Studies on A Model for Predicting Readmission of Patients with Acute Pancreatitis

碩士 === 國立陽明大學 === 生物醫學資訊研究所 === 99 === Unplanned readmission results in a significant burden on the health-care system. A recent study showed that the general unplanned readmissions occur in near to 20% of the cases. Based on the data published by National Health Insurance Bureau (2003), digestive d...

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Bibliographic Details
Main Authors: Lu-Cheng Liu, 劉如偵
Other Authors: Jen-Hsiang Chuang
Format: Others
Language:zh-TW
Published: 2011
Online Access:http://ndltd.ncl.edu.tw/handle/67462174149092109096
Description
Summary:碩士 === 國立陽明大學 === 生物醫學資訊研究所 === 99 === Unplanned readmission results in a significant burden on the health-care system. A recent study showed that the general unplanned readmissions occur in near to 20% of the cases. Based on the data published by National Health Insurance Bureau (2003), digestive diseases accounted for the highest rate (26.25%) of hospital admissions. Since acute pancreatitis is one of the major digestive diseases, the purpose of this study is to explore the risk factors of readmission of this population. A retrospective study was adopted. Data have been collected from a medical center in southern Taiwan. Between the year of 2008 and 2010, a total of 305 acute pancreatitis patients were admitted. Sixty-one patients were readmitted within 30 days of discharge. Data from all other 244 patients were also collected as control group. Logistic Regression was used for data analysis. Albumin level, use of Endoscopic retrograde cholangiopancreatography (ERCP) and cholecystectomy stood out as predicting risk factors. Youden Index was used to calculator the cut pointer; the cut pointer is 0.3, prediction model sensitivity was 72.1%, specificity was 76.6%. Area Under the Curve (AUC) value was 0.763; In this study, the leave-one-out-cross-validation method was used for evaluating the performance of the predictive model; validation AUC value of 0.616. Hosmer-Lemeshow Goodness-of-fit test was used to evaluate the calibration of models; the p value was 0.0001. It indicates that the calibration of the predictive model did not performed well. The studied model may not accurately predict readmission of patients with acute pancreatitis in practice. However, we have identified three important risk factors for predicting readmission of acute pancreatitis patients. Hope for the future to further improve the limitations of this study and to improve the performance of the model for predicting acute pancreatitis patients with unplanned readmission as a tool for decision-making in clinical practice.