Early prediction of acute necrotizing pancreatitis by artificial intelligence : a prospective cohort-analysis of 2387 cases

Publisher Copyright: © 2022, The Author(s).Pancreatic necrosis is a consistent prognostic factor in acute pancreatitis (AP). However, the clinical scores currently in use are either too complicated or require data that are unavailable on admission or lack sufficient predictive value. We therefore aimed to develop a tool to aid in necrosis prediction. The XGBoost machine learning algorithm processed data from 2387 patients with AP. The confidence of the model was estimated by a bootstrapping method and interpreted via the 10th and the 90th percentiles of the prediction scores. Shapley Additive exPlanations (SHAP) values were calculated to quantify the contribution of each variable provided. Finally, the model was implemented as an online application using the Streamlit Python-based framework. The XGBoost classifier provided an AUC value of 0.757. Glucose, C-reactive protein, alkaline phosphatase, gender and total white blood cell count have the most impact on prediction based on the SHAP values. The relationship between the size of the training dataset and model performance shows that prediction performance can be improved. This study combines necrosis prediction and artificial intelligence. The predictive potential of this model is comparable to the current clinical scoring systems and has several advantages over them.Peer reviewe

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

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.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.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.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

Inflammatory bowel disease does not alter the clinical features and the management of acute pancreatitis: A prospective, multicentre, exact-matched cohort analysis

Acute pancreatitis in inflammatory bowel disease occurs mainly as an extraintestinal manifestation or a side effect of medications. We aimed to investigate the prognostic factors and severity indicators of acute pancreatitis and the treatment of patients with both diseases.We performed a matched case-control registry analysis of a multicentre, prospective, international acute pancreatitis registry. Patients with both diseases were matched to patients with acute pancreatitis only in a 1:3 ratio by age and gender. Subgroup analyses were also carried out based on disease type, activity, and treatment of inflammatory bowel disease.No difference in prognostic factors (laboratory parameters, bedside index of severity in acute pancreatitis, imaging results) and outcomes of acute pancreatitis (length of hospitalization, severity, and local or systemic complications) were detected between groups. Significantly lower analgesic use was observed in the inflammatory bowel disease population. Antibiotic use during acute pancreatitis was significantly more common in the immunosuppressed group than in the non-immunosuppressed group (p = 0.017). However, none of the prognostic parameters or the severity indicators showed a significant difference between any subgroup of patients with inflammatory bowel disease.No significant differences in the prognosis and severity of acute pancreatitis could be detected between patients with both diseases and with pancreatitis only. The need for different acute pancreatitis management is not justified in the coexistence of inflammatory bowel disease, and antibiotic overuse should be avoided

Early prediction of acute necrotizing pancreatitis by artificial intelligence : a prospective cohort-analysis of 2387 cases

Pancreatic necrosis is a consistent prognostic factor in acute pancreatitis (AP). However, the clinical scores currently in use are either too complicated or require data that are unavailable on admission or lack sufficient predictive value. We therefore aimed to develop a tool to aid in necrosis prediction. The XGBoost machine learning algorithm processed data from 2387 patients with AP. The confidence of the model was estimated by a bootstrapping method and interpreted via the 10th and the 90th percentiles of the prediction scores. Shapley Additive exPlanations (SHAP) values were calculated to quantify the contribution of each variable provided. Finally, the model was implemented as an online application using the Streamlit Python-based framework. The XGBoost classifier provided an AUC value of 0.757. Glucose, C-reactive protein, alkaline phosphatase, gender and total white blood cell count have the most impact on prediction based on the SHAP values. The relationship between the size of the training dataset and model performance shows that prediction performance can be improved. This study combines necrosis prediction and artificial intelligence. The predictive potential of this model is comparable to the current clinical scoring systems and has several advantages over them

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

Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed.The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit-learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross-validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross-validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP).The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy-to-use web application in the Streamlit Python-based framework (http://easy-app.org/).The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model

Prospective, Multicentre, Nationwide Clinical Data from 600 Cases of Acute Pancreatitis

OBJECTIVE: The aim of this study was to analyse the clinical characteristics of acute pancreatitis (AP) in a prospectively collected, large, multicentre cohort and to validate the major recommendations in the IAP/APA evidence-based guidelines for the management of AP. DESIGN: Eighty-six different clinical parameters were collected using an electronic clinical research form designed by the Hungarian Pancreatic Study Group. PATIENTS: 600 adult patients diagnosed with AP were prospectively enrolled from 17 Hungarian centres over a two-year period from 1 January 2013. MAIN RESULTS: With respect to aetiology, biliary and alcoholic pancreatitis represented the two most common forms of AP. The prevalence of biliary AP was higher in women, whereas alcoholic AP was more common in men. Hyperlipidaemia was a risk factor for severity, lack of serum enzyme elevation posed a risk for severe AP, and lack of abdominal pain at admission demonstrated a risk for mortality. Abdominal tenderness developed in all the patients with severe AP, while lack of abdominal tenderness was a favourable sign for mortality. Importantly, lung injury at admission was associated with mortality. With regard to laboratory parameters, white blood cell count and CRP were the two most sensitive indicators for severe AP. The most common local complication was peripancreatic fluid, whereas the most common distant organ failure in severe AP was lung injury. Deviation from the recommendations in the IAP/APA evidence-based guidelines on fluid replacement, enteral nutrition and timing of interventions increased severity and mortality. CONCLUSIONS: Analysis of a large, nationwide, prospective cohort of AP cases allowed for the identification of important determinants of severity and mortality. Evidence-based guidelines should be observed rigorously to improve outcomes in AP