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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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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
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat DOI
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