» Articles » PMID: 21907980

A Simple Risk Score Accurately Predicts In-hospital Mortality, Length of Stay, and Cost in Acute Upper GI Bleeding

Overview
Date 2011 Sep 13
PMID 21907980
Citations 155
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Although the early use of a risk stratification score in upper GI bleeding is recommended, existing risk scores are not widely used in clinical practice.

Objective: We sought to develop and validate an easily calculated bedside risk score, AIMS65, by using data routinely available at initial evaluation.

Design: Data from patients admitted from the emergency department with acute upper GI bleeding were extracted from a database containing information from 187 U.S. hospitals. Recursive partitioning was applied to derive a risk score for in-hospital mortality by using data from 2004 to 2005 in 29,222 patients. The score was validated by using data from 2006 to 2007 in 32,504 patients. Accuracy to predict mortality was assessed by the area under the receiver operating characteristic (AUROC) curve.

Main Outcome Measurements: Mortality, length of stay (LOS), and cost of admission.

Results: The 5 factors present at admission with the best discrimination were albumin less than 3.0 g/dL, international normalized ratio greater than 1.5, altered mental status, systolic blood pressure 90 mm Hg or lower, and age older than 65 years. For those with no risk factors, the mortality rate was 0.3% compared with 31.8% in patients with all 5 (P < .001). The model had a high predictive accuracy (AUROC = 0.80; 95% CI, 0.78-0.81), which was confirmed in the validation cohort (AUROC = 0.77, 95% CI, 0.75-0.79). Longer LOS and increased costs were seen with higher scores (P < .001).

Limitations: Database data used does not include outcomes such as rebleeding.

Conclusions: AIMS65 is a simple, accurate risk score that predicts in-hospital mortality, LOS, and cost in patients with acute upper GI bleeding.

Citing Articles

Machine Learning-Based Mortality Prediction for Acute Gastrointestinal Bleeding Patients Admitted to Intensive Care Unit.

Liu Z, Zhang L, Jiang G, Chen Q, Hou Y, Wu W Curr Med Sci. 2025; 45(1):70-81.

PMID: 40014197 DOI: 10.1007/s11596-025-00022-6.


New machine-learning models outperform conventional risk assessment tools in Gastrointestinal bleeding.

Boros E, Pinter J, Molontay R, Proszeky K, Vorhendi N, Simon O Sci Rep. 2025; 15(1):6371.

PMID: 39984590 PMC: 11845789. DOI: 10.1038/s41598-025-90986-1.


Prophylactic transarterial embolization in patients with bleeding peptic ulcers following endoscopic control of bleeding.

Zetner D, Roost I, Rosenberg J, Andresen K Cochrane Database Syst Rev. 2025; 2:CD014999.

PMID: 39927555 PMC: 11808832. DOI: 10.1002/14651858.CD014999.pub2.


Which scoring systems are useful for predicting the prognosis of lower gastrointestinal bleeding? Old and new.

Jeong K, Moon H, In K, Kang S, Sung J, Jeong H BMC Gastroenterol. 2025; 25(1):49.

PMID: 39891040 PMC: 11786468. DOI: 10.1186/s12876-025-03638-z.


A novel predictive model for Intensive Care Unit admission in Emergency Department patients with upper gastrointestinal bleeding.

Yang J, Han S, Nah S, Chung S Medicine (Baltimore). 2025; 103(47):e40440.

PMID: 39809218 PMC: 11596417. DOI: 10.1097/MD.0000000000040440.