EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis

Temporal trends in gender, etiology, severity and outcomes of acute pancreatitis in a third-tier Chinese city from 2013 to 2021

BACKGROUND

To evaluate temporal trends in gender, etiology, severity, outcomes, cost and median length of stay (MLS) in patients with acute pancreatitis (AP) in a third-tier Chinese city.

METHODS

Patients with AP admitted to a university hospital between January 2013 and December 2021. Relationships between etiology, prevalence of severe acute pancreatitis (SAP) and survey years were investigated by joinpoint regression analysis.

RESULTS

A total of 5459 (male 62.3%) patients with AP were included. Between January 2013 and December 2021, we observed: (a) the prevalence of biliary diseases-related AP was stable, while the prevalence of hypertriglyceridemia (HTG)-associated AP (Ptrend = 0.04) and alcohol-associated AP (Ptrend < 0.0001) both increased; (b) there was an increase in crude prevalence of SAP from 4.97% to 12.2% between 2013 and 2021 (Ptrend < 0.0001); (c) compared to female populations, male gender had a higher prevalence of AP; (d) there was a decrease in MLS from 11 days to 8 days (Ptrend < 0.0001) and in median cost of hospitalization (MCH) for all patients (from 20,166 to 12,845 YUAN) (Ptrend < 0.0001); (e) the overall in-hospital mortality rate was 1.28% (70/5459) for patients with AP. There was no statistically significant in the time trend of mortality during the study period (Ptrend = 0.5873). At multivariate analysis, survey year was associated with prevalence of SAP after adjustment by age and biliary diseases (OR: 1.07; 95% CI: 1.03-1.12). Based on the stratification by severity of disease, the decrease of MLS and MCH was more significant in non-SAP vs. SAP patients.

CONCLUSIONS

Over the observational period, the proportion of male patients with AP, prevalence of age-adjusted rate of HTG and alcohol-associated AP and SAP increased, while MLS and MCH for all patients decreased, and the time trend of mortality of AP was stable.

Artificial intelligence in gastrointestinal surgery: A minireview of predictive models and clinical applications

BACKGROUND

Artificial intelligence (AI) is playing an increasingly significant role in predicting outcomes of gastrointestinal (GI) surgeries, improving preoperative risk assessment and post-surgical decision-making. AI models, particularly those based on machine learning, have demonstrated potential in predicting surgical complications and recovery trajectories.

AIM

To evaluate the role of AI in predicting outcomes for GI surgeries, focusing on its efficacy in enhancing surgical planning, predicting complications, and optimizing post-operative care.

METHODS

A systematic review of studies published up to March 2025 was conducted across databases such as PubMed, Scopus, and Web of Science. Studies were included if they utilized AI models for predicting surgical outcomes, including morbidity, mortality, and recovery. Data were extracted on the AI techniques, performance metrics, and clinical applicability.

RESULTS

Machine learning models demonstrated significantly better performance than logistic regression models, with an area under the curve difference of 0.07 (95%CI: 0.04–0.09; P < 0.001). Models focusing on variables such as patient demographics, nutritional status, and surgical specifics have shown improved accuracy. AI’s ability to integrate multifaceted data sources, such as imaging and genomics, contributes to its superior predictive power. AI has improved the early detection of gastric cancer, achieving 95% sensitivity in real-world settings.

CONCLUSION

AI has the potential to transform GI surgical practices by offering more accurate and personalized predictions of surgical outcomes. However, challenges related to data quality, model transparency, and clinical integration remain.

An Efficient Electrochemiluminescence Biosensor Based on Ru(bpy)32+@AuNPs@SWCNTs for miRNAs Detection Using a Dual Engine-Triggered DNA Walker

The diagnosis and treatment of acute pancreatitis remain challenging due to the limitations of diagnostic methods, which often result in delayed treatment and suboptimal outcomes. This underscores the need for innovative diagnostic strategies to enable early detection and improve therapeutic interventions. Electrochemiluminescence (ECL)-based biosensors have emerged as a promising solution, offering advantages such as cost-effectiveness, ease of use, and high sensitivity. This study introduces an innovative ECL biosensor design, which incorporates a DNA tetrahedron as a structural scaffold, a double swing arm mechanism for enhanced motion control, and a track-based signal regulation system. This design significantly enhanced the operating efficiency and controllability of DNA walkers. The system utilizes ferrocene (Fc) as a signal quenching agent, with its electrochemical signal restored upon interaction with miRNA24-3p, a biomarker for acute pancreatitis. The platform features a composite luminescent material─tris(2,2'-bipyridine) dichlororuthenium(II)@goldnanoparticles@single-walled carbon nanotubes (Ru(bpy)32+@AuNPs@SWCNTs)─and employs persulfate as a coreactant. Under optimized conditions, this design demonstrated a wide dynamic range (10-15 M to 10-6 M) and an ultralow detection limit of approximately 60 aM for miRNA 24-3p. Additionally, it exhibited excellent specificity, reproducibility, and stability. These findings underscore the potential of this application of this ECL-based platform to revolutionize the clinical diagnosis of acute pancreatitis by enabling more timely and accurate interventions while paving the way for advancements in diagnostic technologies.

Constructing a prediction model for acute pancreatitis severity based on liquid neural network

Acute pancreatitis (AP) is a common disease, and severe acute pancreatitis (SAP) has a high morbidity and mortality rate. Early recognition of SAP is crucial for prognosis. This study aimed to develop a novel liquid neural network (LNN) model for predicting SAP. This study retrospectively analyzed the data of AP patients admitted to the Second Affiliated Hospital of Guilin Medical University between January 2020 and June 2024. Data imbalance was dealt with by data preprocessing and using the synthetic minority oversampling technique (SMOTE). A new feature selection method was designed to optimize model performance. Logistic regression (LR), decision tree (DCT), random forest (RF), Extreme Gradient Boosting (XGBoost), and LNN models were built. The model's performance was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC) and other statistical metrics. In addition, SHapley Additive exPlanations (SHAP) analysis was used to interpret the prediction results of the LNN model. The LNN model performed best in predicting AP severity, with an AUC value of 0.9659 and accuracy, precision, recall, F1 score, and specificity higher than 0.90. SHAP analysis revealed key predictors, such as calcium level, amylase activity, and percentage of basophils, which were strongly associated with AP severity. As an emerging machine learning tool, the LNN model has demonstrated excellent performance and potential in AP severity prediction. The results of this study support the idea that LNN models can be applied to early severity assessment of AP patients in a clinical setting, which can help optimize treatment plans and improve patient prognosis.

Application of deep learning models for accurate classification of fluid collections in acute necrotizing pancreatitis on computed tomography: a multicenter study

PURPOSE

To apply CT-based deep learning (DL) models for accurate solid debris-based classification of pancreatic fluid collections (PFC) in acute pancreatitis (AP).

MATERIAL AND METHODS

This retrospective study comprised four tertiary care hospitals. Consecutive patients with AP and PFCs who had computed tomography (CT) prior to drainage were screened. Those who had magnetic resonance imaging (MRI) or endoscopic ultrasound (EUS) within 20 days of CT were considered for inclusion. Axial CT images were utilized for model training. Images were labelled as those with≤30% solid debris and >30% solid debris based on MRI or EUS. Single center data was used for model training and validation. Data from other three centers comprised the held out external test cohort. We experimented with ResNet 50, Vision transformer (ViT), and MedViT architectures.

RESULTS

Overall, we recruited 152 patients (129 training/validation and 23 testing). There were 1334, 334 and 512 images in the training, validation, and test cohorts, respectively. In the overall training and validation cohorts, ViT and MedVit models had high diagnostic performance (sensitivity 92.4-98.7%, specificity 89.7-98.4%, and AUC 0.908-0.980). The sensitivity (85.3-98.6%), specificity (69.4-99.4%), and AUC (0.779-0.984) of all the models was high in all the subgroups in the training and validation cohorts. In the overall external test cohort, MedViT had the best diagnostic performance (sensitivity 75.2%, specificity 75.3%, and AUC 0.753). MedVit had sensitivity, specificity, and AUC of 75.2%, 74.3%, and 0.748, in walled off necrosis and 79%, 74.2%, 75.3%, and 0.767 for collections >5 cm.

CONCLUSION

DL-models have moderate diagnostic performance for solid-debris based classification of WON and collections greater than 5 cm on CT.

Prediction Models of Medication Adherence in Chronic Disease Patients: Systematic Review and Critical Appraisal

AIMS AND OBJECTIVES

To summarise the currently developed risk prediction models for medication adherence in patients with chronic diseases and evaluate their performance and applicability.

BACKGROUND

Ensuring medication adherence is crucial in effectively managing chronic diseases. Although numerous studies have endeavoured to construct risk prediction models for predicting medication adherence in patients with chronic illnesses, the reliability and practicality of these models remain uncertain.

DESIGN

Systematic review.

METHODS

We conducted searches on PubMed, Web of Science, Cochrane, CINAHL, Embase and Medline from inception until 16 July 2023. Two authors independently screened risk prediction models for medication adherence that met the predefined inclusion criteria. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed to evaluate both the risk of bias and clinical applicability of the included studies. This systematic review adhered to the 2020 PRISMA checklist.

RESULTS

The study included a total of 11 risk prediction models from 11 studies. Medication regimen and age were the most common predictors. The use of PROBAST revealed that some essential methodological details were not thoroughly reported in these models. Due to limitations in methodology, all models were rated as having a high-risk for bias.

CONCLUSIONS

According to PROBAST, the current models for predicting medication adherence in patients with chronic diseases exhibit a high risk of bias. Future research should prioritise enhancing the methodological quality of model development and conducting external validations on existing models.

RELEVANCE TO CLINICAL PRACTICE

Based on the review findings, recommendations have been provided to refine the construction methodology of prediction models with an aim of identifying high-risk individuals and key factors associated with low medication adherence in chronic diseases.

PATIENT OR PUBLIC CONTRIBUTION

This systematic review was conducted without patient or public participation.

A machine learning model for predicting severe mycoplasma pneumoniae pneumonia in school-aged children

OBJECTIVE

To develop an interpretable machine learning (ML) model for predicting severe Mycoplasma pneumoniae pneumonia (SMPP) in order to provide reliable factors for predicting the clinical type of the disease.

METHODS

We collected clinical data from 483 school-aged children with M. pneumoniae pneumonia (MPP) who were hospitalized at the Children's Hospital of Soochow University between September 2021 and June 2024. Difference analysis and univariate logistic regression were employed to identify predictors for training features in ML. Eight ML algorithms were used to build models based on the selected features, and their effectiveness was validated. The area under the curve (AUC), accuracy, five-fold cross-validation, and decision curve analysis (DCA) were utilized to evaluate model performance. Finally, the best-performing ML model was selected, and the Shapley Additive Explanations (SHAP) method was applied to rank the importance of clinical features and interpret the final model.

RESULTS

After feature selection, 30 variables remained. We constructed eight ML models and assessed their effectiveness, finding that the CatBoost model exhibited the best predictive performance, with an AUC of 0.934 and an accuracy of 0.9175. DCA was used to compare the clinical benefits of the models, revealing that the CatBoost model provided greater net benefits than the other ML models within the threshold probability range of 34% to 75%. Additionally, we applied the SHAP method to interpret the CatBoost model, and the SHAP diagram was used to visually show the influence of predictor variables on the outcome. The results identified the top six risk factors as the number of days with fever, D-dimer, platelet count (PLT), C-reactive protein (CRP), lactate dehydrogenase (LDH), and the neutrophil-to-lymphocyte ratio (NLR).

CONCLUSIONS

The interpretable CatBoost model can help physicians accurately identify school-aged children with SMPP. This early identification facilitates better treatment options and timely prevention of complications. Furthermore, the SHAP algorithm enhances the model's transparency and increases its trustworthiness in practical applications.

Machine Learning-Guided Fluid Resuscitation for Acute Pancreatitis Improves Outcomes

INTRODUCTION

Ariel Dynamic Acute Pancreatitis Tracker (ADAPT) is an artificial intelligence tool using mathematical algorithms to predict severity and manage fluid resuscitation needs based on the physiologic parameters of individual patients. Our aim was to assess whether adherence to ADAPT fluid recommendations vs standard management impacted clinical outcomes in a large prospective cohort.

METHODS

We analyzed patients consecutively admitted to the Los Angeles General Medical Center between June 2015 and November 2022 whose course was richly characterized by capturing more than 100 clinical variables. We inputted these data into the ADAPT system to generate resuscitation fluid recommendations and compared with the actual fluid resuscitation within the first 24 hours from presentation. The primary outcome was the difference in organ failure in those who were over-resuscitated (>500 mL) vs adequately resuscitated (within 500 mL) with respect to the ADAPT fluid recommendation. Additional outcomes included intensive care unit admission, systemic inflammatory response syndrome (SIRS) at 48 hours, local complications, and pancreatitis severity.

RESULTS

Among the 1,083 patients evaluated using ADAPT, 700 were over-resuscitated, 196 were adequately resuscitated, and 187 were under-resuscitated. Adjusting for pancreatitis etiology, gender, and SIRS at admission, over-resuscitation was associated with increased respiratory failure (odd ratio [OR] 2.73, 95% confidence interval [CI] 1.06-7.03) as well as intensive care unit admission (OR 2.40, 1.41-4.11), more than 48 hours of hospital length of stay (OR 1.87, 95% CI 1.19-2.94), SIRS at 48 hours (OR 1.73, 95% CI 1.08-2.77), and local pancreatitis complications (OR 2.93, 95% CI 1.23-6.96).

DISCUSSION

Adherence to ADAPT fluid recommendations reduces respiratory failure and other adverse outcomes compared with conventional fluid resuscitation strategies for acute pancreatitis. This validation study demonstrates the potential role of dynamic machine learning tools in acute pancreatitis management.

Prediction and detection of terminal diseases using Internet of Medical Things: A review

The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) has revolutionized disease prediction and detection, but challenges such as data heterogeneity, privacy concerns, and model generalizability hinder its full potential in healthcare. This review examines these challenges and evaluates the effectiveness of AI-IoMT techniques in predicting chronic and terminal diseases, including cardiovascular conditions, Alzheimer's disease, and cancers. We analyze a range of Machine Learning (ML) and Deep Learning (DL) approaches (e.g., XGBoost, Random Forest, CNN, LSTM), alongside advanced strategies like federated learning, transfer learning, and blockchain, to improve model robustness, data security, and interoperability. Findings highlight that transfer learning and ensemble methods enhance model adaptability across clinical settings, while blockchain and federated learning effectively address privacy and data standardization. Ultimately, the review emphasizes the importance of data harmonization, secure frameworks, and multi-disease models as critical research directions for scalable, comprehensive AI-IoMT solutions in healthcare.

AI and Machine Learning for Precision Medicine in Acute Pancreatitis: A Narrative Review

Acute pancreatitis (AP) presents a significant clinical challenge due to its wide range of severity, from mild cases to life-threatening complications such as severe acute pancreatitis (SAP), necrosis, and multi-organ failure. Traditional scoring systems, such as Ranson and BISAP, offer foundational tools for risk stratification but often lack early precision. This review aims to explore the transformative role of artificial intelligence (AI) and machine learning (ML) in AP management, focusing on their applications in diagnosis, severity prediction, complication management, and treatment optimization. A comprehensive analysis of recent studies was conducted, highlighting ML models such as XGBoost, neural networks, and multimodal approaches. These models integrate clinical, laboratory, and imaging data, including radiomics features, and are useful in diagnostic and prognostic accuracy in AP. Special attention was given to models addressing SAP, complications like acute kidney injury and acute respiratory distress syndrome, mortality, and recurrence. AI-based models achieved higher AUC values than traditional models in predicting acute pancreatitis outcomes. XGBoost reached an AUC of 0.93 for early SAP prediction, higher than BISAP (AUC 0.74) and APACHE II (AUC 0.81). PrismSAP, integrating multimodal data, achieved the highest AUC of 0.916. AI models also demonstrated superior accuracy in mortality prediction (AUC 0.975) and ARDS detection (AUC 0.891) AI and ML represent a transformative advance in AP management, facilitating personalized treatment, early risk stratification, and allowing resource utilization to be optimized. By addressing challenges such as model generalizability, ethical considerations, and clinical adoption, AI has the potential to significantly improve patient outcomes and redefine AP care standards globally.

Deep neural networks excel in COVID-19 disease severity prediction-a meta-regression analysis

COVID-19 is a disease in which early prognosis of severity is critical for desired patient outcomes and for the management of limited resources like intensive care unit beds and ventilation equipment. Many prognostic statistical tools have been developed for the prediction of disease severity, but it is still unclear which ones should be used in practice. We aim to guide clinicians in choosing the best available tools to make optimal decisions and assess their role in resource management and assess what can be learned from the COVID-19 scenario for development of prediction models in similar medical applications. Using the five major medical databases: MEDLINE (via PubMed), Embase, Cochrane Library (CENTRAL), Cochrane COVID-19 Study Register, and Scopus, we conducted a comprehensive systematic review of prediction tools between 2020 January and 2023 April for hospitalized COVID-19 patients. We identified both the relevant confounding factors of tool performance using the MetaForest algorithm and the best tools-comparing linear, machine learning, and deep learning methods-with mixed-effects meta-regression models. The risk of bias was evaluated using the PROBAST tool. Our systematic search identified eligible 27,312 studies, out of which 290 were eligible for data extraction, reporting on 430 independent evaluations of severity prediction tools with ~ 2.8 million patients. Neural Network-based tools have the highest performance with a pooled AUC of 0.893 (0.748-1.000), 0.752 (0.614-0.853) sensitivity, 0.914 (0.849-0.952) specificity, using clinical, laboratory, and imaging data. The relevant confounders of performance are the geographic region of patients, the rate of severe cases, and the use of C-Reactive Protein as input data. 88% of studies have a high risk of bias, mostly because of deficiencies in the data analysis. All investigated tools in use aid decision-making for COVID-19 severity prediction, but Machine Learning tools, specifically Neural Networks clearly outperform other methods, especially in cases when the basic characteristics of severe and non-severe patient groups are similar, and without the need for more data. When highly specific biomarkers are not available-such as in the case of COVID-19-practitioners should abandon general clinical severity scores and turn to disease specific Machine Learning tools.

The 12-Year Experience of the Hungarian Pancreatic Study Group

The Hungarian Pancreatic Study Group (HPSG) was established with the aim of advancing pancreatology. Our summary outlines the methodologies, key results, and future directions of the HPSG. Methodological elements included, the formation of strategic national and international collaborations, the establishment of patient registries and biobanks, and a strong focus on education and guideline development. Key results encompassed, pioneering research on pancreatic ductal function and the role of cystic fibrosis transmembrane conductance regulator (CFTR) in inflammation, significant advancements in understanding acute and chronic pancreatitis, and the execution of numerous clinical trials to explore new therapeutic approaches. Despite challenges, such as securing funding and translating research into clinical practice, the HPSG's commitment to patient care and scientific innovation has been unwavering. The group aims to deepen research into pancreatic cancer and chronic pancreatitis, conduct more randomized controlled trials (RCTs), and expand its efforts internationally by involving global staff and patients. The authors hope that this summary inspires others to undertake similar initiatives and contribute to the global advancement of medical research and patient care in pancreatology.

YKL-40 as a risk stratification marker in acute pancreatitis: A prospective study

BACKGROUND/OBJECTIVES

Increased systemic concentrations of YKL-40 are seen in various inflammatory conditions. We explored the relationship between the serum YKL-40 concentrations and subsequent disease severity in patients with acute pancreatitis (AP).

METHODS

Consecutive adults with AP were prospectively enrolled, and classified as having mild, moderate or severe disease. On admission and 48 h later, C-reactive protein (CRP), YKL-40, interleukin-6 and 8 (IL-6, IL-8), and tumor necrosis factor alpha (TNF-α) concentrations were measured. Patients were also classified as those with low (<50 ng/mL, in the range seen in 30 age and sex-matched non-AP subjects), high (≥190 ng/mL, seen in most of the other inflammatory conditions), and intermediate YKL-40 (50-189 ng/mL).

RESULTS

Incidence of mild, moderate and severe AP among the 150 enrolled patients was 80 (53.3 %), 59 (39.3 %), and 11 (7.4 %), respectively. Both on admission and 48 h later, high YKL-40 (vs. intermediate or low) was strongly associated with higher odds of a more severe AP, independently of the concurrent IL-8 and TNF-α concentrations (OR around 3.5-4.0, or higher). On admission, the association was independent also of the concurrent CRP, whereas the association between the later concentrations and the outcome was conditional on CRP - uncertain at low, strong at high CRP. The high YKL-40 - outcome association at both time-points was conditional on concurrent IL-6: uncertain if IL-6 was low, strong if IL-6 was high.

CONCLUSIONS

Serum YKL-40 is a plausible candidate for further evaluation as an early biochemical indicator of subsequent AP severity, particularly if considered jointly with CRP and/or IL-6.

Three-tiered critical care management of acute pancreatitis

INTRODUCTION AND AIMS

Acute pancreatitis (AP) can rapidly progress from a stable condition to multiple organ failure with high mortality. We aimed to describe the characteristics of AP patients requiring admission to a critical care facility and to identify predictors of disease progression.

METHODS

We conducted a post-hoc analysis using prospectively collected data from AP patients admitted to the high dependency unit (HDU) and intensive care unit (ICU) at the University of Pécs, Hungary, from 2016 to 2019. Patients were categorized according to critical care needs and severity. Daily records of organ function, organ support and laboratory parameters were kept. Descriptive analysis and predictive models were developed to forecast the need for escalated critical care and mortality.

RESULTS

Analysis of 92 cases (65 % male, mean age 63 (range 19-92) years) revealed a median critical care stay of 8 days (range 1-69) and a mortality rate of 47 %. Naive Bayes prediction models using admission C-reactive protein (CRP) and amylase levels achieved 75 % accuracy in predicting mortality and a 65 % probability of requiring HDU and/or ICU admission. CRP levels increased significantly (47 vs 62 mg/l, p: 0.015) from 48 to 24 h before critical care admission, contrasting with controls, resulting in significantly higher CRP levels in critical care patients (62 vs 32 mg/l, p: 0.007) 24 h before admission.

CONCLUSION

Our findings suggest that on-admission CRP and amylase cannot reliably predict progression of AP. However, elevated and increasing levels of CRP and amylase may indicate the need for early HDU admission to enable closer monitoring.

A systematic review of machine learning-based prognostic models for acute pancreatitis: Towards improving methods and reporting quality

BACKGROUND

An accurate prognostic tool is essential to aid clinical decision-making (e.g., patient triage) and to advance personalized medicine. However, such a prognostic tool is lacking for acute pancreatitis (AP). Increasingly machine learning (ML) techniques are being used to develop high-performing prognostic models in AP. However, methodologic and reporting quality has received little attention. High-quality reporting and study methodology are critical for model validity, reproducibility, and clinical implementation. In collaboration with content experts in ML methodology, we performed a systematic review critically appraising the quality of methodology and reporting of recently published ML AP prognostic models.

METHODS/FINDINGS

Using a validated search strategy, we identified ML AP studies from the databases MEDLINE and EMBASE published between January 2021 and December 2023. We also searched pre-print servers medRxiv, bioRxiv, and arXiv for pre-prints registered between January 2021 and December 2023. Eligibility criteria included all retrospective or prospective studies that developed or validated new or existing ML models in patients with AP that predicted an outcome following an episode of AP. Meta-analysis was considered if there was homogeneity in the study design and in the type of outcome predicted. For risk of bias (ROB) assessment, we used the Prediction Model Risk of Bias Assessment Tool. Quality of reporting was assessed using the Transparent Reporting of a Multivariable Prediction Model of Individual Prognosis or Diagnosis-Artificial Intelligence (TRIPOD+AI) statement that defines standards for 27 items that should be reported in publications using ML prognostic models. The search strategy identified 6,480 publications of which 30 met the eligibility criteria. Studies originated from China (22), the United States (4), and other (4). All 30 studies developed a new ML model and none sought to validate an existing ML model, producing a total of 39 new ML models. AP severity (23/39) or mortality (6/39) were the most common outcomes predicted. The mean area under the curve for all models and endpoints was 0.91 (SD 0.08). The ROB was high for at least one domain in all 39 models, particularly for the analysis domain (37/39 models). Steps were not taken to minimize over-optimistic model performance in 27/39 models. Due to heterogeneity in the study design and in how the outcomes were defined and determined, meta-analysis was not performed. Studies reported on only 15/27 items from TRIPOD+AI standards, with only 7/30 justifying sample size and 13/30 assessing data quality. Other reporting deficiencies included omissions regarding human-AI interaction (28/30), handling low-quality or incomplete data in practice (27/30), sharing analytical codes (25/30), study protocols (25/30), and reporting source data (19/30).

CONCLUSIONS

There are significant deficiencies in the methodology and reporting of recently published ML based prognostic models in AP patients. These undermine the validity, reproducibility, and implementation of these prognostic models despite their promise of superior predictive accuracy.

REGISTRATION

Research Registry (reviewregistry1727).

AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis

Early identification of pancreatitis remains a significant clinical diagnostic challenge that impacts patient outcomes. The evolution of quantitative imaging followed by deep learning models has shown great promise in the non-invasive diagnosis of pancreatitis and its complications. We provide an overview of advancements in diagnostic imaging and quantitative imaging methods along with the evolution of artificial intelligence (AI). In this article, we review the current and future states of methodology and limitations of AI in improving clinical support in the context of early detection and management of pancreatitis.

Development and Validation of a Coagulation Risk Prediction Model for Anticoagulant-Free Hemodialysis: Enhancing Hemodialysis Safety for Patients

INTRODUCTION

This study aimed to develop and validate a risk prediction model for predicting the likelihood of coagulation in patients undergoing anticoagulant-free hemodialysis (HD). Anticoagulant-free HD technique is necessary in patients with contraindications to systemic therapy. Coagulation is a complication of this technique. Unfortunately, no predictive model is currently available to assess the risk of coagulation in anticoagulant-free HD.

METHODS

We retrospectively analyzed the clinical data from 299 HD sessions involving 164 patients who underwent anticoagulant-free HD between January 2022 and June 2023. To identify the risk factors for coagulation in anticoagulant-free HD, a univariate analysis was performed on 18 independent variables. Logistic regression was used to establish predictive models by identifying factors contributing to coagulation in anticoagulant-free HD. A calibration curve was drawn using regression coefficients and 1,000 bootstrap repetitions to validate our model internally. The performance of the prediction model was evaluated using receiver operating characteristic, area under the curve (AUC), and decision curve analysis (DCA).

RESULTS

The incidence of coagulation in patients on anticoagulant-free HD was 35.1%. Logistic regression analysis showed that platelet (PLT), hematocrit (HCT) levels, dialysate type, and age were risk factors for coagulation in anticoagulant-free HD patients (p < 0.05). The Hosmer-Lemeshow test showed p = 0.29, and the AUC is 0.76 (95% CI 0.70-0.80). The optimal critical value was 0.40, yielding a sensitivity of 61.0%, a specificity of 80.4%, and a Youden index of 0.41.

CONCLUSION

In anticoagulant-free HD, there were numerous risk factors and a 35.1% occurrence of coagulation. The constructed coagulation risk prediction model exhibited good predictive and clinical utility and could serve as a reference for the initial assessment and screening of coagulation risk in anticoagulant-free HD.

Recent Applications of Explainable AI (XAI): A Systematic Literature Review

This systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of explainable AI (XAI) over the past three years. From an initial pool of 664 articles identified through the Web of Science database, 512 peer-reviewed journal articles met the inclusion criteria—namely, being recent, high-quality XAI application articles published in English—and were analyzed in detail. Both qualitative and quantitative statistical techniques were used to analyze the identified articles: qualitatively by summarizing the characteristics of the included studies based on predefined codes, and quantitatively through statistical analysis of the data. These articles were categorized according to their application domains, techniques, and evaluation methods. Health-related applications were particularly prevalent, with a strong focus on cancer diagnosis, COVID-19 management, and medical imaging. Other significant areas of application included environmental and agricultural management, industrial optimization, cybersecurity, finance, transportation, and entertainment. Additionally, emerging applications in law, education, and social care highlight XAI’s expanding impact. The review reveals a predominant use of local explanation methods, particularly SHAP and LIME, with SHAP being favored for its stability and mathematical guarantees. However, a critical gap in the evaluation of XAI results is identified, as most studies rely on anecdotal evidence or expert opinion rather than robust quantitative metrics. This underscores the urgent need for standardized evaluation frameworks to ensure the reliability and effectiveness of XAI applications. Future research should focus on developing comprehensive evaluation standards and improving the interpretability and stability of explanations. These advancements are essential for addressing the diverse demands of various application domains while ensuring trust and transparency in AI systems.

Chronic liver disease is an important risk factor for worse outcomes in acute pancreatitis: a systematic review and meta-analysis

Chronic liver diseases (CLD) affect 1.5 billion patients worldwide, with dramatically increasing incidence in recent decades. It has been hypothesized that the chronic hyperinflammation associated with CLD may increase the risk of a more severe course of acute pancreatitis (AP). This study aims to investigate the underlying impact of CLD on the outcomes of AP. A systematic search was conducted in Embase, Medline, and Central databases until October 2022. Studies investigating patients with acute pancreatitis and CLD, were included in the meta-analysis. A total of 14,963 articles were screened, of which 36 were eligible to be included. CLD was a risk factor for increased mortality with an odds ratio (OR) of 2.53 (CI 1.30 to 4.93, p = 0.01). Furthermore, renal, cardiac, and respiratory failures were more common in the CLD group, with ORs of 1.92 (CI 1.3 to 2.83, p = 0.01), 2.11 (CI 0.93 to 4.77, p = 0.062) and 1.99 (CI 1.08 to 3.65, p = 0.033), respectively. Moreover, the likelihood of developing Systemic Inflammatory Response Syndrome (SIRS) was significantly higher, with an OR of 1.95 (CI 1.03 to 3.68, p = 0.042). CLD is an important risk factor for worse outcomes in AP pancreatitis, leading to higher mortality and increased rates of local and systemic complications.

A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning

Objective

The purpose of this study is to explore whether machine learning can be used to establish an effective model for the diagnosis of Parkinson's disease (PD) by using texture features extracted from cerebellar gray matter and white matter, so as to identify subtle changes that cannot be observed by the naked eye.

Method

This study involved a data collection period from June 2010 to March 2023, including 374 subjects from two cohorts. The Parkinson's Progression Markers Initiative (PPMI) served as the training set, with control group and PD patients (HC: 102 and PD: 102) from 24 global sites. Our institution's data was utilized as the test set (HC: 91 and PD: 79). Machine learning was employed to establish multiple models for PD diagnosis based on texture features of the cerebellum's gray and white matter. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP).

Results

The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Kruskal-Wallis (KW) and logistic regression via Lasso (LRLasso), the AUC was highest using the "one-standard error" rule. 'WM_original_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature.

Conclusion

The texture features of cerebellar gray matter and white matter combined with machine learning may have potential value in the diagnosis of Parkinson's disease, in which the heterogeneity of white matter may be a more valuable imaging marker.

Predictive Value of Troponin I, Creatinine Kinase Isoenzyme and the New Japanese Severity Score in Severe Acute Pancreatitis

Purpose

To evaluate troponin I, creatine kinase isoenzyme, and the new Japanese Severity Score(JSS) for predicting Severe Acute Pancreatitis-Associated myocardial Injury(SACI).

Patients and Methods

This retrospective study included 136 patients with Severe Acute Pancreatitis, hospitalized in grade-III hospital from June 1, 2015, to October 31, 2022; selected using convenience sampling method and divided into SACI occurrence (n =34) and SACI non-occurrence (n =102) groups. New JSS evaluated predictive value of each SACI index. Binary logistic regression model compared risk factors and constructed a prediction model. Area under receiver operating characteristic curve (AUC) and Hosmer-Lemeshow goodness of fit test evaluated model's prediction efficiency and calibration ability.

Results

The incidence of SACI was 25%. Univariate analysis found that troponin I and creatine kinase isoenzyme were significantly different (P < 0.05) and independent risk factors for SACI. The new JSS, troponin I, and creatine kinase isoenzyme were included in the prediction model. The prediction model had a good calibration ability, and its predicted value and the actual observed value were not significantly different (Hosmer-Lemeshow χ2 = 5.408, P = 0.368). AUC of the model was 0.803 (95% CI: 0.689-0.918), and the optimal threshold of the prediction model was 0.318 with the maximum Youden index (0.488). The AUC for internal validation was 0.788 (95% CI: 0.657-0.876), and external validation was 0.761 (95% CI: 0.622-0.832).

Conclusion

Troponin I and creatine kinase isoenzymes combined with the new JSS have a high predictive value for SACI, improving the early prediction and treatment of at-risk patients.

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.

Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning

OBJECTIVE

Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL).

METHODS

In this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected. First, we trained Model α based on clinical features selected by least absolute shrinkage and selection operator analysis. Second, radiomics features were extracted from 3D-CT scans and Model β was developed on the features after dimensionality reduction using principal component analysis. Third, Model γ was trained on 2D-CT images. Lastly, a multimodal model, namely PrismSAP, was constructed based on aforementioned features in the training set. The predictive accuracy of PrismSAP was verified in the validation and internal test sets and further validated in the external test set. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, recall, precision and F1-score.

RESULTS

A total of 1,221 eligible patients were randomly split into a training set (n = 864), a validation set (n = 209) and an internal test set (n = 148). Data of 266 patients were for external testing. In the external test set, PrismSAP performed best with the highest AUC of 0.916 (0.873-0.960) among all models [Model α: 0.709 (0.618-0.800); Model β: 0.749 (0.675-0.824); Model γ: 0.687 (0.592-0.782); MCTSI: 0.778 (0.698-0.857); RANSON: 0.642 (0.559-0.725); BISAP: 0.751 (0.668-0.833); SABP: 0.710 (0.621-0.798)].

CONCLUSION

The proposed multimodal model outperformed any single-modality models and traditional scoring systems.

Predicting the risk of early intensive care unit admission for patients hospitalized with acute pancreatitis using supervised machine learning

BACKGROUND

Acute pancreatitis (AP) is a complex and life-threatening disease. Early recognition of factors predicting morbidity and mortality is crucial. We aimed to develop and validate a pragmatic model to predict the individualized risk of early intensive care unit (ICU) admission for patients with AP.

METHODS

The 2019 Nationwide Readmission Database was used to identify patients hospitalized with a primary diagnosis of AP without ICU admission. A matched comparison cohort of AP patients with ICU admission within 7 days of hospitalization was identified from the National Inpatient Sample after 1:N propensity score matching. The least absolute shrinkage and selection operator (LASSO) regression was used to select predictors and develop an ICU acute pancreatitis risk (IAPR) score validated by 10-fold cross-validation.

RESULTS

A total of 1513 patients hospitalized for AP were included. The median age was 50.0 years (interquartile range: 39.0-63.0). The three predictors that were selected included hypoxia (area under the curve [AUC] 0.78), acute kidney injury (AUC 0.72), and cardiac arrhythmia (AUC 0.61). These variables were used to develop a nomogram that displayed excellent discrimination (AUC 0.874) (bootstrap bias-corrected 95% confidence interval 0.824-0.876). There was no evidence of miscalibration (test statistic = 2.88; P = 0.09). For high-risk patients (total score >6 points), the sensitivity was 68.94% and the specificity was 92.66%.

CONCLUSIONS

This supervised machine learning-based model can help recognize high-risk AP hospitalizations. Clinicians may use the IAPR score to identify patients with AP at high risk of ICU admission within the first week of hospitalization.

Recent Treatment Strategies for Acute Pancreatitis

Acute pancreatitis (AP) is a leading gastrointestinal disease that causes hospitalization. Initial management in the first 72 h after the diagnosis of AP is pivotal, which can influence the clinical outcomes of the disease. Initial management, including assessment of disease severity, fluid resuscitation, pain control, nutritional support, antibiotic use, and endoscopic retrograde cholangiopancreatography (ERCP) in gallstone pancreatitis, plays a fundamental role in AP treatment. Recent updates for fluid resuscitation, including treatment goals, the type, rate, volume, and duration, have triggered a paradigm shift from aggressive hydration with normal saline to goal-directed and non-aggressive hydration with lactated Ringer's solution. Evidence of the clinical benefit of early enteral feeding is becoming definitive. The routine use of prophylactic antibiotics is generally limited, and the procalcitonin-based algorithm of antibiotic use has recently been investigated to distinguish between inflammation and infection in patients with AP. Although urgent ERCP (within 24 h) should be performed for patients with gallstone pancreatitis and cholangitis, urgent ERCP is not indicated in patients without cholangitis. The management approach for patients with local complications of AP, particularly those with infected necrotizing pancreatitis, is discussed in detail, including indications, timing, anatomical considerations, and selection of intervention methods. Furthermore, convalescent treatment, including cholecystectomy in gallstone pancreatitis, lipid-lowering medications in hypertriglyceridemia-induced AP, and alcohol intervention in alcoholic pancreatitis, is also important for improving the prognosis and preventing recurrence in patients with AP. This review focuses on recent updates on the initial and convalescent management strategies for AP.

Nomogram and Web Calculator Based on Lasso-Logistic Regression for Predicting Persistent Organ Failure in Acute Pancreatitis Patients

PURPOSE

Acute pancreatitis is a common gastrointestinal emergency. Approximately 20% of patients with acute pancreatitis develop organ failure, which is significantly associated with adverse outcomes. This study aimed to establish an early prediction model for persistent organ failure in acute pancreatitis patients using 24-hour admission indicators.

PATIENTS AND METHODS

Clinical data and 24-h laboratory indicators of patients diagnosed with acute pancreatitis from January 1, 2017 to January 1, 2022 in Shanxi Bethune Hospital were collected. Patients from 2017 to 2021 were used as the training cohort to establish the prediction model, and patients from 2021 to 2022 were used as the validation cohort. Univariate logistic regression and LASSO regression were used to establish prediction models. The performance of the model was evaluated using area under the curve (AUC), calibration curves, and decision curve analysis (DCA), and subsequently validated in the validation group.

RESULTS

A total of 1166 patients with acute pancreatitis were included, a total of 145 patients suffered from persistent organ failure from 2017 to 2021. Data were initially selected for 100 variables, and after inclusion and exclusion, 46 variables were used for further analysis. Two prediction models were established and nomogram was drawn respectively. After comparison, the prediction values of the two models were similar (The univariate model AUC was 0.867, 95% CI (0.834-0.9). The LASSO model AUC was 0.864, 95% CI (0.828-0.895)), and the model established by LASSO regression was more parsimonious. A web calculator was developed using the model established by LASSO.

CONCLUSION

Predictive model including 6 risk indicators can be used to predict the risk of persistent organ failure in patients with acute pancreatitis.

Predictive value of adipokines for the severity of acute pancreatitis: a meta-analysis

BACKGROUND

Severe acute pancreatitis (SAP) is a dangerous condition with a high mortality rate. Many studies have found an association between adipokines and the development of SAP, but the results are controversial. Therefore, we performed a meta-analysis of the association of inflammatory adipokines with SAP.

METHODS

We screened PubMed, EMBASE, Web of Science and Cochrane Library for articles on adipokines and SAP published before July 20, 2023. The quality of the literature was assessed using QUADAS criteria. Standardized mean differences (SMD) with 95% confidence intervals (CI) were calculated to assess the combined effect. Subgroup analysis, sensitivity analysis and publication bias tests were also performed on the information obtained.

RESULT

Fifteen eligible studies included 1332 patients with acute pancreatitis (AP). Pooled analysis showed that patients with SAP had significantly higher serum levels of resistin (SMD = 0.78, 95% CI:0.37 to 1.19, z = 3.75, P = 0.000). The difference in leptin and adiponectin levels between SAP and mild acute pancreatitis (MAP) patients were not significant (SMD = 0.30, 95% CI: -0.08 to 0.68, z = 1.53, P = 0.127 and SMD = 0.11, 95% CI: -0.17 to 0.40, z = 0.80, P = 0.425, respectively). In patients with SAP, visfatin levels were not significantly different from that in patients with MAP (SMD = 1.20, 95% CI: -0.48 to 2.88, z = 1.40, P = 0.162).

CONCLUSION

Elevated levels of resistin are associated with the development of SAP. Resistin may serve as biomarker for SAP and has promise as therapeutic target.

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.

Acute pancreatitis: A review of diagnosis, severity prediction and prognosis assessment from imaging technology, scoring system and artificial intelligence

Acute pancreatitis (AP) is a potentially life-threatening inflammatory disease of the pancreas, with clinical management determined by the severity of the disease. Diagnosis, severity prediction, and prognosis assessment of AP typically involve the use of imaging technologies, such as computed tomography, magnetic resonance imaging, and ultrasound, and scoring systems, including Ranson, Acute Physiology and Chronic Health Evaluation II, and Bedside Index for Severity in AP scores. Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity, while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications. Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild, moderate, or severe categories, guiding treatment decisions, such as intensive care unit admission, early enteral feeding, and antibiotic use. Despite the central role of imaging technologies and scoring systems in AP management, these methods have limitations in terms of accuracy, reproducibility, practicality and economics. Recent advancements of artificial intelligence (AI) provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data. AI algorithms can analyze large amounts of clinical and imaging data, identify scoring system patterns, and predict the clinical course of disease. AI-based models have shown promising results in predicting the severity and mortality of AP, but further validation and standardization are required before widespread clinical application. In addition, understanding the correlation between these three technologies will aid in developing new methods that can accurately, sensitively, and specifically be used in the diagnosis, severity prediction, and prognosis assessment of AP through complementary advantages.

Diabetes as a consequence of acute pancreatitis

Diabetes is a highly prevalent disease that was initially simplified into three major types: Type 1, type 2 and gestational diabetes. With the global rise in incidence of acute pancreatitis (AP), a lesser-known type of diabetes referred to as diabetes of the exocrine pancreas (DEP) is becoming more recognized. However, there is a poor understanding of the inherent relationship between diabetes and AP. There is established data about certain diseases affecting the exocrine function of the pancreas which can lead to diabetes. More specifically, there are well established guidelines for diagnosis and management of DEP caused be chronic pancreatitis. Conversely, the sequelae of AP leading to diabetes has limited recognition and data. The purpose of this review is to provide a comprehensive summary of the prevalence, epidemiology, pathophysiology and future research aims of AP-related diabetes. In addition, we propose a screening and diagnostic algorithm to aid clinicians in providing better care for their patients.

Artificial intelligence assisted whole organ pancreatic fat estimation on magnetic resonance imaging and correlation with pancreas attenuation on computed tomography

BACKGROUND

Fatty pancreas is associated with inflammatory and neoplastic pancreatic diseases. Magnetic resonance imaging (MRI) is the diagnostic modality of choice for measuring pancreatic fat. Measurements typically use regions of interest limited by sampling and variability. We have previously described an artificial intelligence (AI)-aided approach for whole pancreas fat estimation on computed tomography (CT). In this study, we aimed to assess the correlation between whole pancreas MRI proton-density fat fraction (MR-PDFF) and CT attenuation.

METHODS

We identified patients without pancreatic disease who underwent both MRI and CT between January 1, 2015 and June 1, 2020. 158 paired MRI and CT scans were available for pancreas segmentation using an iteratively trained convolutional neural network (CNN) with manual correction. Boxplots were generated to visualize slice-by-slice variability in 2D-axial slice MR-PDFF. Correlation between whole pancreas MR-PDFF and age, BMI, hepatic fat and pancreas CT-Hounsfield Unit (CT-HU) was assessed.

RESULTS

Mean pancreatic MR-PDFF showed a strong inverse correlation (Spearman -0.755) with mean CT-HU. MR-PDFF was higher in males (25.22 vs 20.87; p = 0.0015) and in subjects with diabetes mellitus (25.95 vs 22.17; p = 0.0324), and was positively correlated with age and BMI. The pancreatic 2D-axial slice-to-slice MR-PDFF variability increased with increasing mean whole pancreas MR-PDFF (Spearman 0.51; p < 0.0001).

CONCLUSION

Our study demonstrates a strong inverse correlation between whole pancreas MR-PDFF and CT-HU, indicating that both imaging modalities can be used to assess pancreatic fat. 2D-axial pancreas MR-PDFF is variable across slices, underscoring the need for AI-aided whole-organ measurements for objective and reproducible estimation of pancreatic fat.

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).

To infer the probability of cervical ossification of the posterior longitudinal ligament and explore its impact on cervical surgery

The ossification of the posterior longitudinal ligament (OPLL) in the cervical spine is commonly observed in degenerative changes of the cervical spine. Early detection of cervical OPLL and prevention of postoperative complications are of utmost importance. We gathered data from 775 patients who underwent cervical spine surgery at the First Affiliated Hospital of Guangxi Medical University, collecting a total of 84 variables. Among these patients, 144 had cervical OPLL, while 631 did not. They were randomly divided into a training cohort and a validation cohort. Multiple machine learning (ML) methods were employed to screen the variables and ultimately develop a diagnostic model. Subsequently, we compared the postoperative outcomes of patients with positive and negative cervical OPLL. Initially, we compared the advantages and disadvantages of various ML methods. Seven variables, namely Age, Gender, OPLL, AST, UA, BMI, and CHD, exhibited significant differences and were used to construct a diagnostic nomogram model. The area under the curve (AUC) values of this model in the training and validation groups were 0.76 and 0.728, respectively. Our findings revealed that 69.2% of patients who underwent cervical OPLL surgery eventually required elective anterior surgery, in contrast to 86.8% of patients who did not have cervical OPLL. Patients with cervical OPLL had significantly longer operation times and higher postoperative drainage volumes compared to those without cervical OPLL. Interestingly, preoperative cervical OPLL patients demonstrated significant increases in mean UA, age, and BMI. Furthermore, 27.1% of patients with cervical anterior longitudinal ligament ossification (OALL) also exhibited cervical OPLL, whereas this occurrence was only observed in 6.9% of patients without cervical OALL. We developed a diagnostic model for cervical OPLL using the ML method. Our findings indicate that patients with cervical OPLL are more likely to undergo posterior cervical surgery, and they exhibit elevated UA levels, higher BMI, and increased age. The prevalence of cervical anterior longitudinal ligament ossification was also significantly higher among patients with cervical OPLL.

A modified Ranson score to predict disease severity, organ failure, pancreatic necrosis, and pancreatic infection in patients with acute pancreatitis

Background

Although there are several scoring systems currently used to predict the severity of acute pancreatitis, each of them has limitations. Determine the accuracy of a modified Ranson score in predicting disease severity and prognosis in patients with acute pancreatitis (AP).

Methods

AP patients admitted or transferred to our institution were allocated to a modeling group (n = 304) or a validation group (n = 192). A modified Ranson score was determined by excluding the fluid sequestration parameter and including the modified computed tomography severity index (CTSI). The diagnostic performance of the modified Ranson score was compared with the Ranson score, modified CTSI, and bedside index of severity in acute pancreatitis (BISAP) score in predicting disease severity, organ failure, pancreatic necrosis and pancreatic infection.

Results

The modified Ranson score had significantly better accuracy that the Ranson score in predicting all four outcome measures in the modeling group and in the validation group (all p < 0.05). For the modeling group the modified Ranson score had the best accuracy for predicting disease severity and organ failure, and second-best accuracy for predicting pancreatic necrosis and pancreatic infection. For the verification group, it had the best accuracy for predicting organ failure, second-best accuracy for predicting disease severity and pancreatic necrosis, and third-best accuracy for predicting pancreatic infection.

Conclusion

The modified Ranson score provided better accuracy than the Ranson score in predicting disease severity, organ failure, pancreatic necrosis and pancreatic infection. Relative to the other scoring systems, the modified Ranson system was superior in predicting organ failure.

A Metabolism-Based Interpretable Machine Learning Prediction Model for Diabetic Retinopathy Risk: A Cross-Sectional Study in Chinese Patients with Type 2 Diabetes

The burden of diabetic retinopathy (DR) is increasing, and the sensitive biomarkers of the disease were not enough. Studies have found that the metabolic profile, such as amino acid (AA) and acylcarnitine (AcylCN), in the early stages of DR patients might have changed, indicating the potential of metabolites to become new biomarkers. We are amid to construct a metabolite-based prediction model for DR risk. This study was conducted on type 2 diabetes (T2D) patients with or without DR. Logistic regression and extreme gradient boosting (XGBoost) prediction models were constructed using the traditional clinical features and the screening features, respectively. Assessing the predictive power of the models in terms of both discrimination and calibration, the optimal model was interpreted using the Shapley Additive exPlanations (SHAP) to quantify the effect of features on prediction. Finally, the XGBoost model incorporating AA and AcylCN variables had the best comprehensive evaluation (ROCAUC = 0.82, PRAUC = 0.44, Brier score = 0.09). C18 : 1OH lower than 0.04 μmol/L, C18 : 1 lower than 0.70 μmol/L, threonine higher than 27.0 μmol/L, and tyrosine lower than 36.0 μmol/L were associated with an increased risk of developing DR. Phenylalanine higher than 52.0 μmol/L was associated with a decreased risk of developing DR. In conclusion, our study mainly used AAs and AcylCNs to construct an interpretable XGBoost model to predict the risk of developing DR in T2D patients which is beneficial in identifying high-risk groups and preventing or delaying the onset of DR. In addition, our study proposed possible risk cut-off values for DR of C18 : 1OH, C18 : 1, threonine, tyrosine, and phenylalanine.

Current trends in acute pancreatitis: Diagnostic and therapeutic challenges

Acute pancreatitis (AP) is an inflammatory disease of the pancreas, which can progress to severe AP, with a high risk of death. It is one of the most complicated and clinically challenging of all disorders affecting the abdomen. The main causes of AP are gallstone migration and alcohol abuse. Other causes are uncommon, controversial and insufficiently explained. The disease is primarily characterized by inappropriate activation of trypsinogen, infiltration of inflammatory cells, and destruction of secretory cells. According to the revised Atlanta classification, severity of the disease is categorized into three levels: Mild, moderately severe and severe, depending upon organ failure and local as well as systemic complications. Various methods have been used for predicting the severity of AP and its outcome, such as clinical evaluation, imaging evaluation and testing of various biochemical markers. However, AP is a very complex disease and despite the fact that there are of several clinical, biochemical and imaging criteria for assessment of severity of AP, it is not an easy task to predict its subsequent course. Therefore, there are existing controversies regarding diagnostic and therapeutic modalities, their effectiveness and complications in the treatment of AP. The main reason being the fact, that the pathophysiologic mechanisms of AP have not been fully elucidated and need to be studied further. In this editorial article, we discuss the efficacy of the existing diagnostic and therapeutic modalities, complications and treatment failure in the management of AP.

Metabolic-associated fatty liver disease is associated with acute pancreatitis with more severe course: Post hoc analysis of a prospectively collected international registry

INTRODUCTION

Non-alcoholic fatty liver disease (NAFLD) is a proven risk factor for acute pancreatitis (AP). However, NAFLD has recently been redefined as metabolic-associated fatty liver disease (MAFLD). In this post hoc analysis, we quantified the effect of MAFLD on the outcomes of AP.

METHODS

We identified our patients from the multicentric, prospective International Acute Pancreatitis Registry of the Hungarian Pancreatic Study Group. Next, we compared AP patients with and without MAFLD and the individual components of MAFLD regarding in-hospital mortality and AP severity based on the revised Atlanta classification. Lastly, we calculated odds ratios (ORs) with 95% confidence intervals (CIs) using multivariate logistic regression analysis.

RESULTS

MAFLD had a high prevalence in AP, 39% (801/2053). MAFLD increased the odds of moderate-to-severe AP (OR = 1.43, CI: 1.09-1.89). However, the odds of in-hospital mortality (OR = 0.89, CI: 0.42-1.89) and severe AP (OR = 1.70, CI: 0.97-3.01) were not higher in the MAFLD group. Out of the three diagnostic criteria of MAFLD, the highest odds of severe AP was in the group based on metabolic risk abnormalities (OR = 2.68, CI: 1.39-5.09). In addition, the presence of one, two, and three diagnostic criteria dose-dependently increased the odds of moderate-to-severe AP (OR = 1.23, CI: 0.88-1.70, OR = 1.38, CI: 0.93-2.04, and OR = 3.04, CI: 1.63-5.70, respectively) and severe AP (OR = 1.13, CI: 0.54-2.27, OR = 2.08, CI: 0.97-4.35, and OR = 4.76, CI: 1.50-15.4, respectively). Furthermore, in patients with alcohol abuse and aged ≥60 years, the effect of MAFLD became insignificant.

CONCLUSIONS

MAFLD is associated with AP severity, which varies based on the components of its diagnostic criteria. Furthermore, MAFLD shows a dose-dependent effect on the outcomes of AP.

Machine learning for the prediction of post-ERCP pancreatitis risk: A proof-of-concept study

BACKGROUND

Predicting Post-Endoscopic Retrograde Cholangiopancreatography (ERCP) pancreatitis (PEP) risk can be determinant in reducing its incidence and managing patients appropriately, however studies conducted thus far have identified single-risk factors with standard statistical approaches and limited accuracy.

AIM

To build and evaluate performances of machine learning (ML) models to predict PEP probability and identify relevant features.

METHODS

A proof-of-concept study was performed on ML application on an international, multicenter, prospective cohort of ERCP patients. Data were split in training and test set, models used were gradient boosting (GB) and logistic regression (LR). A 10-split random cross-validation (CV) was applied on the training set to optimize parameters to obtain the best mean Area Under Curve (AUC). The model was re-trained on the whole training set with the best parameters and applied on test set. Shapley-Additive-exPlanation (SHAP) approach was applied to break down the model and clarify features impact.

RESULTS

One thousand one hundred and fifty patients were included, 6.1% developed PEP. GB model outperformed LR with AUC in CV of 0.7 vs 0.585 (p-value=0.012). GB AUC in test was 0.671. Most relevant features for PEP prediction were: bilirubin, age, body mass index, procedure time, previous sphincterotomy, alcohol units/day, cannulation attempts, gender, gallstones, use of Ringer's solution and periprocedural NSAIDs.

CONCLUSION

In PEP prediction, GB significantly outperformed LR model and identified new clinical features relevant for the risk, most being pre-procedural.

No evidence for the benefit of PPIs in the treatment of acute pancreatitis: a systematic review and meta-analysis

Although current guidelines do not recommend the use of proton pump inhibitors (PPIs) in the standard of care of acute pancreatitis (AP), they are often prescribed in clinical practice, mainly for ulcer stress prophylaxis. In this systematic review and meta-analysis we evaluated the association between the use of PPIs in the management of AP and various clinical outcomes. We conducted the systematic research in six databases without restrictions on January 24th, 2022. We investigated adult patient with AP, who were treated with PPI compared to conventional therapy. The pooled odds ratios, mean differences, and corresponding 95% confidence intervals were calculated with random effect model. We included six RCTs and three cohort studies, consisting of 28,834 patients. We found a significant decrease in the rate of pancreatic pseudocyst formation in patients who received PPI treatment. PPI use was associated with a higher risk of GI bleeding, however this finding could be due to the patients' comorbid conditions. We found no significant difference in the rates of 7-day mortality, length of hospital stay, and acute respiratory distress syndrome between the groups. The available data on this topic are limited; therefore, further well designed RCTs are needed to evaluate the potential benefits and adverse effects of PPIs in AP.

Characteristics and risk factors for extrapancreatic infection in patients with moderate or severe acute pancreatitis

BACKGROUND

How to detect acute pancreatitis (AP) complicated with infection early and how to arrange the treatment time are still the main problems in the world. There are few reports on the potential relationship between extrapancreatic infections and AP. The purpose of this article was to investigate the characteristics, influencing factors and prognosis of extrapancreatic infection in AP patients with modified Marshall score ≥2 on admission.

MATERIALS AND METHODS

We retrospectively analyzed AP admitted to emergency intensive care unit of Ruijin hospital within 72 h of onset from September 2019 to December 2021. In addition to the patients' baseline data, sites of infection and microorganisms outside the pancreas were collected. Microbial cultures were used to identify infections of the respiratory tract, blood, abdominal cavity, biliary tract, urinary tract and clostridium difficile in feces.

RESULTS

144 patients with AP were included, of which extrapancreatic infection accounted for 40.28%. C-reactive protein, procalcitonin, blood urea nitrogen, serum creatinine, oxygenation index, modified Marshall score, BISAP score and APACHE II score were significantly increased in the extrapancreatic infection group. The risk factors of extrapancreatic infection included blood urea nitrogen, Modified Marshall score and duration of mechanical ventilation. The positive rates of pathogenic bacteria in sputum culture, blood culture, ascites culture and bile culture were significantly higher than those in the 1-3 days after admission. The infection begins to worsen as early as 4-7 days after the onset of symptoms. Extrapancreatic infection is associated with pancreatic necrosis, the rate of laparotomy, length of hospital stay and in-hospital mortality.

CONCLUSION

Our research has confirmed the need to prevent and monitor extrapancreatic infection in the early stage.

Acute Pancreatitis Severity Prediction: It Is Time to Use Artificial Intelligence

The clinical course of acute pancreatitis (AP) can be variable depending on the severity of the disease, and it is crucial to predict the probability of organ failure to initiate early adequate treatment and management. Therefore, possible high-risk patients should be admitted to a high-dependence unit. For risk assessment, we have three options: (1) There are univariate biochemical markers for predicting severe AP. One of their main characteristics is that the absence or excess of these factors affects the outcome of AP in a dose-dependent manner. Unfortunately, all of these parameters have low accuracy; therefore, they cannot be used in clinical settings. (2) Score systems have been developed to prognosticate severity by using 4-25 factors. They usually require multiple parameters that are not measured on a daily basis, and they often require more than 24 h for completion, resulting in the loss of valuable time. However, these scores can foresee specific organ failure or severity, but they only use dichotomous parameters, resulting in information loss. Therefore, their use in clinical settings is limited. (3) Artificial intelligence can detect the complex nonlinear relationships between multiple biochemical parameters and disease outcomes. We have recently developed the very first easy-to-use tool, EASY-APP, which uses multiple continuous variables that are available at the time of admission. The web-based application does not require all of the parameters for prediction, allowing early and easy use on admission. In the future, prognostic scores should be developed with the help of artificial intelligence to avoid information loss and to provide a more individualized risk assessment.

The Potential Role of Model for End-Stage Liver Disease (MELD)-Sodium Score in Predicting the Severity of Acute Pancreatitis

Background and aim Acute pancreatitis is a common inflammation of the pancreas which can be severe and even potentially mortal. High rates of mortality showed the importance of immediate identification of patients at high risk and led the clinicians to refer to various scoring systems. Our aim was to investigate a clinical predictive model using the Model for End-Stage Liver Disease-Sodium (MELD-sodium) scoring system, adapting it to acute pancreatitis patients referring to the systemic inflammatory nature of the disease and potential multi-organ failures in severe form. Methods Our multicenter study was designed retrospectively. The medical records were reviewed for the period of two years. Demographics, biochemical results, MELD-sodium scores and mortality rates were analysed. Results MELD-sodium score was found to be statistically correlated with both mortality and the severity of pancreatitis (p<0.001) and significant difference between both mild and severe (p<0.001), moderate and severe groups (p<0.001). Mortality was found to be significantly higher in patients with MELD-Na score when the cut-off value was accepted as '≥11'. Conclusion We found that MELD-sodium score was significantly associated with both severity of disease and mortality rates and also significantly effective between both mild/severe and moderate/severe groups which may be a guide for future multi-center reviews with larger patient and control groups, which can define the potential role of this non-invasive and easy-to-use predictive model in acute pancreatitis patients.

Stmol: A component for building interactive molecular visualizations within streamlit web-applications

Streamlit is an open-source Python coding framework for building web-applications or "web-apps" and is now being used by researchers to share large data sets from published studies and other resources. Here we present Stmol, an easy-to-use component for rendering interactive 3D molecular visualizations of protein and ligand structures within Streamlit web-apps. Stmol can render protein and ligand structures with just a few lines of Python code by utilizing popular visualization libraries, currently Py3DMol and Speck. On the user-end, Stmol does not require expertise to interactively navigate. On the developer-end, Stmol can be easily integrated within structural bioinformatic and cheminformatic pipelines to provide a simple means for user-end researchers to advance biological studies and drug discovery efforts. In this paper, we highlight a few examples of how Stmol has already been utilized by scientific communities to share interactive molecular visualizations of protein and ligand structures from known open databases. We hope Stmol will be used by researchers to build additional open-sourced web-apps to benefit current and future generations of scientists.

The Pancreas and Known Factors of Acute Pancreatitis

Pancreatitis is regarded by clinicians as one of the most complicated and clinically challenging of all disorders affecting the abdomen. It is classified on the basis of clinical, morphological, and histological criteria. Causes of acute pancreatitis can easily be identified in 75–85% of patients. The main causes of acute, recurrent acute, and chronic pancreatitis are gallstone migration and alcohol abuse. Other causes are uncommon, controversial, or unexplained. For instance, cofactors of all forms of pancreatitis are pancreas divisum and hypertriglyceridemia. Another factor that should be considered is a complication of endoscopic retrograde cholangiopancreatography: post-endoscopic retrograde cholangiopancreatography acute pancreatitis. The aim of this study is to present the known risk factors for acute pancreatitis, beginning with an account of the morphology, physiology, and development of the pancreas.

Acute Pancreatitis: Diagnosis and Treatment

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

Development and validation of a risk prediction score for the severity of acute hypertriglyceridemic pancreatitis in Chinese patients

BACKGROUND

The frequency of acute hypertriglyceridemic pancreatitis (AHTGP) is increasing worldwide. AHTGP may be associated with a more severe clinical course and greater mortality than pancreatitis caused by other causes. Early identification of patients with severe inclination is essential for clinical decision-making and improving prognosis. Therefore, we first developed and validated a risk prediction score for the severity of AHTGP in Chinese patients.

AIM

To develop and validate a risk prediction score for the severity of AHTGP in Chinese patients.

METHODS

We performed a retrospective study including 243 patients with AHTGP. Patients were randomly divided into a development cohort (n = 170) and a validation cohort (n = 73). Least absolute shrinkage and selection operator and logistic regression were used to screen 42 potential predictive variables to construct a risk score for the severity of AHTGP. We evaluated the performance of the nomogram and compared it with existing scoring systems. Last, we used the best cutoff value (88.16) for severe acute pancreatitis (SAP) to determine the risk stratification classification.

RESULTS

Age, the reduction in apolipoprotein A1 and the presence of pleural effusion were independent risk factors for SAP and were used to construct the nomogram (risk prediction score referred to as AAP). The concordance index of the nomogram in the development and validation groups was 0.930 and 0.928, respectively. Calibration plots demonstrate excellent agreement between the predicted and actual probabilities in SAP patients. The area under the curve of the nomogram (0.929) was better than those of the Bedside Index of Severity in AP (BISAP), Ranson, Acute Physiology and Chronic Health Evaluation (APACHE II), modified computed tomography severity index (MCTSI), and early achievable severity index scores (0.852, 0.825, 0.807, 0.831 and 0.807, respectively). In comparison with these scores, the integrated discrimination improvement and decision curve analysis showed improved accuracy in predicting SAP and better net benefits for clinical decisions. Receiver operating characteristic curve analysis was used to determine risk stratification classification for AHTGP by dividing patients into high-risk and low-risk groups according to the best cutoff value (88.16). The high-risk group (> 88.16) was closely related to the appearance of local and systemic complications, Ranson score ≥ 3, BISAP score ≥ 3, MCTSI score ≥ 4, APACHE II score ≥ 8, C-reactive protein level ≥ 190, and length of hospital stay.

CONCLUSION

The nomogram could help identify AHTGP patients who are likely to develop SAP at an early stage, which is of great value in guiding clinical decisions.

Combination of the BISAP Score and miR-155 is Applied in Predicting the Severity of Acute Pancreatitis

Purpose

To evaluate the predictive value of combination of Bedside Index for Severity in AP (BISAP) score and miR-155 for the severity of acute pancreatitis (AP).

Patients and Methods

A total of 1046 AP patients were divided into control group and case group according to the severity of AP [mild and moderately severe AP vs severe AP (SAP)]. Demographic data, comorbidities, clinical characteristics and laboratory data were collected. Multivariate analysis was conducted for the variables with two-sided P<0.10 in univariate analysis to identify independent associated factors for progression to SAP in AP patients. The predictive values were evaluated using receiver operating characteristic (ROC) curve, and the area under curve (AUC) was compared using Z test.

Results

A total of 117 (11.2%) patients were evaluated as SAP. Univariate analysis showed that there were significant differences in age, hypertension, ICU admission, hospital stay, Leukocytes, CRP, BUN, BISAP score and miR-155 between case group and control group (P<0.05), and the P value of Fibrinogen was <0.10. Multivariate analysis showed that the BISAP score, BUN, Leukocytes, age and CRP were independent risk factors for progression to SAP among AP patients after adjusting for hypertension, ICU admission, hospital stay and Fibrinogen, while miR-155 was a protective factor. The ROC curves demonstrated the AUCs of BISAP score, miR-155 and their combination were 0.842 (SE: 0.017, 95% CI: 0.809–0.874), 0.751 (SE: 0.022, 95% CI: 0.708–0.793) and 0.945 (SE: 0.007, 95% CI: 0.931–0.959), respectively. Z test showed that the AUC of combination prediction was significantly higher than that of individual predictions (0.945 vs 0.842, Z=5.602, P<0.001; 0.945 vs 0.751, Z=8.403, P<0.001). The sensitivity, specificity and negative predictive value (NPV) of combination prediction were 95.7%, 93.6% and 99.4%, respectively.

Conclusion

The combination of the BISAP score and miR-155 should be utilized to elevate the predictive value for the severity of AP in clinic.

Diagnosis and Treatment of Acute Pancreatitis

The pancreas is a glandular organ that is responsible for the proper functioning of the digestive and endocrine systems, and therefore, it affects the condition of the entire body. Consequently, it is important to effectively diagnose and treat diseases of this organ. According to clinicians, pancreatitis—a common disease affecting the pancreas—is one of the most complicated and demanding diseases of the abdomen. The classification of pancreatitis is based on clinical, morphologic, and histologic criteria. Medical doctors distinguish, inter alia, acute pancreatitis (AP), the most common causes of which are gallstone migration and alcohol abuse. Effective diagnostic methods and the correct assessment of the severity of acute pancreatitis determine the selection of an appropriate treatment strategy and the prediction of the clinical course of the disease, thus preventing life-threatening complications and organ dysfunction or failure. This review collects and organizes recommendations and guidelines for the management of patients suffering from acute pancreatitis.