Pankaj Gupta1, Praveen Kumar-M2, Mansi Verma3, Vishal Sharma4, Jayanta Samanta4, Harshal Mandavdhare4, Saroj K Sinha4, Usha Dutta4, Rakesh Kochhar4. 1. GE Radiology, Department of Radiodiagnosis, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, 160012, India. Pankajgupta959@gmail.com. 2. Department of Pharmacology, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, 160012, India. 3. GE Radiology, Department of Radiodiagnosis, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, 160012, India. 4. Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Abstract
PURPOSE: The existing CT indices do not allow quantitative prediction of clinical outcomes in acute pancreatitis (AP). The aim of this study was to develop and validate a revised CT index using a nomogram-based approach. METHODS: This retrospective study comprised consecutive patients with AP who underwent contrast-enhanced CT between June 2017 and March 2019. 123 CT scans were randomly divided into training (n = 103) and validation groups (n = 20). Two radiologists analyzed CT scans for findings described in modified CT severity index and additional exploratory items (13 items). Seven items (pancreatic necrosis, number of collections, size of collections, ascites, pleural effusion, celiac artery involvement, and liver steatosis) found to be statistically significant were used for development of index. Synthetic minority oversampling technique (SMOTE) was employed to balance representation of minority classes and hence this index was named "SMOTE Application for Reading CT in AcuTe Pancreatitis (SMART-CT index)". Binomial logistic regression was used for development of prediction algorithm. Nomograms were then created and validated for each outcome. RESULTS: The new CT index had area under the curve (AUC) of 0.79 [95% CI 0.65-0.93], 0.66 (95% CI 0.54-0.77), 0.75 (95% CI 0.65-0.85), 0.83 (95% CI 0.69-0.96), 0.70 (95% CI 0.60-0.81), and 0.64 (95% CI 0.53-0.75) for mortality, intensive care unit (ICU) stay, length of hospitalization, length of ICU stay, number of admissions, and severity, respectively. The AUC of validation cohort was comparable to the training cohort. CONCLUSION: The novel nomogram-based index predicts occurrence of clinical outcome with moderate accuracy.
PURPOSE: The existing CT indices do not allow quantitative prediction of clinical outcomes in acute pancreatitis (AP). The aim of this study was to develop and validate a revised CT index using a nomogram-based approach. METHODS: This retrospective study comprised consecutive patients with AP who underwent contrast-enhanced CT between June 2017 and March 2019. 123 CT scans were randomly divided into training (n = 103) and validation groups (n = 20). Two radiologists analyzed CT scans for findings described in modified CT severity index and additional exploratory items (13 items). Seven items (pancreatic necrosis, number of collections, size of collections, ascites, pleural effusion, celiac artery involvement, and liver steatosis) found to be statistically significant were used for development of index. Synthetic minority oversampling technique (SMOTE) was employed to balance representation of minority classes and hence this index was named "SMOTE Application for Reading CT in AcuTe Pancreatitis (SMART-CT index)". Binomial logistic regression was used for development of prediction algorithm. Nomograms were then created and validated for each outcome. RESULTS: The new CT index had area under the curve (AUC) of 0.79 [95% CI 0.65-0.93], 0.66 (95% CI 0.54-0.77), 0.75 (95% CI 0.65-0.85), 0.83 (95% CI 0.69-0.96), 0.70 (95% CI 0.60-0.81), and 0.64 (95% CI 0.53-0.75) for mortality, intensive care unit (ICU) stay, length of hospitalization, length of ICU stay, number of admissions, and severity, respectively. The AUC of validation cohort was comparable to the training cohort. CONCLUSION: The novel nomogram-based index predicts occurrence of clinical outcome with moderate accuracy.
Authors: P Gupta; G Chayan Das; V Sharma; H Mandavdhare; J Samanta; H Singh; S Kant Sinha; U Dutta; R Kochhar Journal: Acta Gastroenterol Belg Date: 2019 Oct-Dec Impact factor: 1.316
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