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1.

001-es BibID:BIBFORM111659
035-os BibID:(Scopus)85166916995
Első szerző:Czapári Dóra
Cím:Detailed characteristics of post-discharge mortality in acute pancreatitis / Dóra Czapári, Alex Váradi, Nelli Farkas, Gergely Nyári, Katalin Márta, Szilárd Váncsa, Rita Nagy, Brigitta Teutsch, Stefania Bunduc, Bálint Erőss, László Czakó, Áron Vincze, Ferenc Izbéki, Mária Papp, Béla Merkely, Andrea Szentesi, Peter Hegyi, Hungarian Pancreatic Study Group
Dátum:2023
ISSN:0016-5085
Megjegyzések:Background and aims 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. Methods 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. Results After an AP episode patients have an approximately three-fold higher incidence rate of mortality than the general population (0.0404 vs. 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. Conclusion Almost as many patients in our cohort die in the first 90-day period after discharge asduring 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.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Gastroenterology. - 165 : 3 (2023), p. 682-695. -
További szerzők:Váradi Alex (1991-) (biológus) Farkas Nelli Nyári Gergely Róbert Márta Katalin Váncsa Szilárd Nagy Rita Teutsch Brigitta Bunduc, Stefania Erőss Bálint Czakó László Vincze Áron Izbéki Ferenc Papp Mária (1975-) (belgyógyász, gasztroenterológus) Merkely Béla (1965-) (orvos) Szentesi Andrea Hegyi Péter Jr. (belgyógyász) Vitális Zsuzsanna (1963-) (belgyógyász, gasztroenterológus) Hungarian Pancreatic Study Group
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2.

001-es BibID:BIBFORM090166
Első szerző:Hágendorn Roland
Cím:Disturbance of consciousness deteriorates the severity of acute pancreatitis. An international multicentre cohort analyses of 1220 prospectively collected patients / R. Hagendorn, Á. Vincze, F. Izbeki, L. Gajdan, S. Godi, A. Illes, P. Sarlos, N. Farkas, B. Erős, V. Lillik, D. Illes, P. Varjú, K. Marta, I. Török, M. Papp, Z. Vitalis, B. Bod, J. Hamvas, Z. Szepes, T. Takacs, L. Czakó, A. Szentesi, A. Parniczky, P. Hegyi, A. Miko
Dátum:2020
ISSN:1424-3903
Megjegyzések:Purpose: Disturbance of consciousness (DOC) may develop in acute pancreatitis (AP). In clinical practice, it is known that DOC may worsen the patient's condition, but we have no exact data on how DOC affects the outcome of AP. Materials and methods: From the Hungarian Pancreatic Study Groups' AP registry, 1220 cases contained the exact data on DOC. Patients were separated to Non-DOC and DOC, whereas DOC was further divided into non-alcohol related DOC (Non-ALC DOC) and ALC-DOC groups. Statistical analysis was performed by SPSS 24 Software Package. Results: From the 1220 patients, 47 (3.85%) developed DOC, 23 (48.9%) cases were ALC DOC vs. 24 (51.1%) Non-ALC DOC. The incidence of severe AP was higher in the DOC compared to the Non-DOC group (19.15% vs. 5.29%, p<0.001). The mortality was higher in the DOC vs. Non-DOC group (14.89% vs. 1.71%, p<0.001). Length of hospitalization (LOH) was longer in the DOC vs. non-DOC group (Me:11; IQR:8-17 days vs. Me:9; IQR:6-13 days, p?0.049). Patients with ALC DOC developed more frequently moderately-severe AP vs. Non-ALC DOC (43.48% vs. 12.5%), while the incidence of severe AP was significantly higher in Non-ALC vs. ALC DOC group (33.33% vs. 4.35%) (p<0.001). LOH showed tendency to be longer in Non-ALC DOC compared to ALC DOC respectively (Me:13; IQR:7-20 days vs. Me:9.5; IQR:8-15.5 days, p?0.119). Conclusions: DOC during AP is associated with a higher rate of moderate and severe AP and increases the risk of mortality; therefore, the DOC should be closely monitored and prevented in AP.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idézhető absztrakt
folyóiratcikk
Megjelenés:Pancreatology. - 20 (2020), p. S25. -
További szerzők:Vincze Áron Izbéki Ferenc Gajdán László Gódi Szilárd Illés Árpád (1959-) (belgyógyász, haematológus, onkológus) Sarlós Péter Farkas Nelli Erős Bálint Lillik Veronika Illés Dóra Varjú Péter Márta Katalin Török I. Papp Mária (1975-) (belgyógyász, gasztroenterológus) Vitális Zsuzsanna (1963-) (belgyógyász, gasztroenterológus) Bod Barnabás Hamvas József Szepes Zoltán Takács T. Czakó László Szentesi Andrea Párniczky Andrea (gyermekgyógyász) Hegyi Péter Jenő (belgyógyász) Mikó Alexandra
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3.

001-es BibID:BIBFORM085501
Első szerző:Hágendorn Roland
Cím:Development of disturbance of consciousness is associated with increased severity in acute pancreatitis / Roland Hágendorn, Áron Vincze, Ferenc Izbéki, László Gajdán, Szilárd Gódi, Anita Illés, Patrícia Sarlós, Nelli Farkas, Bálint Erőss, Veronika Lillik, Dóra Illés, Péter Varjú, Katalin Márta, Imola Török, Mária Papp, Zsuzsanna Vitális, Barnabás Bod, József Hamvas, Zoltán Szepes, Tamás Takács, László Czakó, Zsolt Márton, Andrea Szentesi, Andrea Párniczky, Péter Hegyi, Alexandra Mikó
Dátum:2020
ISSN:1424-3903
Megjegyzések:Background Disturbance of consciousness (DOC) may develop in acute pancreatitis (AP). In clinical practice, it is known that DOC may worsen the patient's condition, but we have no exact data on how DOC affects the outcome of AP. Methods From the Hungarian Pancreatic Study Groups' AP registry, 1220 prospectively collected cases were analysed, which contained exact data on DOC, included patients with confusion, delirium, convulsion, and alcohol withdrawal, answering a post hoc defined research question. Patients were separated to Non-DOC and DOC, whereas DOC was further divided into non-alcohol related DOC (Non-ALC DOC) and ALC DOC groups. For statistical analysis, independent sample t-test, Mann-Whitney, Chi-squared, or Fisher exact test were used. Results From the 1220 patients, 47 (3.9%) developed DOC, 23 (48.9%) cases were ALC DOC vs. 24 (51.1%) Non-ALC DOC. Analysis between the DOC and Non-DOC groups showed a higher incidence of severe AP (19.2% vs. 5.3%, p<0.001), higher mortality (14.9% vs. 1.7%, p<0.001), and a longer length of hospitalization (LOH) (Me=11; IQR: 8-17 days vs. Me=9; IQR: 6-13 days, p=0.049) respectively. Patients with ALC DOC developed more frequently moderate AP vs. Non-ALC DOC (43.5% vs. 12.5%), while the incidence of severe AP was higher in Non-ALC vs. ALC DOC group (33.3% vs. 4.4%) (p<0.001). LOH showed a tendency to be longer in Non-ALC DOC compared to ALC DOC, respectively (Me:13; IQR:7-20 days vs. Me:9.5; IQR:8-15.5 days, p=0.119). Conclusion DOC during AP is associated with a higher rate of moderate and severe AP and increases the risk of mortality.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
disturbance of consciousness
acute pancreatitis
alcohol
delirium
mortality
Megjelenés:Pancreatology. - 20 : 5 (2020), p. 806-812. -
További szerzők:Vincze Áron Izbéki Ferenc Gajdán László Gódi Szilárd Illés Anita Sarlós Patrícia Farkas Nelli Erőss Bálint Lillik Veronika Illés Dóra Varjú Péter Márta Katalin Török Imola Papp Mária (1975-) (belgyógyász, gasztroenterológus) Vitális Zsuzsanna (1963-) (belgyógyász, gasztroenterológus) Bod Barnabás Hamvas József Szepes Zoltán Takács Tamás (Szeged) Czakó László Márton Zsolt Szentesi Andrea Párniczky Andrea (gyermekgyógyász) Hegyi Péter Jenő (belgyógyász) Mikó Alexandra
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4.

001-es BibID:BIBFORM101539
035-os BibID:(Cikkazonosító)7827 (WOS)000795163100024 (Scopus)85130054194 (PMID)35552440
Első szerző:Kiss Szabolcs
Cím:Early prediction of acute necrotizing pancreatitis by artificial intelligence : a prospective cohort-analysis of 2387 cases / Szabolcs Kiss, József Pintér, Roland Molontay, Marcell Nagy, Nelli Farkas, Zoltán Sipos, Péter Fehérvári, László Pecze, Mária Földi, Áron Vincze, Tamás Takács, László Czakó, Ferenc Izbéki, Adrienn Halász, Eszter Boros, József Hamvas, Márta Varga, Artautas Mickevicius, Nándor Faluhelyi, Orsolya Farkas, Szilárd Váncsa, Rita Nagy, Stefania Bunduc, Péter Jenő Hegyi, Katalin Márta, Katalin Borka, Attila Doros, Nóra Hosszúfalusi, László Zubek, Bálint Erőss, Zsolt Molnár, Andrea Párniczky, Péter Hegyi, Andrea Szentesi, Hungarian Pancreatic Study Group
Dátum:2022
ISSN:2045-2322
Megjegyzések: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 2,387 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.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Scientific Reports. - 12 : 1 (2022), p. 1-1. -
További szerzők:Pintér József (1930-) (urológus) Molontay Roland Nagy Marcell Farkas Nelli Sipos Zoltán (1988-) (vegyész, angol-magyar szakfordító) Fehérvári Péter Pecze László Földi Mária Vincze Áron Takács Tamás (Szeged) Czakó László Izbéki Ferenc Halász Adrienn Boros Eszter Hamvas József Varga Márta Mickevicius, Artautas Faluhelyi Nándor Farkas Orsolya Váncsa Szilárd Nagy Rita Bunduc, Stefania Hegyi Péter Jenő (belgyógyász) Márta Katalin Borka Katalin Doros Attila Hosszúfalusi Nóra Zubek László (1970-) (aneszteziológus és intenzív terápiás, kardiológus, oxyológus) Erőss Bálint Molnár Zsolt (Pécs, aneszteziológus) Párniczky Andrea (gyermekgyógyász) Hegyi Péter (pszichológus) Szentesi Andrea Papp Mária (1975-) (belgyógyász, gasztroenterológus) Vitális Zsuzsanna (1963-) (belgyógyász, gasztroenterológus) Hungarian Pancreatic Study Group
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5.

001-es BibID:BIBFORM083103
Első szerző:Koncz Balázs
Cím:LIFEStyle, Prevention and Risk of Acute PaNcreatitis (LIFESPAN) : protocol of a multicentre and multinational observational case-control study / Koncz Balázs, Darvasi Erika, Erdősi Dalma, Szentesi Andrea, Márta Katalin, Erőss Bálint, Pécsi Dániel, Gyöngyi Zoltán, Girán János, Farkas Nelli, Papp Maria, Fehér Eszter, Vitális Zsuzsanna, Janka Tamás, Vincze Áron, Izbéki Ferenc, Dunás-Varga Veronika, Gajdán László, Török Imola, Károly Sándor, Antal Judit, Zádori Noémi, Lerch Markus M., Neoptolemos John, Sahin-Toth Miklos, Petersen Ole H., Hegyi Péter
Dátum:2020
ISSN:2044-6055 2044-6055
Megjegyzések:AbstrACt Introduction Acute pancreatitis (AP) is a life- threatening inflammatory disease of the exocrine pancreas which needs acute hospitalisation. Despite its importance, we have significant lack of knowledge whether the lifestyle factors elevate or decrease the risk of AP or influence the disease outcome. So far, no synthetising study has been carried out examining associations between socioeconomic factors, dietary habits, physical activity, chronic stress, sleep quality and AP. Accordingly, LIFESPAN identifies risk factors of acute pancreatitis and helps to prepare preventive recommendations for lifestyle elements. Methods and analysis LIFESPAN is an observational, multicentre international case?control study. Participating subjects will create case and control groups. The study protocol was designed according to the SPIRIT guideline. Patients in the case group (n=1700) have suffered from AP (alcohol- induced, n=500; biliary, n=500; hypertriglyceridemiainduced, n=200; other, n=500); the control group subjects have no AP in their medical history. Our study will have three major control groups (n=2200): hospital- based (n=500), population- based (n=500) and aetiology- based (alcohol, n=500; biliary, n=500 and hypertriglyceridemia, n=200). All of them will be matched to the case group individually by gender, age and location of residence. Aggregately, 3900 subjects will be enrolled into the study. The study participants will complete a complex questionnaire with the help of a clinical research administrator/study nurse. Analysis methods include analysis of the continuous and categorical values. Ethics and dissemination The study has obtained the relevant ethical approval (54175-2/2018/EKU) and also internationally registered (ISRCTN25940508). After obtaining the final conclusions, we will publish the data to the medical community and will also disseminate our results via open access. trial registration number ISRCTN25940508; Pre- results.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
acute pancreatitis
lifestyle
prevention
Megjelenés:BMJ Open. - 10 : 1 (2020), p. 1-9. -
További szerzők:Darvasi Erika Erdősi Dalma Szentesi Andrea Márta Katalin Erőss Bálint Pécsi Dániel Gyöngyi Zoltán Girán János Farkas Nelli Papp Mária (1975-) (belgyógyász, gasztroenterológus) Fehér Eszter Vitális Zsuzsanna (1963-) (belgyógyász, gasztroenterológus) Janka Tamás Vincze Áron Izbéki Ferenc Dunás-Varga Veronika Gajdán László Török Imola Károly Sándor Antal Judit Zádori Noémi Lerch, Markus M. Neoptoleomos, Johan P. Sahin-Tóth Miklós Petersen, Ole H. Hegyi Péter Jenő (belgyógyász)
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6.

001-es BibID:BIBFORM101294
035-os BibID:(cikkazonosító)e842 (wos)000804849400001
Első szerző:Kui Balázs
Cím:EASY-APP : an artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis / Kui Balázs, Pintér József, Molontay Roland, Nagy Marcell, Farkas Nelli, Gede Noémi, Vincze Áron, Bajor Judit, Gódi Szilárd, Czimmer József, Szabó Imre, Illés Anita, Sarlós Patrícia, Hágendorn Roland, Pár Gabriella, Papp Mária, Vitális Zsuzsanna, Kovács György, Fehér Eszter, Földi Ildikó, Izbéki Ferenc, Gajdán László, Fejes Roland, Németh Balázs Csaba, Török Imola, Farkas Hunor, Artautas Mickevicius, Ville Sallinen, Shamil Galeev, Elena Ramirez Maldonado, Párniczky Andrea, Erőss Bálint, Hegyi Péter Jenő, Márta Katalin, Váncsa Szilárd, Sutton Robert, Enrique de-Madaria, Elizabeth Pando, Piero Alberti, Maria José Gómez-Jurado, Alina Tantau, Szentesi Andrea, Hegyi Péter, Hungarian Pancreatic Study Group
Dátum:2022
ISSN:2001-1326
Megjegyzések:Background: Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients, who are 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 hours to predict the severity, so the early therapeutic window is missing. Methods: The early achievable severity index (EASY) is a registered multicentre, multinational, prospective, observational study (ISRCTN10525246). Clinical parameters were collected from 15 countries and 28 medical centres via eCRF. The predictions were made using machine learning models including Decision Tree, Random Forest, Logistic Regression, SVM, CatBoost, and XGBoost. For the modeling, we used the scikit-learn, xgboost, and catboost Python packages. We have evaluated our models using 4-fold cross-validation and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics have been calculated on the union of the test sets of the cross-validation. The most important factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence, called SHapley Additive exPlanations (SHAP). Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation, and the bootstrapping method for the estimation of confidence we have developed a web application in the Streamlit Python-based framework. Results: The prediction model is based on the international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model has been an XGBoost classifier with an average AUC score of 0.81 and accuracy of 89.1% and the model is improving with experience. The six most influential features are the respiratory rate, body temperature, abdominal muscular reflex, gender, age, and glucose level. Finally, a free and easy-to-use web application was developed (http://easy-app.org/). Conclusions: The EASY prediction score is a practical tool for identifying patients at high risk for severe acute pancreatitis within hours of hospital admission. The easy-to-use web application is available for clinicians and contributes to the improvement of the model.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
severity prediction
acute pancreatitis
artificial intelligence
Megjelenés:Clinical and Translational Medicine. - 12 : 6 (2022), p. 1-13. -
További szerzők:Pintér József (1930-) (urológus) Molontay Roland Nagy Marcell Farkas Nelli Gede Noémi Vincze Áron Bajor Judit Gódi Szilárd Czimmer József Szabó Imre Illés Anita Sarlós Patrícia Hágendorn Roland Pár Gabriella Papp Mária (1975-) (belgyógyász, gasztroenterológus) Vitális Zsuzsanna (1963-) (belgyógyász, gasztroenterológus) Kovács György (1982-) (belgyógyász, gasztroenterológus) Fehér Eszter Földi Ildikó (1981-) (orvos) Izbéki Ferenc Gajdán László Fejes Roland Németh Balázs Csaba Török Imola Farkas Hunor Mickevicius, Artautas Sallinen, Ville Galeev, Shamil Ramírez-Maldonado, Elena Párniczky Andrea (gyermekgyógyász) Erőss Bálint Hegyi Péter Jenő (belgyógyász) Márta Katalin Váncsa Szilárd Sutton, Robert de-Madaria, Enrique Pando, Elizabeth Alberti, Piero Gómez-Jurado, Maria José Tantau, Alina Szentesi Andrea Hegyi Péter (pszichológus) Hungarian Pancreatic Study Group
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