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Published on: October 16, 2013
Clinical utility of pre-endoscopy risk scores in upper gastrointestinal bleeding
Jennifer Tham1, Adrian Stanley1
1Department of Gastroenterology, Glasgow Royal Infirmary, Glasgow, UK.
Introduction: Acute upper-gastrointestinal bleeding (AUGIB) is a common medical emergency, with an incidence of 103-172 per 100,000 in the United Kingdom (UK) and mortality of 2% to 10%. Early and accurate prediction of the severity of an AUGIB episode may help guide management, including in or outpatient management, level of care required, and timing of endoscopy. This article aims to address the clinical utility of the various pre-endoscopic risk assessment tools used in AUGIB.Areas covered: The authors undertook a literature review of the current evidence on the pre-endoscopic risk assessment scores. Additional the authors discuss the recently published novel risk assessment scores.Expert opinion: The evidence shows that GBS is the most clinically useful risk assessment score in correctly identifying very low-risk patients suitable for outpatient management. At present, research is ongoing to assess machine learning in the assessment of patients presenting with AUGIB. More research is needed but it shows promise for the future.
Introduction: Acute upper-gastrointestinal bleeding (AUGIB) is a common medical emergency, with an incidence of 103-172 per 100,000 in the United Kingdom (UK) and mortality of 2% to 10%. Early and accurate prediction of the severity of an AUGIB episode may help guide management, including in or outpatient management, level of care required, and timing of endoscopy. This article aims to address the clinical utility of the various pre-endoscopic risk assessment tools used in AUGIB.Areas covered: The authors undertook a literature review of the current evidence on the pre-endoscopic risk assessment scores. Additional the authors discuss the recently published novel risk assessment scores.Expert opinion: The evidence shows that GBS is the most clinically useful risk assessment score in correctly identifying very low-risk patients suitable for outpatient management. At present, research is ongoing to assess machine learning in the assessment of patients presenting with AUGIB. More research is needed but it shows promise for the future.
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