Hye Won Choi1, Hyun Jeong Park1, Seo-Youn Choi2, Jae Hyuk Do3, Na Young Yoon4, Ara Ko1, Eun Sun Lee1. 1. 1 Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, 102 Heukseok-ro, Dongjak-gu, Seoul 06973, Republic of Korea. 2. 2 Department of Radiology, Soonchunhyang University Bucheon Hospital, Bucheon, Republic of Korea. 3. 3 Department of Internal Medicine, College of Medicine, Chung-Ang University, Chung-Ang University College of Medicine, Seoul, Republic of Korea. 4. 4 College of Business, Korea Advanced Institute of Science and Technology, Seoul, Republic of Korea.
Abstract
OBJECTIVE: The objective of our study was to develop a decision tree model for the early prediction of the severity of acute pancreatitis (AP) using clinical and radiologic scoring systems. MATERIALS AND METHODS: For this retrospective study, 192 patients with AP who underwent CT 72 hours or less after symptom onset were divided into two cohorts: a training cohort (n = 115) and a validation cohort (n = 77). Univariate analysis was performed to identify significant parameters for the prediction of severe AP in the training cohort. For early prediction of disease severity, a classification tree analysis (CTA) model was constructed using significant scoring systems shown by univariate analysis. To assess the diagnostic performance of the model, we compared the area under the ROC curve (AUC) with each selected single parameter. We also evaluated the diagnostic performance in the validation cohort. RESULTS: The Acute Physiology and Chronic Health Evaluation (APACHE)-II score, bedside index for severity in acute pancreatitis (BISAP) score, extrapancreatic inflammation on CT (EPIC) score, and Balthazar grade were included in the CTA model. In the training cohort, our CTA model showed a trend of a higher AUC (0.853) than the AUC of each single parameter (APACHE-II score, 0.835; BISAP score, 0.842; EPIC score, 0.739; Balthazar grade, 0.700) (all, p > 0.0125) while achieving specificity (100%) higher than and accuracy (94.8%) comparable to each single parameter (both, p < 0.0125). In the validation cohort, the CTA model achieved diagnostic performance similar to the training cohort with an AUC of 0.833. CONCLUSION: Our CTA model consisted of clinical (i.e., APACHE-II and BISAP scores) and radiologic (i.e., Balthazar grade and EPIC score) scoring systems and may be useful for the early prediction of the severity of AP and identification of high-risk patients who require close surveillance.
OBJECTIVE: The objective of our study was to develop a decision tree model for the early prediction of the severity of acute pancreatitis (AP) using clinical and radiologic scoring systems. MATERIALS AND METHODS: For this retrospective study, 192 patients with AP who underwent CT 72 hours or less after symptom onset were divided into two cohorts: a training cohort (n = 115) and a validation cohort (n = 77). Univariate analysis was performed to identify significant parameters for the prediction of severe AP in the training cohort. For early prediction of disease severity, a classification tree analysis (CTA) model was constructed using significant scoring systems shown by univariate analysis. To assess the diagnostic performance of the model, we compared the area under the ROC curve (AUC) with each selected single parameter. We also evaluated the diagnostic performance in the validation cohort. RESULTS: The Acute Physiology and Chronic Health Evaluation (APACHE)-II score, bedside index for severity in acute pancreatitis (BISAP) score, extrapancreatic inflammation on CT (EPIC) score, and Balthazar grade were included in the CTA model. In the training cohort, our CTA model showed a trend of a higher AUC (0.853) than the AUC of each single parameter (APACHE-II score, 0.835; BISAP score, 0.842; EPIC score, 0.739; Balthazar grade, 0.700) (all, p > 0.0125) while achieving specificity (100%) higher than and accuracy (94.8%) comparable to each single parameter (both, p < 0.0125). In the validation cohort, the CTA model achieved diagnostic performance similar to the training cohort with an AUC of 0.833. CONCLUSION: Our CTA model consisted of clinical (i.e., APACHE-II and BISAP scores) and radiologic (i.e., Balthazar grade and EPIC score) scoring systems and may be useful for the early prediction of the severity of AP and identification of high-risk patients who require close surveillance.
Entities:
Keywords:
acute pancreatitis; classification tree analysis; contrast-enhanced CT; early prediction; severe acute pancreatitis
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