high-density lipoprotein cholesterol, blood urea nitrogen, and serum creatinine can predict severe acute pancreatitis

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2017
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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.
Reference Key
hong2017biomedhigh-density Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Wandong Hong;Suhan Lin;Maddalena Zippi;Wujun Geng;Simon Stock;Vincent Zimmer;Chunfang Xu;Mengtao Zhou
Journal spectrochimica acta - part a: molecular and biomolecular spectroscopy
Year 2017
DOI
10.1155/2017/1648385
URL
http://dx.doi.org/10.1155/2017/1648385
Keywords
Medicine

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