Ning Ding1, Cuirong Guo2, Changluo Li2, Yang Zhou1, Xiangping Chai1. 1. Department of Emergency Medicine, The Second Xiangya Hospital, Emergency Medicine and Difficult Diseases Institute, Central South University, China. 2. Department of Emergency Medicine, Changsha Central Hospital, University of South China, China.
Abstract
BACKGROUND: Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of in-hospital mortality in AP. METHODS: Patients with AP were identified from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. Clinical and laboratory data were utilized to perform a predictive model by back propagation ANN approach. RESULTS: A total of 337 patients with AP were analyzed in the study, and the in-hospital mortality rate was 11.2%. A total of 12 variables that differed between patients in survivor group and nonsurvivor group were applied to construct ANN model. Three independent variables were identified as risk factors associated with in-hospital mortality by multivariate logistic regression analysis. The predictive performance based on the area under the receiver operating characteristic curve (AUC) was 0.769 for ANN model, 0.607 for logistic regression, 0.652 for Ranson score, and 0.401 for SOFA score. CONCLUSION: An ANN predictive model for in-hospital mortality in patients with AP in MIMIC-III database was first performed. The patients with high risk of fatal outcome can be screened out easily in the early stage of AP by our model.
BACKGROUND: Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of in-hospital mortality in AP. METHODS: Patients with AP were identified from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. Clinical and laboratory data were utilized to perform a predictive model by back propagation ANN approach. RESULTS: A total of 337 patients with AP were analyzed in the study, and the in-hospital mortality rate was 11.2%. A total of 12 variables that differed between patients in survivor group and nonsurvivor group were applied to construct ANN model. Three independent variables were identified as risk factors associated with in-hospital mortality by multivariate logistic regression analysis. The predictive performance based on the area under the receiver operating characteristic curve (AUC) was 0.769 for ANN model, 0.607 for logistic regression, 0.652 for Ranson score, and 0.401 for SOFA score. CONCLUSION: An ANN predictive model for in-hospital mortality in patients with AP in MIMIC-III database was first performed. The patients with high risk of fatal outcome can be screened out easily in the early stage of AP by our model.
Authors: Georgios I Papachristou; Venkata Muddana; Dhiraj Yadav; Michael O'Connell; Michael K Sanders; Adam Slivka; David C Whitcomb Journal: Am J Gastroenterol Date: 2009-10-27 Impact factor: 10.864
Authors: Mary T Keogan; Joseph Y Lo; Kelly S Freed; Vasillios Raptopoulos; Simon Blake; Ihab R Kamel; K Weisinger; Max P Rosen; Rendon C Nelson Journal: Acad Radiol Date: 2002-04 Impact factor: 3.173
Authors: Efstratios Koutroumpakis; Bechien U Wu; Olaf J Bakker; Anwar Dudekula; Vikesh K Singh; Marc G Besselink; Dhiraj Yadav; Rawad Mounzer; Hjalmar C van Santvoort; David C Whitcomb; Hein G Gooszen; Peter A Banks; Georgios I Papachristou Journal: Am J Gastroenterol Date: 2015-11-10 Impact factor: 10.864
Authors: Kyoung Hwa Lee; Jae June Dong; Su Jin Jeong; Myeong-Hun Chae; Byeong Soo Lee; Hong Jae Kim; Sung Hun Ko; Young Goo Song Journal: J Clin Med Date: 2019-10-02 Impact factor: 4.241
Authors: Na Shi; Lan Lan; Jiawei Luo; Ping Zhu; Thomas R W Ward; Peter Szatmary; Robert Sutton; Wei Huang; John A Windsor; Xiaobo Zhou; Qing Xia Journal: J Pers Med Date: 2022-04-11
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; 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 Journal: Clin Transl Med Date: 2022-06
Authors: Péter Hegyi; Andrea Szentesi; 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 Journal: Sci Rep Date: 2022-05-12 Impact factor: 4.996