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Predictors of mortality and endoscopic intervention in patients with upper gastrointestinal bleeding in the intensive
Vijaya L Rao1, Nina Gupta1, Eric Swei2
1Section of Gastroenterology, Hepatology and Nutrition, Department of Internal Medicine, University of Chicago Medicine, Chicago, IL, USA.
Background:
The outcomes of patients undergoing esophagogastroduodenoscopy (EGD) in the intensive care unit (ICU) for upper gastrointestinal bleeding (UGIB) are not well described. Our aims were to determine predictors of 30-day mortality and endoscopic intervention, and assess the utility of existing clinical-prediction tools for UGIB in this population.
Methods:
Patients hospitalized in an ICU between 2008 and 2015 who underwent EGD were identified using a validated, machine-learning algorithm. Logistic regression was used to determine factors associated with 30-day mortality and endoscopic intervention. Area under receiver-operating characteristics (AUROC) analysis was used to evaluate established UGIB scoring systems in predicting mortality and endoscopic intervention in patients who presented to the hospital with UGIB.
Results:
A total of 606 patients underwent EGD for UGIB while admitted to an ICU. The median age of the cohort was 62 years and 55.9% were male. Multivariate analysis revealed that predictors associated with 30-day mortality included American Society of Anesthesiologists (ASA) class (odds ratio [OR] 4.1, 95% confidence interval [CI] 2.2-7.9), Charlson score (OR 1.2, 95% CI 1.0-1.3), and duration from hospital admission to EGD (OR 1.04, 95% CI 1.01-1.07). Rockall, Glasgow-Blatchford, and AIMS65 scores were poorly predictive of endoscopic intervention (AUROC: 0.521, 0.514, and 0.540, respectively) and in-hospital mortality (AUROC: 0.510, 0.568, and 0.506, respectively).
Conclusions:
Predictors associated with 30-day mortality include ASA classification, Charlson score, and duration in the hospital prior to EGD. Existing risk tools are poorly predictive of clinical outcomes, which highlights the need for a more accurate risk-stratification tool to predict the benefit of intervention within the ICU population.
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