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Bechien U. Wu

Bio: Bechien U. Wu is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Acute pancreatitis & Pancreatitis. The author has an hindex of 27, co-authored 70 publications receiving 3054 citations. Previous affiliations of Bechien U. Wu include Harvard University & University of Southern California.


Papers
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Journal ArticleDOI
01 Dec 2008-Gut
TL;DR: The BISAP is a simple and accurate method for the early identification of patients at increased risk for in-hospital mortality in acute pancreatitis.
Abstract: Background: Identification of patients at risk for mortality early in the course of acute pancreatitis (AP) is an important step in improving outcome. Methods: Using Classification and Regression Tree (CART) analysis, a clinical scoring system was developed for prediction of in-hospital mortality in AP. The scoring system was derived on data collected from 17 992 cases of AP from 212 hospitals in 2000–2001. The new scoring system was validated on data collected from 18 256 AP cases from 177 hospitals in 2004–2005. The accuracy of the scoring system for prediction of mortality was measured by the area under the receiver operating characteristic curve (AUC). The performance of the new scoring system was further validated by comparing its predictive accuracy with that of Acute Physiology and Chronic Health Examination (APACHE) II. Results: CART analysis identified five variables for prediction of in-hospital mortality. One point is assigned for the presence of each of the following during the first 24 h: blood urea nitrogen (BUN) >25 mg/dl; impaired mental status; systemic inflammatory response syndrome (SIRS); age >60 years; or the presence of a pleural effusion (BISAP). Mortality ranged from >20% in the highest risk group to Conclusions: A new mortality-based prognostic scoring system for use in AP has been derived and validated. The BISAP is a simple and accurate method for the early identification of patients at increased risk for in-hospital mortality.

601 citations

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TL;DR: Patients with acute pancreatitis who were resuscitated with lactated Ringer's solution had reduced systemic inflammation compared with those who received saline.

421 citations

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TL;DR: The existing scoring systems seem to have reached their maximal efficacy in predicting persistent organ failure in acute pancreatitis, and 12 predictive rules that combined these scores to optimize predictive accuracy are developed.

305 citations

Journal ArticleDOI
TL;DR: The BISAP score represents a simple way to identify patients at risk of increased mortality and the development of intermediate markers of severity within 24 h of presentation and can be utilized to improve clinical care and facilitate enrollment in clinical trials.

239 citations

Journal ArticleDOI
TL;DR: The severity of acute pancreatitis is greater among patients with SirS on day 1 and, in particular, among those with 3 or 4 SIRS criteria, compared with those without SIRs on day1.

226 citations


Cited by
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Journal ArticleDOI
01 Jan 2013-Gut
TL;DR: This international, web-based consensus provides clear definitions to classify acute pancreatitis using easily identified clinical and radiologic criteria and should encourage widespread adoption.
Abstract: Background and objective The Atlanta classification of acute pancreatitis enabled standardised reporting of research and aided communication between clinicians. Deficiencies identified and improved understanding of the disease make a revision necessary. Methods A web-based consultation was undertaken in 2007 to ensure wide participation of pancreatologists. After an initial meeting, the Working Group sent a draft document to 11 national and international pancreatic associations. This working draft was forwarded to all members. Revisions were made in response to comments, and the web-based consultation was repeated three times. The final consensus was reviewed, and only statements based on published evidence were retained. Results The revised classification of acute pancreatitis identified two phases of the disease: early and late. Severity is classified as mild, moderate or severe. Mild acute pancreatitis, the most common form, has no organ failure, local or systemic complications and usually resolves in the first week. Moderately severe acute pancreatitis is defined by the presence of transient organ failure, local complications or exacerbation of co-morbid disease. Severe acute pancreatitis is defined by persistent organ failure, that is, organ failure >48 h. Local complications are peripancreatic fluid collections, pancreatic and peripancreatic necrosis (sterile or infected), pseudocyst and walled-off necrosis (sterile or infected). We present a standardised template for reporting CT images. Conclusions This international, web-based consensus provides clear definitions to classify acute pancreatitis using easily identified clinical and radiologic criteria. The wide consultation among pancreatologists to reach this consensus should encourage widespread adoption.

3,415 citations

Journal ArticleDOI
TL;DR: As the diagnosis of AP is most often established by clinical symptoms and laboratory testing, contrast-enhanced computed tomography and/or magnetic resonance imaging of the pancreas should be reserved for patients in whom the diagnosis is unclear or who fail to improve clinically.

1,657 citations

DOI
Johnson C D, Besselink M G, Carter R1, 阮戈冲, 吴东 
18 Jul 2017
TL;DR: There is a wide spectrum of disease from mild (80%), where patients recover within a few days, to severe (20%) with prolonged hospital stay, the need for critical care support, and a 15-20% risk of death.
Abstract: Acute pancreatitis is inflammation of the pancreas; it is sometimes associated with a systemic inflammatory response that can impair the function of other organs or systems. The inflammation may settle spontaneously or may progress to necrosis of the pancreas or surrounding fatty tissue. The distant organ or system dysfunction may resolve or may progress to organ failure. Thus there is a wide spectrum of disease from mild (80%), where patients recover within a few days, to severe (20%) with prolonged hospital stay, the need for critical care support, and a 15-20% risk of death. If patients have organ failure during the first week in hospital, it is usually already present on the first day in hospital. This early organ failure may resolve in response to treatment. The diagnosis of severe acute pancreatitis depends on the presence of persistent organ failure (>48 hours) either during the first week or at a later stage, and also on the presence of local complications (usually apparent after the first week).

1,399 citations

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TL;DR: The 2012 IAP/APA guidelines provide recommendations concerning key aspects of medical and surgical management of acute pancreatitis based on the currently available evidence that should serve as a reference standard for current management and guide future clinical research on acute Pancreatitis.

1,396 citations

Journal ArticleDOI
08 May 2018
TL;DR: A representation of patients’ entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format is proposed, and it is demonstrated that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization.
Abstract: Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient’s record. We propose a representation of patients’ entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93–0.94), 30-day unplanned readmission (AUROC 0.75–0.76), prolonged length of stay (AUROC 0.85–0.86), and all of a patient’s final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient’s chart.

1,388 citations