatitis (AP) early poses a challenge in clinical practice. While there are well-established clinical scoring tools, their actual predictive performance remains uncertain. Various studies have explored the application of machine-learning methods for early AP prediction. However, a more comprehensive evidence-based assessment is needed to determine their predictive accuracy. Hence, this systematic review and meta-analysis aimed to evaluate the predictive accuracy of machine learning in assessing the severity of AP.

Methods

PubMed, EMBASE, Cochrane Library, and Web of Science were systematically searched until December 5, 2023. The risk of bias in eligible studies was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analyses, based on different machine learning types, were performed. Additionally, the predictive accuracy of mainstream scoring tools was summarized.

Results

This systematic review ultimately included 33 original studies. The pooled c-index in both the training and validation sets was 0.87 (95 % CI: 0.84-0.89) and 0.88 (95 % CI: 0.86-0.90), respectively. The sensitivity in the training set was 0.81 (95 % CI: 0.77-0.84), and in the validation set, it was 0.79 (95 % CI: 0.71-0.85). The specificity in the training set was 0.84 (95 % CI: 0.78-0.89), and in the validation set, it was 0.90 (95 % CI: 0.86-0.93). The primary model incorporated was logistic regression; however, its predictive accuracy was found to be inferior to that of neural networks, random forests, and xgboost. The pooled c-index of the APACHE II, BISAP, and Ranson were 0.74 (95 % CI: 0.68-0.80), 0.77 (95 % CI: 0.70-0.85), and 0.74 (95 % CI: 0.68-0.79), respectively.

Conclusions

Machine learning demonstrates excellent accuracy in predicting the severity of AP, providing a reference for updating or developing a straightforward clinical prediction tool.

Acute Pancreatitis: Clinical Profile of 60 Patients

Introduction  Acute pancreatitis (AP) is a common differential diagnosis of acute pain abdomen and cannot be considered self-limiting as it has serious early and long-term impacts. Depending on severity, AP is divided into mild, moderately severe, and severe AP. Management of AP involves accurate diagnosis, high-quality supportive care, monitoring for early detection and treatment of complications, and prevention of relapse. Aim To assess the etiological risk factors, clinical profile, and complications in patients with AP. Methods The present study was conducted on 60 eligible patients admitted to the Department of Medicine, Government Medical College, and Hospital of Northern India. A diagnosis of AP was established based on the revised Atlanta classification (2012) for the classification of AP, and relevant data were collected and statistically analyzed. Results Most of the AP patients were in the 21-40 year age group. The majority were males (88.3%). Alcohol was the most common etiological factor in 76.7% of patients followed by cholelithiasis in 10% of patients. Pain abdomen was the most common presenting clinical feature occurring in 96.7% of patients and vomiting in 65% of the patients. Acute fluid collection was the most common pancreatic complication occurring in 26.7% of the patients, pancreatic edema was seen in 21.7%, and pancreatic necrosis in 15%. Among extrapancreatic complications, ascites was most commonly seen in 50% of patients followed by pleural effusion in 15%, shock in 15%, multiple organ dysfunction syndrome (MODS) in 15%, and hypocalcemia in 11.7% of patients. Conclusion AP should be one of the differentials for patients presenting with pain abdomen, especially when probable risk factors such as alcohol abuse and cholelithiasis are present. A high index of suspicion to diagnose AP is needed as timely management may prevent systematic complications, thus improving the outcome. Poor prognostic indicators are raised levels of total serum bilirubin, raised serum lipase, reduced serum albumin, and low platelet count among AP patients.

Serum iron fluctuations link ferroptosis process with mortality and prognosis of acute pancreatitis

Recently, the existence of ferroptosis has been confirmed in chronic pancreatitis. However, its role in acute pancreatitis (AP) process, especially in critical status, has not yet been mentioned. To verify this hypothesis, we included 873 AP patients (training set) and 1,188 NAFLD patients (internal validation set) selected from MIMIC-III (Medical Information Mark for Intensive Care) database and 218 AP patients (external validation set) in Linshui County People's Hospital ICU data. We analyzed the correlation between mortality and ferroptosis associating factors (such as serum iron, ALP, lactate, etc.) in them through regression analysis. In addition, to test the significance of these factors, the nomogram, AUC, and DCA analysis were applied. The results showed that serum iron, IBC, ALP, and lactate (p < 0.05) were independent factors for the mortality and prognosis of these patients. These correlations suggest ferroptosis and follow-up cell programmed death may own an important clinical interference significance among this population.

Development and Validation of a Dynamic Nomogram for Predicting in-Hospital Mortality in Patients with Acute Pancreatitis: A Retrospective Cohort Study in the Intensive Care Unit

Purpose

The aim of this study is to develop and validate a predictive model for the prediction of in-hospital mortality in patients with acute pancreatitis (AP) based on the intensive care database.

Patients and Methods

We analyzed the data of patients with AP in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and Electronic Intensive Care Unit Collaborative Research Database (eICU-CRD). Then, patients from MIMIC-IV were divided into a development group and a validation group according to the ratio of 8:2, and eICU-CRD was assigned as an external validation group. Univariate logistic regression and least absolute shrinkage and selection operator regression were used for screening the best predictors, and multivariate logistic regression was used to establish a dynamic nomogram. We evaluated the discrimination, calibration, and clinical efficacy of the nomogram, and compared the performance of the nomogram with Acute Physiology and Chronic Health Evaluation II (APACHE-II) score and Bedside Index of Severity in AP (BISAP) score.

Results

A total of 1030 and 514 patients with AP in MIMIC-IV database and eICU-CRD were included in the study. After stepwise analysis, 8 out of a total of 37 variables were selected to construct the nomogram. The dynamic nomogram can be obtained by visiting https://model.sci-inn.com/KangZou/. The area under receiver operating characteristic curve (AUC) of the nomogram was 0.859, 0.871, and 0.847 in the development, internal, and external validation set respectively. The nomogram had a similar performance with APACHE-II (AUC = 0.841, p = 0.537) but performed better than BISAP (AUC = 0.690, p = 0.001) score in the validation group. Moreover, the calibration curve presented a satisfactory predictive accuracy, and the decision curve analysis suggested great clinical application value of the nomogram.

Conclusion

Based on the results of internal and external validation, the nomogram showed favorable discrimination, calibration, and clinical practicability in predicting the in-hospital mortality of patients with AP.

Association between lactate-to-albumin ratio and 28-days all-cause mortality in patients with acute pancreatitis: A retrospective analysis of the MIMIC-IV database

Objective

The Lactate-to-Albumin Ratio (LAR) has been applied as a new predictor in sepsis, heart failure, and acute respiratory failure. However, the role of LAR in predicting all-cause mortality in patients with acute pancreatitis has not been evaluated. Therefore, this study aimed to elucidate the correlation between LAR and 28-d all-cause mortality in patients with Acute Pancreatitis (AP).

Methods

This study is a retrospective cohort study with the data from the MIMIC-IV (v1.0) database. We included adult patients with acute pancreatitis who were admitted to the intensive care unit in the study. The primary outcome was to evaluate the ability of LAR to predict death at 28-d of hospital admission in patients with AP.

Results

A total of 539 patients with acute pancreatitis were included in this study. They were divided into a survival group (486 patients) and a death group (53 patients) according to whether they survived within 28-d of admission, and the mortality rate of patients within 28-d of admission was 9.8%. LAR was shown to be an independent predictor of all-cause mortality within 28-d of admission in patients with AP by multivariate COX regression analysis (HR, 1.59; 95% CI, 1.23 - 2.05; P < 0.001). the Area Under the Curve (AUC) value for LAR was 74.26% (95% CI: 67.02% - 81.50%), which was higher than that for arterial blood lactate (AUC = 71.25%) and serum albumin (AUC = 65.92%) alone. It was not inferior even when compared to SOFA (AUC = 75.15%). The optimal cutoff value for separating the survival and death groups according to Receiver Operating Characteristic (ROC) was found to be 1.1124. plotting Kaplan-Meier analysis with this cutoff value showed that patients with LAR ≥ 1.1124 had significantly higher all-cause mortality within 28-d of admission than those with LAR < 1.1124 (P < 0.001). The final subgroup analysis showed no significant interaction of LAR with each subgroup (P for interaction: 0.06 - 0.974).

Conclusion

LAR can be used as an independent predictor of all-cause mortality in AP patients within 28-d of admission, with superior prognostic performance than arterial blood lactate or serum albumin alone.

Improving mortality prediction in Acute Pancreatitis by machine learning and data augmentation

Acute Pancreatitis (AP) is the inflammation of the pancreas that can be fatal or lead to further complications based on the severity of the attack. Early detection of AP disease can help save lives by providing utmost care, rigorous treatment, and better resources. In this era of data and technology, instead of relying on manual scoring systems, scientists are employing advanced machine learning and data mining models for the early detection of patients with high chances of mortality. The current work on AP mortality prediction is negligible, and the few studies that exist have many shortcomings and are impractical for clinical deployment. In this research work, we tried to overcome the existing issues. One main issue is the lack of high-quality public datasets for AP, which are crucial for effectively training ML models. The available datasets are small in size, have many missing values, and suffer from high class imbalance. We augmented three public datasets, MIMIC-III, MIMIC-IV, and eICU, to obtain a larger dataset, and experiments proved that augmented data trained classifiers better than original small datasets. Moreover, we employed emerging advanced techniques to handle underlying issues in data. The results showed that iterative imputer is best for filling missing values in AP data. It beats not only the basic techniques but also the Knn-based imputation. Class imbalance is first addressed using data downsampling; apparently, it gave decent results on small test sets. However, we conducted numerous experiments on large test sets to prove that downsampling in the case of AP produced misleading and poor results. Next, we applied various techniques to upsample data in two different class splits, a 50 to 50 and a 70 to 30 majority-minority class split. Four different tabular generative adversarial networks, CTGAN, TGAN, CopulaGAN, and CTAB, and a variational autoencoder, TVAE, were deployed for synthetic data generation. SMOTE was also utilized for data upsampling. The computational results showed that the Random Forest (RF) classifier outperformed all other classifiers on a 50 to 50 class split data generated by CTGAN, with 0.702 Fβ and 0.833 recall. Results produced by RF on the TVAE dataset were also comparable, with 0.698 Fβ. In the case of SMOTE-based upsampling, DNN performed best with a 0.671 Fβ score.

Predicting persistent organ failure on admission in patients with acute pancreatitis: development and validation of a mobile nomogram

BACKGROUND

Early prediction of persistent organ failure (POF) is important for triage and timely treatment of patients with acute pancreatitis (AP).

METHODS

All AP patients were consecutively admitted within 48 h of symptom onset. A nomogram was developed to predict POF on admission using data from a retrospective training cohort, validated by two prospective cohorts. The clinical utility of the nomogram was defined by concordance index (C-index), decision curve analysis (DCA), and clinical impact curve (CIC), while the performance by post-test probability.

RESULTS

There were 816, 398, and 880 patients in the training, internal and external validation cohorts, respectively. Six independent predictors determined by logistic regression analysis were age, respiratory rate, albumin, lactate dehydrogenase, oxygen support, and pleural effusion and were included in the nomogram (web-based calculator: https://shina.shinyapps.io/DynNomapp/). This nomogram had reasonable predictive ability (C-indexes 0.88/0.91/0.81 for each cohort) and promising clinical utility (DCA and CIC). The nomogram had a positive likelihood ratio and post-test probability of developing POF in the training, internal and external validation cohorts of 4.26/31.7%, 7.89/39.1%, and 2.75/41%, respectively, superior or equal to other prognostic scores.

CONCLUSIONS

This nomogram can predict POF of AP patients and should be considered for clinical practice and trial allocation.

Development and validation of a nomogram for early assessment the severity of acute pancreatitis

BACKGROUND

Acute pancreatitis is an acute inflammatory disorder of the pancreas, and severe acute pancreatitis is associated with high mortality. Early assessment the severity of AP has an important significance for improving clinical outcomes. Our object aimed to develop a nomogram with high simplicity and rapidity for predicting the severity of acute pancreatitis.

METHODS

Patients admitted to the Hunan Provincial People's Hospital within 72 h from onset of AP from January 2010 and December 2020 were enrolled to establish a nomogram. Independent predictors were determined using univariate and multivariate analysis and then assembled to construct a predicting nomogram. The performance of proposed nomogram was evaluated by Brier score and Harrell's concordance index (C-index). Meanwhile, clinical data of AP patients from January 2021 to January 2022 were collected for external validation.

RESULTS

Album (OR 0.891, 95%CI 0.867-0.917), calcium (OR 0.151, 95%CI 0.084-0.273), neutrophil to lymphocyte ratio (OR 1.055, 95%CI 1.023-1.088) and systemic inflammatory response syndrome (OR 6.292, 95%CI 4.459-8.879) were identified as independent factors of SAP after univariate and multivariate analysis (p < .05). A predictive nomogram was accordingly established using these four independent variables. The internally verified C-index was 0.796 (95% CI 0.773-0.818), Brier score was 0.138. The externally verified C index was 0.820 (95% CI 0.754-0.887).

CONCLUSION

A nomogram for predicting the severity of AP was well developed, it may be of great significance for clinicians to quickly assess the progress of AP and choose more-targeted strategies.

Prediction of the severity of acute pancreatitis using machine learning models

BACKGROUND

Acute pancreatitis (AP) is the most common pancreatic disease. Predicting the severity of AP is critical for making preventive decisions. However, the performance of existing scoring systems in predicting AP severity was not satisfactory. The purpose of this study was to develop predictive models for the severity of AP using machine learning (ML) algorithms and explore the important predictors that affected the prediction results.

METHODS

The data of 441 patients in the Department of Gastroenterology in our hospital were analyzed retrospectively. The demographic data, blood routine and blood biochemical indexes, and the CTSI score were collected to develop five different ML predictive models to predict the severity of AP. The performance of the models was evaluated by the area under the receiver operating characteristic curve (AUC). The important predictors were determined by ranking the feature importance of the predictive factors.

RESULTS

Compared to other ML models, the extreme gradient boosting model (XGBoost) showed better performance in predicting severe AP, with an AUC of 0.906, an accuracy of 0.902, a sensitivity of 0.700, a specificity of 0.961, and a F1socre of 0.764. Further analysis showed that the CTSI score, ALB, LDH, and NEUT were the important predictors of the severity of AP.

CONCLUSION

The results showed that the XGBoost algorithm can accurately predict the severity of AP, which can provide an assistance for the clinicians to identify severe AP at an early stage.

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

Background

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

Methods

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

Results

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

Conclusions

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

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.

Liver injury associated with acute pancreatitis: The current status of clinical evaluation and involved mechanisms

Acute pancreatitis (AP) is a very common acute disease, and the mortality rate of severe AP (SAP) is between 15% and 35%. The main causes of death are multiple organ dysfunction syndrome and infections. The mortality rate of patients with SAP related to liver failure is as high as 83%, and approximately 5% of the SAP patients have fulminant liver failure. Liver function is closely related to the progression and prognosis of AP. In this review, we aim to elaborate on the clinical manifestations and mechanism of liver injury in patients with AP.

Ranson score to stratify severity in Acute Pancreatitis remains valid - Old is gold

Introduction: Acute pancreatitis (AP) is a common gastrointestinal disease with a wide spectrum of severity and morbidity. Developed in 1974, the Ranson score was the first scoring system to prognosticate AP. Over the past decades, while the Ranson score remains widely used, it was identified to have certain limitations, such as having low predictive power. It has also been criticized for its 48-hour requirement for computation of the final score, which has been argued to potentially delay management. With advancements in our understanding of AP, is the Ranson score still relevant as an effective prognostication system for AP?Areas covered: This review summarizes the available evidence comparing Ranson score with other conventional and novel scoring systems, in terms of prognostic accuracy, benefits, limitations and clinical applicability. It also evaluates the effectiveness of Ranson score with regard to the Revised Atlanta Classification.Expert opinion: The Ranson score consistently exhibits comparable prognostic accuracy to other newer scoring systems, and the 48-hour timeframe for computing the full Ranson score is an inherent strength, not a weakness. These aspects, coupled with relative ease of use, practicality and universality of the score, advocate for the continued relevance of the Ranson score in modern clinical practice.

The association of parameters of body composition and laboratory markers with the severity of hypertriglyceridemia-induced pancreatitis

BACKGROUND

Hypertriglyceridemia has arisen as the third leading cause of acute pancreatitis. This study aimed at exploring the association between the severity of hypertriglyceridemia-induced pancreatitis (HTGP) and computed tomography (CT)-based body composition parameters and laboratory markers.

METHODS

Laboratory and clinical parameters were collected from 242 patients with HTGP between 2017 and 2020. Severity of HTGP was evaluated by original or modified CT severity index. Body composition parameters such as area and radiodensity of muscle, subcutaneous adipose tissue and visceral adipose tissue were calculated by CT at the level of third lumbar vertebra. Parameters were compared between mild and moderately severe to severe HTGP. Uni-variate and multi-variate Logistic regression analyses were employed to assess the risk factors of the severity of HTGP.

RESULTS

Seventy patients (28.9%) presented with mild HTGP. Body mass index, waist circumference and all CT-based body composition parameters differed between male and female patients. None was associated with the severity of HTGP, neither in males nor in females. Receiver operating characteristic curves showed that areas under the curves of apolipoprotein A-I and albumin to predict the severity of HTGP were 0.786 and 0.759, respectively (all P < 0.001). Uni-variate and further multi-variate Logistic regression analysis confirmed that low serum albumin (< 35 g/L, P = 0.004, OR = 3.362, 95%CI = 1.492-8.823) and apolipoprotein A-I (< 1.1 g/L, P < 0.001, OR = 5.126, 95%CI = 2.348-11.195), as well as high C-reactive protein (> 90 mg/L, P = 0.005, OR = 3.061, 95%CI = 1.407-6.659) and lipase (P = 0.033, OR = 2.283, 95%CI = 1.070-4.873) were risk factors of moderately severe to severe HTGP. Levels of albumin, apolipoprotein A-I, C-reactive protein and lipase were also associated with the length of hospital stay (all P < 0.05). Besides, low serum albumin, low-density lipoprotein cholesterol and high radiodensity of subcutaneous adipose tissue were significant risk factors of pancreatic necrosis in patients with HTGP (all P < 0.05).

CONCLUSIONS

Low serum albumin and apolipoprotein A-I, and high C-reactive protein and lipase upon admission were associated with a more severe type of HTGP and longer hospital stay for these patients. Albumin and apolipoprotein A-I may serve as novel biomarkers for the severity of HTGP. However, none of the body composition parameters was associated with the severity of HTGP.