Published in:
Open Access 01-12-2018 | Research article
Comparison of AIMS65, Glasgow–Blatchford and Rockall scoring approaches in predicting the risk of in-hospital death among emergency hospitalized patients with upper gastrointestinal bleeding: a retrospective observational study in Nanjing, China
Published in: BMC Gastroenterology | Issue 1/2018
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Background
This study aims to compare the performance of AIMS65, Glasgow–Blatchford (GBS) and Rockall scores (RS) in predicting the death risk among emergency-hospitalized patients with upper gastrointestinal bleeding (UGIB) in regional China.
Methods
A retrospective study was implemented between January 2014 and December 2015. Eligible participants were those who were hospitalized with UGIB. The outcome variable was in-hospital death, while explanatory variables were AIMS65, GBS and RS scores. Odds ratios (OR) and 95% confidence interval (CI) were estimated to assess the association of AIMS65, GBS and RS with death risk using multivariate logistic regression models. The areas under the receiver operating characteristics curve (AUC) of three scoring systems were computed to compare their predictive power.
Results
Among 799 UGIB participants, 674 were non-variceal bleeding (NVUGIB) and 125 variceal bleeding (VUGIB) patients. AIMS65 (OR = 14.72, 95% CI = 6.48, 33.43) and RS (OR = 1.60, 95% CI = 1.20, 2.13) were positively associated with the risk of in-hospital death. Moreover, AIMS65 (AUC = 0.91, 95% CI = 0.84, 0.98) performed the best in predicting in-hospital death, followed by RS (AUC = 0.79, 95% CI = 0.72, 0.86) and GBS (AUC = 0.71, 95% CI = 0.59, 0.83) among overall UGIB participants. AIMS65 was also the best indicator to predict in-hospital death among either NVUGIB participants (AUC = 0.89, 95% CI = 0.80, 0.98) or VUGIB participants (AUC = 0.94, 95% CI = 0.89, 1.00).
Conclusions
AIMS65, GBS and RS scoring approaches were all acceptable for predicting in-hospital death among UGIB patients irrespective of the subtype of UGIB in China. The AIMS65 might be the most powerful predictor.