Three Simple Parameters on Admission to the Emergency Department are Predictors for Endoscopic Intervention in

Fatih Acehan1, Buket Karsavuranoğlu1, Cagdas Kalkan2

  • 1Department of Internal Medicine, Ankara City Hospital, Ankara, Turkey.

PubMed

Insights

Predicting the need for endoscopic intervention in nonvariceal upper gastrointestinal bleeding (UGIB) is crucial. Syncope, mean arterial pressure (MAP), and blood urea nitrogen (BUN) at admission are key predictors, simplifying risk assessment.

Area of Science:

  • Gastroenterology
  • Emergency Medicine
  • Clinical Prediction Models

Background:

  • Nonvariceal upper gastrointestinal bleeding (UGIB) frequently necessitates endoscopic intervention.
  • Accurate prediction of this need is vital for timely and appropriate patient management.

Purpose of the Study:

  • To identify factors predicting the requirement for endoscopic intervention upon emergency department admission.
  • To evaluate the efficacy of clinical and laboratory findings in predicting the need for endoscopic therapy.

Main Methods:

  • Retrospective review of consecutive patients with gastrointestinal hemorrhage (ICD-10 code K92.2) undergoing upper endoscopy.
  • Comparison of clinical and laboratory data between patients receiving endoscopic treatment and those not.
  • Multivariate regression analysis to determine independent predictors of endoscopic intervention.

Main Results:

  • Syncope, mean arterial pressure (MAP), and blood urea nitrogen (BUN) at admission were identified as independent predictors for endoscopic intervention.
  • The combination of syncope, MAP, and BUN demonstrated predictive capability for endoscopic intervention.
  • This combination showed superior prediction compared to Rockall and AIMS65 scores, but was comparable to the Glasgow-Blatchford score.

Conclusions:

  • Syncope, MAP, and BUN are practical and effective predictors for endoscopic therapy in nonvariceal UGIB.
  • These simple parameters offer a more accessible alternative to complex scoring systems for predicting intervention needs.
  • The findings support the integration of these parameters into clinical decision-making for UGIB management.
Abstract

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