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BioMed Research International
Volume 2017, Article ID 1648385, 7 pages
https://doi.org/10.1155/2017/1648385
Research Article

High-Density Lipoprotein Cholesterol, Blood Urea Nitrogen, and Serum Creatinine Can Predict Severe Acute Pancreatitis

1Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
2Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
3Unit of Gastroenterology and Digestive Endoscopy, Sandro Pertini Hospital, Rome, Italy
4Department of Anesthesiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
5Department of Surgery, World Mate Emergency Hospital, Battambang, Cambodia
6Department of Medicine II, Saarland University Medical Center, Kirrberger Str., 66421 Homburg, Germany
7Department of Medicine, Marienhausklinik St. Josef Kohlhof, Neunkirchen, Germany
8Department of Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China

Correspondence should be addressed to Chunfang Xu; moc.anis@ydutsfcx and Mengtao Zhou; moc.anis@oatgnemuohzyduts

Received 26 May 2017; Revised 2 July 2017; Accepted 24 July 2017; Published 22 August 2017

Academic Editor: Valeria Rolla

Copyright © 2017 Wandong Hong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background and Aims. Early prediction of disease severity of acute pancreatitis (AP) would be helpful for triaging patients to the appropriate level of care and intervention. The aim of the study was to develop a model able to predict Severe Acute Pancreatitis (SAP). Methods. A total of 647 patients with AP were enrolled. The demographic data, hematocrit, High-Density Lipoprotein Cholesterol (HDL-C) determinant at time of admission, Blood Urea Nitrogen (BUN), and serum creatinine (Scr) determinant at time of admission and 24 hrs after hospitalization were collected and analyzed statistically. Results. Multivariate logistic regression indicated that HDL-C at admission and BUN and Scr at 24 hours (hrs) were independently associated with SAP. A logistic regression function (LR model) was developed to predict SAP as follows: −2.25–0.06 HDL-C (mg/dl) at admission + 0.06 BUN (mg/dl) at 24 hours + 0.66 Scr (mg/dl) at 24 hours. The optimism-corrected c-index for LR model was 0.832 after bootstrap validation. The area under the receiver operating characteristic curve for LR model for the prediction of SAP was 0.84. Conclusions. The LR model consists of HDL-C at admission and BUN and Scr at 24 hours, representing an additional tool to stratify patients at risk of SAP.