Találati lista | Tudóstér

001-es BibID:	BIBFORM088207
Első szerző:	Demcsák Alexandra
Cím:	Acid suppression therapy, gastrointestinal bleeding and infection in acute pancreatitis - An international cohort study / Alexandra Demcsak, Alexandra Soos, Lilla Kincses, Ines Capunge, Georgi Minkov, Mila Kovacheva-Slavova, Radislav Nakov, Dong Wu, Wei Huang, Qing Xia, Lihui Deng, Marcus Hollenbach, Alexander Schneider, Michael Hirth, Orestis Ioannidis, Aron Vincze, Judit Bajor, Patrícia Sarlos, Laszló Czakó, Dora Illés, Ferenc Izbeki, Laszló Gajdán, Maria Papp, Jozsef Hamvas, Marta Varga, Peter Kanizsai, Ernő Bóna, Alexandra Miko, Szilard Váncsa, Márk Félix Juhász, Klementina Ocskay, Erika Darvasi, Emőke Miklós, Balint Erőss, Andrea Szentesi, Andrea Parniczky, Riccardo Casadei, Claudio Ricci, Carlo Ingaldi, Laura Mastrangelo, Elio Jovine, Vincenzo Cennamo, Marco V. Marino, Giedrius Barauskas, Povilas Ignatavicius, Mario Pelaez-Luna, Andrea Soriano Rios, Svetlana Turcan, Eugen Tcaciuc, Ewa Małecka-Panas, Hubert Zatorski, Vitor Nunes, Antonio Gomes, Tiago Cúrdia Gonçalves, Marta Freitas, Júlio Constantino, Milene Sa, Jorge Pereira, Bogdan Mateescu, Gabriel Constantinescu, Vasile Sandru, Ionut Negoi, Cezar Ciubotaru, Valentina Negoita, Stefania Bunduc, Cristian Gheorghe, Sorin Barbu, Alina Tantau, Marcel Tantau, Eugen Dumitru, Andra Iulia Suceveanu, Cristina Tocia, Adriana Gherbon, Andrey Litvin, Natalia Shirinskaya, Yliya Rabotyagova, Mihailo Bezmarevic, Péter Jenő Hegyi, Jimin Han, Juan Armando Rodriguez-Oballe, Isabel Miguel Salas, Eva Pijoan Comas, Daniel de la Iglesia Garcia, Andrea Jardi Cuadrado, Adriano Quiroga Castineira, Yu-Ting Chang, Ming-Chu Chang, Ali Kchaou, Ahmed Tlili, Sabite Kacar, Volkan Gokbulut, Deniz Duman, Haluk Tarik Kani, Engin Altintas, Serge Chooklin, Serhii Chuklin, Amir Gougol, George Papachristou, Peter Hegyi Jr.
Dátum:	2020
ISSN:	1424-3903
Megjegyzések:	Background:Acid suppressing drugs (ASD) are generally used in acute pancreatitis (AP); however, largecohorts are not available to understand their efficiency and safety. Therefore, our aims were to evaluatethe association between the administration of ASDs, the outcome of AP, the frequency of gastrointestinal(GI) bleeding and GI infection in patients with AP.Methods:We initiated an international survey and performed retrospective data analysis on AP patientshospitalized between January 2013 and December 2018.Results:Data of 17,422 adult patients with AP were collected from 59 centers of 23 countries. We foundthat 23.3% of patients received ASDs before and 86.6% during the course of AP. ASDs were prescribed to57.6% of patients at discharge. ASD administration was associated with more severe AP and highermortality. GI bleeding was reported in 4.7% of patients, and it was associated with pancreatitis severity,mortality and ASD therapy. Stool culture test was performed in 6.3% of the patients with 28.4% positiveresults.Clostridium difficilewas the cause of GI infection in 60.5% of cases. Among the patients with GIinfections, 28.9% received ASDs, whereas 24.1% were without any acid suppression treatment. GI infec-tion was associated with more severe pancreatitis and higher mortality.Conclusions:Although ASD therapy is widely used, it is unlikely to have beneficial effects either on theoutcome of AP or on the prevention of GI bleeding during AP. Therefore, ASD therapy should be sub-stantially decreased in the therapeutic management of AP.
Tárgyszavak:	Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Acid suppressing drug Gastrointestinal bleeding Gastrointestinal infection Acute pancreatitis Proton pump inhibitor
Megjelenés:	Pancreatology. - 20 : 7 (2020), p. 1323-1331. -
További szerzők:	Soós Alexandra Kincses Lilla Capunge, Ines Minkov, Georgi Kovacheva-Slavova, Mila Nakov, Radislav Wu, Dong Huang, Wei Xia, Qing Deng, Lihui Hollenbach, Marcus Schneider, Alexander Hirth, Michael Ioannidis, Orestis Vincze Áron Bajor Judit Sarlós Patrícia Czakó László Illés Dóra Izbéki Ferenc Gajdán László Papp Mária (1975-) (belgyógyász, gasztroenterológus) Hamvas József Varga Márta Kanizsai Péter Bóna Ernő Mikó Alexandra Váncsa Szilárd Juhász Márk Félix Ocskay Klementina Darvasi Erika Miklós Emőke Erőss Bálint Szentesi Andrea Párniczky Andrea (gyermekgyógyász) Casadei, Riccardo Ricci, Claudio Ingaldi, Carlo Mastrangelo, Laura Jovine, Elio Cennamo, Vincenzo Marino, Marco Vito Barauskas, Giedrius Ignatavicius, Povilas Pelaez-Luna, Mario Rios, Andrea Soriano Turcan, Svetlana Tcaciuc, Eugen Małecka-Panas, Ewa Zatorski, Hubert Nunes, Vitor Gomes, António Pedro Gonçalves, Tiago Cúrdia Freitas, Marta Constantino, Júlio Sá, Milene Pereira, Jorge Mateescu, Bogdan Constantinescu, Gabriel Sandru, Vasile Negoi, Ionut Ciubotaru, Cezar Negoita, Valentina Bunduc, Stefania Gheorghe, Cristian Barbu, Sorin Tantau, Alina Tantau, Marcel Dumitru, Eugen Suceveanu, Andra Iulia Tocia, Cristina Gherbon, Adriana Litvin, A. Andrey Shirinskaya, Natalia V. Rabotyagova, Yliya Bezmarevic, Mihailo Hegyi Péter Jenő (belgyógyász) Han, Jimin Rodriguez-Oballe, Juan Armando Salas, Isabel Miguel Comas, Eva Pijoan Garcia, Daniel de la Iglesia Cuadrado, Andrea Jardi Castiñeira, Adriano Quiroga Chang, Yu-Ting Chang, Ming-Chu Kchaou, Ali Tlili, Ahmed Kacar, Sabite Gökbulut, Volkan Duman, Deniz Kani, Haluk Tarik Altintas, Engin Chooklin, Serge Chuklin, Serhii Gougol, Amir Papachristou, Georgios I. Hegyi Péter Jr. (belgyógyász)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon:

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
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat DOI
Borító:
Saját polcon: