EASY‐APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis
Year: 2022
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients 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 h to predict the severity; therefore, the early therapeutic window is often missed. METHODS: The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit-learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross-validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross-validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP). RESULTS: The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy-to-use web application in the Streamlit Python-based framework (http://easy-app.org/). CONCLUSIONS: The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model.
DOI: 10.1002/ctm2.842
Authors: Balázs Kui, József Pintér, Roland Molontay, Marcell Nagy, Nelli Farkas, Noémi Gede, Áron Vincze, Judit Bajor, Szilárd Gódi, József Czimmer, Imre Szabó, Anita Illés, Patrícia Sarlós, Roland Hágendorn, Gabriella Pár, Mária Papp, Zsuzsanna Vitális, György Kovács, Eszter Fehér, Ildikó Földi, Ferenc Izbéki, László Gajdán, Roland Fejes, Balázs Csaba Németh, Imola Török, Hunor Farkas, Artautas Mickevicius, Ville Sallinen, Shamil Galeev, Elena Ramírez‐Maldonado, Andrea Párniczky, Bálint Erőss, Péter Jenő Hegyi, Péter Jenő Hegyi, Katalin Márta, Szilárd Váncsa, Robert Sutton, Peter Szatmary, Diane Latawiec, Chris Halloran, Enrique de‐Madaria, Elizabeth Pando, Piero Alberti, Maria José Gómez‐Jurado, Alina Tantau, Andrea Szentesi, Péter Hegyi, Péter Hegyi, the Hungarian Pancreatic Study Group
Institutions: University of Szeged, Budapest University of Technology and Economics, University of Pecs, University of Debrecen, Fejér Megyei Szent György Egyetemi Oktató Kórház, Universitatea de Medicină, Farmacie, Științe și Tehnologie „George Emil Palade” din Târgu Mureș, Vilnius University, Vilnius University Hospital Santariskiu Klinikos, University of Helsinki, Helsinki University Hospital, Consorci Sanitari Garraf, Heim Pál Országos Gyermekgyógyászati Intézet, Semmelweis University, University of Liverpool, Liverpool University Hospitals NHS Foundation Trust, Hospital General Universitario de Alicante Doctor Balmis, Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Universitat Autònoma de Barcelona, Vall d'Hebron Hospital Universitari, Iuliu Hațieganu University of Medicine and Pharmacy
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,300
OpenAlex ID: W4281929909