All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.

Abstract

The Modified Early Warning Score (MEWS): An Instant Physiological Prognostic Indicator of Poor Outcome in Acute Pancreatitis

Context The Modified Early Warning Score (MEWS) is a bedside scoring system that is non-invasive, simple and repeatable to reflect dynamic changes in physiological state. Objective This study aims to assess accuracy of MEWS and determine an optimal MEWS value in predicting severity in acute pancreatitis (AP). Methods A prospective database of consecutive admissions with AP to a single institution was analysed to determine value of MEWS in identifying severe acute pancreatitis (SAP) and predicting poor outcome. Receiver operator curves (ROC) were used to determine optimal accuracy. Sensitivity, specificity, negative predictive value (NPV), and positive predictive values (PPV) were calculated for the optimal MEWS values obtained. Results One-hundred and 42 patients with AP were included. The optimal highest MEWS per 24 hours period (hMEWS) and mean MEWS per 24 hour period (mMEWS) in predicting SAP as determined by ROC were 2.5 and 1.625 respectively; with hMEWS ≥3 and mMEWS > 1 utilised in this cohort as MEWS scores are whole numbers. Onadmission, sensitivity, specificity, NPV, PPV, and accuracy of hMEWS ≥3 was 95.5%, 90.8%, 99%, 65.6% & 92%; and for mMWES > 1 was 95.5%, 87.5%, 99%, 58.3% & 88.7%, both superior than the Imrie score: 31.5%, 92.1%, 88.9%, 40% and 83.5%. The accuracy of hMEWS ≥3 and mMEWS > 1 increased over the subsequent 72 hours (days 0-2) from 92 to 96%, and 89% to 94% respectively. Conclusions MEWS provides a novel, easy, instant, repeatable, reliable prognostic score that may be superior to existing scoring systems. A larger cohort is required to validate these findings.


Author(s): Aravind Suppiah, Deep Malde, Mazin Hamed, Victoria Allgar, Gareth Morris-Stiff, Andrew Smith, Tameem Arab

Abstract | Full-Text | PDF

Share this  Facebook  Twitter  LinkedIn  Google+

Abstracted/Indexed in

  • Index Copernicus
  • Academic Journals Database
  • Genamics JournalSeek
  • Academic Keys
  • China National Knowledge Infrastructure (CNKI)
  • CiteFactor
  • Scimago
  • British Library
  • Electronic Journals Library
  • Directory of Research Journal Indexing (DRJI)
  • WorldCat
  • Emerging Sources Citation Index (ESCI)
  • EBSCO Host
  • MIAR
  • International Committee of Medical Journal Editors (ICMJE)
  • University of Zurich - UZH
  • University Grants Commission
  • SWB Online-Katalog
  • Scholarsteer
  • Geneva Foundation for Medical Education and Research
  • Secret Search Engine Labs
Flyer image