Scientific Reports (May 2022)
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
Affiliations
- Szabolcs Kiss
- Doctoral School of Clinical Medicine, Faculty of Medicine, University of Szeged
- József Pintér
- Human and Social Data Science Lab, Budapest University of Technology and Economics
- Roland Molontay
- Human and Social Data Science Lab, Budapest University of Technology and Economics
- Marcell Nagy
- Human and Social Data Science Lab, Budapest University of Technology and Economics
- Nelli Farkas
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs
- Zoltán Sipos
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs
- Péter Fehérvári
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs
- László Pecze
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs
- Mária Földi
- Doctoral School of Clinical Medicine, Faculty of Medicine, University of Szeged
- Áron Vincze
- Division of Gastroenterology, First Department of Medicine, Medical School, University of Pécs
- Tamás Takács
- Department of Medicine, University of Szeged
- László Czakó
- Department of Medicine, University of Szeged
- Ferenc Izbéki
- Department of Internal Medicine, Szent György Teaching Hospital of County Fejér
- Adrienn Halász
- Doctoral School of Clinical Medicine, Faculty of Medicine, University of Szeged
- Eszter Boros
- Department of Internal Medicine, Szent György Teaching Hospital of County Fejér
- József Hamvas
- Bajcsy-Zsilinszky Hospital
- Márta Varga
- Department of Gastroenterology, BMKK Dr Rethy Pal Hospital
- Artautas Mickevicius
- Vilnius University Hospital Santaros Clinics, Clinics of Abdominal Surgery, Nephrourology and Gastroenterology, Faculty of Medicine, Vilnius University
- Nándor Faluhelyi
- Department of Medical Imaging, Medical School, University of Pécs
- Orsolya Farkas
- Department of Medical Imaging, Medical School, University of Pécs
- Szilárd Váncsa
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs
- Rita Nagy
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs
- Stefania Bunduc
- Centre for Translational Medicine, Semmelweis University
- Péter Jenő Hegyi
- Centre for Translational Medicine, Semmelweis University
- Katalin Márta
- Centre for Translational Medicine, Semmelweis University
- Katalin Borka
- Centre for Translational Medicine, Semmelweis University
- Attila Doros
- Centre for Translational Medicine, Semmelweis University
- Nóra Hosszúfalusi
- Centre for Translational Medicine, Semmelweis University
- László Zubek
- Centre for Translational Medicine, Semmelweis University
- Bálint Erőss
- Centre for Translational Medicine, Semmelweis University
- Zsolt Molnár
- Centre for Translational Medicine, Semmelweis University
- Andrea Párniczky
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs
- Péter Hegyi
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs
- Andrea Szentesi
- Doctoral School of Clinical Medicine, Faculty of Medicine, University of Szeged
- Hungarian Pancreatic Study Group
- Division of Pancreatic Diseases, Heart and Vascular Center, Semmelweis University
- DOI
- https://doi.org/10.1038/s41598-022-11517-w
- Journal volume & issue
-
Vol. 12,
no. 1
pp. 1 – 11
Abstract
Abstract 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 2387 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.
