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

作者：Hong, Wandong; Lin, Suhan; Zippi, Maddalena; Geng, Wujun; Stock, Simon; Zimmer, Vincent; Xu, Chunfang^*; Zhou, Mengtao^*

来源：Biomed Research International, 2017, 2017: 1648385.

DOI：10.1155/2017/1648385

• PERSISTENT ORGAN FAILURE
• LIPID PROFILE
• NECROSIS
• POPULATION
• MANAGEMENT
• MORTALITY
• RISK

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-Cat admission and BUN and Scr at 24 hours (hrs) were independently associated with SAP. Alogistic regression 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.

• 出版日期2017
• 单位温州医科大学; 苏州大学