Yang Fei1, Kun Gao1, Jianfeng Tu1, Wei Wang2, Guang-Quan Zong2, Wei-Qin Li3. 1. Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, No. 305 Zhongshan East Road, Nanjing, 210002, China. 2. Department of General Surgery, The 81st Hospital of P.L.A./Bayi Hospital Affiliated Nanjing University of Chinese Medicine, Nanjing, 210002, China. 3. Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, No. 305 Zhongshan East Road, Nanjing, 210002, China. Electronic address: chamskuler@163.com.
Abstract
OBJECT: Acute pancreatitis (AP) keeps as severe medical diagnosis and treatment problem. Early evaluation for severity and risk stratification in patients with AP is very important. Some scoring system such as acute physiology and chronic health evaluation-II (APACHE-II), the computed tomography severity index (CTSI), Ranson's score and the bedside index of severity of AP (BISAP) have been used, nevertheless, there're a few shortcomings in these methods. The aim of this study was to construct a new modeling including intra-abdominal pressure (IAP) and body mass index (BMI) to evaluate the severity in AP. METHODS: The study comprised of two independent cohorts of patients with AP, one set was used to develop modeling from Jinling hospital in the period between January 2013 and October 2016, 1073 patients were included in it; another set was used to validate modeling from the 81st hospital in the period between January 2012 and December 2016, 326 patients were included in it. The association between risk factors and severity of AP were assessed by univariable analysis; multivariable modeling was explored through stepwise selection regression. The change in IAP and BMI were combined to generate a regression equation as the new modeling. Statistical indexes were used to evaluate the value of the prediction in the new modeling. RESULTS: Univariable analysis confirmed change in IAP and BMI to be significantly associated with severity of AP. The predict sensitivity, specificity, positive predictive value, negative predictive value and accuracy by the new modeling for severity of AP were 77.6%, 82.6%, 71.9%, 87.5% and 74.9% respectively in the developing dataset. There were significant differences between the new modeling and other scoring systems in these parameters (P < 0.05). In addition, a comparison of the area under receiver operating characteristic curves of them showed a statistically significant difference (P < 0.05). The same results could be found in the validating dataset. CONCLUSIONS: A new modeling based on IAP and BMI is more likely to predict the severity of AP.
OBJECT: Acute pancreatitis (AP) keeps as severe medical diagnosis and treatment problem. Early evaluation for severity and risk stratification in patients with AP is very important. Some scoring system such as acute physiology and chronic health evaluation-II (APACHE-II), the computed tomography severity index (CTSI), Ranson's score and the bedside index of severity of AP (BISAP) have been used, nevertheless, there're a few shortcomings in these methods. The aim of this study was to construct a new modeling including intra-abdominal pressure (IAP) and body mass index (BMI) to evaluate the severity in AP. METHODS: The study comprised of two independent cohorts of patients with AP, one set was used to develop modeling from Jinling hospital in the period between January 2013 and October 2016, 1073 patients were included in it; another set was used to validate modeling from the 81st hospital in the period between January 2012 and December 2016, 326 patients were included in it. The association between risk factors and severity of AP were assessed by univariable analysis; multivariable modeling was explored through stepwise selection regression. The change in IAP and BMI were combined to generate a regression equation as the new modeling. Statistical indexes were used to evaluate the value of the prediction in the new modeling. RESULTS: Univariable analysis confirmed change in IAP and BMI to be significantly associated with severity of AP. The predict sensitivity, specificity, positive predictive value, negative predictive value and accuracy by the new modeling for severity of AP were 77.6%, 82.6%, 71.9%, 87.5% and 74.9% respectively in the developing dataset. There were significant differences between the new modeling and other scoring systems in these parameters (P < 0.05). In addition, a comparison of the area under receiver operating characteristic curves of them showed a statistically significant difference (P < 0.05). The same results could be found in the validating dataset. CONCLUSIONS: A new modeling based on IAP and BMI is more likely to predict the severity of AP.
Authors: Ari Leppäniemi; Matti Tolonen; Antonio Tarasconi; Helmut Segovia-Lohse; Emiliano Gamberini; Andrew W Kirkpatrick; Chad G Ball; Neil Parry; Massimo Sartelli; Daan Wolbrink; Harry van Goor; Gianluca Baiocchi; Luca Ansaloni; Walter Biffl; Federico Coccolini; Salomone Di Saverio; Yoram Kluger; Ernest Moore; Fausto Catena Journal: World J Emerg Surg Date: 2019-06-13 Impact factor: 5.469
Authors: Alexandra Mikó; Éva Vigh; Péter Mátrai; Alexandra Soós; András Garami; Márta Balaskó; László Czakó; Bernadett Mosdósi; Patrícia Sarlós; Bálint Erőss; Judit Tenk; Ildikó Rostás; Péter Hegyi Journal: Front Physiol Date: 2019-08-27 Impact factor: 4.566