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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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001-es BibID:	BIBFORM079249
Első szerző:	Párniczky Andrea (gyermekgyógyász)
Cím:	Antibiotic therapy in acute pancreatitis : from global overuse to evidence based recommendations / Andrea Parniczky, Tamas Lantos, Eszter Margit Toth, Zsolt Szakacs, Szilard Godi, Roland Hagendorn, Dora Illes, Balazs Koncz, Katalin Marta, Alexandra Miko, Dora Mosztbacher, Balazs Csaba Nemeth, Daniel Pecsi, Aniko Szabo, Akos Szücs, Peter Varjú, Andrea Szentesi, Erika Darvasi, Balint Eross, Ferenc Izbeki, Laszlo Gajdan, Adrienn Halasz, Aron Vincze, Imre Szabo, Gabriella Par, Judit Bajor, Patrícia Sarlos, Jozsef Czimmer, Jozsef Hamvas, Tamas Takacs, Zoltan Szepes, Laszlo Czako, Marta Varga, Janos Novak, Barnabas Bod, Attila Szepes, Janos Sümegi, Maria Papp, Csaba Gog, Imola Torok, Wei Huang, Qing Xia, Ping Xue, Weiqin Li, Weiwei Chen, Natalia V. Shirinskaya, Vladimir L. Poluektov, Anna V. Shirinskaya, Péter Jenő Hegyi, Marian Batovský, Juan Armando Rodriguez-Oballe, Isabel Miguel Salas, Javier Lopez-Diaz, J. Enrique Dominguez-Munoz, Xavier Molero, Elizabeth Pando, María Lourdes Ruiz-Rebollo, Beatriz Burgueno-Gomez, Yu-Ting Chang, Ming-Chu Chang, Ajay Sud, Danielle Moore, Robert Sutton, Amir Gougol, Georgios I. Papachristou, Yaroslav Mykhailovych Susak, Illia Olehovych Tiuliukin, Antonio Pedro Gomes, Maria Jesus Oliveira, David Joao Aparício, Marcel Tantau, Floreta Kurti, Mila Kovacheva-Slavova, Stephanie-Susanne Stecher, Julia Mayerle, Goran Poropat, Kshaunish Das, Marco Vito Marino, Gabriele Capurso, Ewa Małecka-Panas, Hubert Zatorski, Anita Gasiorowska, Natalia Fabisiak, Piotr Ceranowicz, Beata Kusnierz-Cabala, Joana Rita Carvalho, Samuel Raimundo Fernandes, Jae Hyuck Chang, Eun Kwang Choi, Jimin Han, Sara Bertilsson, Hanaz Jumaa, Gabriel Sandblom, Sabite Kacar, Minas Baltatzis, Aliaksandr Vladimir Varabei, Vizhynis Yeshy, Serge Chooklin, Andriy Kozachenko, Nikolay Veligotsky, Peter Jr. Hegyi, Hungarian Pancreatic Study Group
Dátum:	2019
ISSN:	1424-3903
Megjegyzések:	Background: Unwarranted administration of antibiotics in acute pancreatitis presents a global challenge. The clinical reasoning behind the misuse is poorly understood. Our aim was to investigate current clinical practices and develop recommendations that guide clinicians in prescribing antibiotic treatment in acute pancreatitis. Methods: Four methods were used. 1) Systematic data collection was performed to summarize current evidence; 2) a retrospective questionnaire was developed to understand the current global clinical practice; 3) five years of prospectively collected data were analysed to identify the clinical parameters used by medical teams in the decision making process, and finally; 4) the UpToDate Grading of Rec- ommendations, Assessment, Development and Evaluation (GRADE) system was applied to provide evi- dence based recommendations for healthcare professionals. Results: The systematic literature search revealed no consensus on the start of AB therapy in patients with no bacterial culture test. Retrospective data collection on 9728 patients from 22 countries indicated a wide range (31e82%) of antibiotic use frequency in AP. Analysis of 56 variables from 962 patients showed that clinicians initiate antibiotic therapy based on increased WBC and/or elevated CRP, lipase and amylase levels. The above mentioned four laboratory parameters showed no association with infection in the early phase of acute pancreatitis. Instead, procalcitonin levels proved to be a better biomarker of early infection. Patients with suspected infection because of fever had no benefit from antibiotic therapy. Conclusions: The authors formulated four consensus statements to urge reduction of unjustified anti- biotic treatment in acute pancreatitis and to use procalcitonin rather than WBC or CRP as biomarkers to guide decision-making.
Tárgyszavak:	Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban Acute pancreatitis Antibiotic Guideline Recommendation Infection
Megjelenés:	Pancreatology. - 19 : 4 (2019), p. 488-499. -
További szerzők:	Lantos Tamás Tóth Eszter Margit Szakács Zsolt Gódi Szilárd Hágendorn Roland Illés Dóra Koncz Balázs Márta Katalin Mikó Alexandra Mosztbacher Dóra Németh Balázs Csaba Pécsi Dániel Szabó Anikó Szűcs Ákos (sebész) Varjú Péter Szentesi Andrea Darvasi Erika Erőss Bálint Izbéki Ferenc Gajdán László Halász Adrienn Vincze Áron Szabó Imre (1952-) (orvos) Pár Gabriella Bajor Judit Sarlós Patrícia Czimmer József Hamvas József Takács Tamás (Szeged) Szepes Zoltán Czakó László Varga Márta Novák János Bod Barnabás Szepes Attila Sümegi János Papp Mária (1975-) (belgyógyász, gasztroenterológus) Góg Csaba Török Imola Huang, Wei Xia, Qing Xue, Ping Li, Weiqin Chen, Weiwei Shirinskaya, Natalia V. Poluektov, Vladimir L. Shirinskaya, Anna V. Hegyi Péter Jenő (belgyógyász) Bátovský, Marian Rodriguez-Oballe, Juan Armando Salas, Isabel Miguel Lopez-Diaz, Javier Dominguez-Munoz, J. Enrique Molero, Xavier Pando, Elizabeth Ruiz-Rebollo, María Lourdes Burgueño-Gómez, Beatriz Chang, Yu-Ting Chang, Ming-Chu Sud, Ajay Moore, Danielle Sutton, Robert Gougol, Amir Papachristou, Georgios I. Susak, Yaroslav Mykhailovych Tiuliukin, Illia Olehovych Gomes, António Pedro Oliveira, Maria Jesus Aparício, David João Tantau, Marcel Kurti, Floreta Kovacheva-Slavova, Mila Stecher, Stephanie-Susanne Mayerle, Julia Poropat, Goran Das, Kshaunish Marino, Marco Vito Capurso, Gabriele Małecka-Panas, Ewa Zatorski, Hubert Gasiorowska, Anita Fabisiak, Natalia Ceranowicz, Piotr Kuśnierz-Cabala, Beata Carvalho, Joana Rita Fernandes, Samuel Raimundo Chang, Jae Hyuck Choi, Eun Kwang Han, Jimin Bertilsson, Sara Jumaa, Hanaz Sandblom, Gabriel Kacar, Sabite Baltatzis, Minas Varabei, Aliaksandr Vladimir Yeshy, Vizhynis Chooklin, Serge Kozachenko, Andriy Veligotsky, Nikolay Hegyi Péter Jr. (belgyógyász) Hungarian Pancreatic Study Group
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