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

Acute pancreatitis; Artificial intelligence; Severity predictionPancreatitis aguda; Inteligencia artificial; Predicción de gravedadPancreatitis aguda; Intel·ligència artificial; Predicció de la gravetatBackground 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. Methods 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). Results 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/). Conclusions 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.University of Pécs Medical School Research Fund. Grant Number: 300909. National Research, Development and Innovation Office Research Fund. Grant Numbers: K131996, FK131864, K128222, FK12463

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

Az antibiotikumrezisztencia változása cholangitisben. Klinikai tapasztalataink = Changes in antibiotic resistance in cholangitis. Our clinical experience

Absztrakt: Bevezetés: Az antibiotikumok (AB) nem megfelelő alkalmazása miatt számtalan kórokozó vált multirezisztenssé. Az egyik leggyakoribb kiindulási gócot az epeutak gyulladása képezi. A fatális kimenetel megelőzésében kulcsszerepet játszik a megfelelő AB-politika. Célkitűzés: A cholangitis leggyakoribb kórokozói AB-érzékenységének és a választott empirikus AB-kezelés hatásosságának vizsgálata. Betegek és módszer: Retrospektív kutatásunk során a 2006-os és a 2016-os év folyamán a Szegedi Tudományegyetem Általános Orvostudományi Karának I. Belklinikáján cholangitis indikációval végzett endoszkópos retrográd cholangiopancreatographia (ERCP) során nyert epeminták mikrobiológiai eredményének áttekintése történt. Eredmények: 2006-ban 29, 2016-ban 111 epemintavétel történt, ezekből 22 (75%), illetve 106 (95%) volt pozitív. A betegek átlagéletkora 61 ± 14 vs. 71 ± 14 év, a nemek aránya közel azonos volt. 2006-ban 10 esetben indítottak empirikus AB-ot (ciprofloxacin és metronidazol, illetve imipenem), ezekre 9 esetben (90%) a tenyészett kórokozó érzékeny volt. 2016-ban 88 esetben indítottak AB-ot (ciprofloxacin és metronidazol mellett ceftriaxon és metronidazol, valamint imipenem és metronidazol is szerepelt). 29 esetben az empirikusan indított AB nem volt hatékony. A ciprofloxacin hatékonysága 64%-ra csökkent 2016-ra. A cholangitist okozó leggyakoribb kórokozók típusa (Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae) a két vizsgált évben nem változott, ciprofloxacinrezisztenciájuk azonban növekedett. A polimikrobás infekciók aránya rendre 73% és 64% volt. Következtetés: A pozitív epetenyésztések száma szignifikánsan emelkedett 2016-ban. A leggyakoribb kórokozók típusában nem adódott eltérés. Az empirikusan indított ciprofloxacin antibiotikum hatékonysága csökkent 2016-ban. Eredményeink a cholangitist okozó kórokozók típuseloszlásában megfelelnek a cholangitisre vonatkozó ajánlás (Tokyo Guideline) adatainak. Orv Hetil. 2019; 160(36): 1437–1442. | Abstract: Introduction: Due to the inappropriate use of antibiotics (AB), more pathogens become multiresistant. One of the most severe sources of sepsis is cholangitis. To avoid fatal outcome, an effective AB policy plays a key role. Aim: To investigate the AB resistance of bacteria causing cholangitis and the efficacy of AB treatment. Patients and method: Microbiological tests of bile samples collected during cholangitis-indicated endoscopic retrograde cholangiopancreatographies were analysed at the First Department of Medicine, University of Szeged, in 2006 and in 2016. Results: 29 and 111 patients had bile sample collection in 2006 and in 2016, respectively. Of that, 22 (75%) and 106 (95%) were positive. Mean age: 61 ± 14 vs. 71 ± 14 years, no difference between men/women ratio. In 2006, 10 cases empirical AB (ciprofloxacine with metronidazole or imipenem) were used. In 9 cases (90%), the AB was adequate based on the microbiological results. In 2016, in 88 cases empirical AB was applied (ciprofloxacine and metronidazole, ceftriaxone with metronidazole or imipenem with metronidazole). In 29 cases, the empirical AB was ineffective. The efficacy of ciprofloxacine decreased to 64% in 2016. The profile of the most frequent cholangitis-causing pathogens (Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae) was the same, but their resistency against ciprofloxacine increased. The rates of polymicrobal infections were 73% and 63%, respectively. Conclusion: The rates of positive bile samples were significantly higher in 2016. The profile of the most frequent pathogens was the same. The efficacy of the first-choice empirical AB ciprofloxacine decreased in 2016. The types of the most frequent cholangitis-causing bacteria are in line with the ones included in the Tokyo Guideline. Orv Hetil. 2019; 160(36): 1437–1442

Detailed characteristics of post-discharge mortality in acute pancreatitis

The in-hospital survival of patients suffering from acute pancreatitis (AP) is 95-98%. However, there is growing evidence that patients discharged after AP may be at risk of serious morbidity and mortality. Here, we aimed to investigate the risk, causes, and predictors of the most severe consequence of the post-AP period: mortality.2,613, well-characterized patients from twenty-five centers were collected and followed by the Hungarian Pancreatic Study Group between 2012 and 2021. A general and a hospital-based population was used as the control group.After an AP episode patients have an approximately three-fold higher incidence rate of mortality than the general population (0.0404vs.0.0130 person-years). First-year mortality after discharge was almost double than in-hospital mortality (5.5%vs.3.5%), with 3.0% occurring in the first 90-day period. Age, comorbidities, and severity were the most significant independent risk factors for death following AP. Furthermore, multivariate analysis identified creatinine, glucose, and pleural fluid on admission as independent risk factors associated with post-discharge mortality. In the first 90-day period, cardiac failure and AP-related sepsis were among the main causes of death following discharge, while cancer-related cachexia and non-AP-related infection were the key causes in the later phase.Almost as many patients in our cohort die in the first 90-day period after discharge as during their hospital stay. Evaluation of cardiovascular status, follow-up of local complications, and cachexia-preventing oncological care should be an essential part of post-AP patient care. Future study protocols in AP must include at least a 90-day follow-up period after discharge

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

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