Author:
Xue Zaiyao,Che Hebin,Xie Deyou,Ren Jiefeng,Si Quanjin
Abstract
Abstract
Background
Upper gastrointestinal bleeding (UGIB) in older patients is associated with substantial in-hospital morbidity and mortality. This study aimed to develop and validate a simplified risk score for predicting 30-day in-hospital mortality in this population.
Methods
A retrospective analysis was conducted on data from 1899 UGIB patients aged ≥ 65 years admitted to a single medical center between January 2010 and December 2019. An additional cohort of 330 patients admitted from January 2020 to October 2021 was used for external validation. Variable selection was performed using five distinct methods, and models were generated using generalized linear models, random forest, support vector machine, and k-nearest neighbors approaches. The developed score, “ABCAP,” incorporated Albumin < 30 g/L, Blood Urea Nitrogen (BUN) > 7.5 mmol/L, Cancer presence, Altered mental status, and Pulse rate > 100/min, each assigned a score of 1. Internal and external validation procedures compared the ABCAP score with the AIMS65 score.
Results
In internal validation, the ABCAP score demonstrated robust predictive capability with an area under the curve (AUC) of 0.878 (95% CI: 0.824–0.932), which was significantly better than the AIMS65 score (AUC: 0.827, 95% CI: 0.751–0.904), as revealed by the DeLong test (p = 0.048). External validation of the ABCAP score resulted in an AUC of 0.799 (95% CI: 0.709–0.889), while the AIMS65 score yielded an AUC of 0.743 (95% CI: 0.647–0.838), with no significant difference between the two scores based on the DeLong test (p = 0.16). However, the ABCAP score at the 3–5 score level demonstrated superior performance in identifying high-risk patients compared to the AIMS65 score. This score exhibited consistent predictive accuracy across variceal and non-variceal UGIB subgroups.
Conclusions
The ABCAP score incorporates easily obtained clinical variables and demonstrates promising predictive ability for 30-day in-hospital mortality in older UGIB patients. It allows effective mortality risk stratification and showed slightly better performance than the AIMS65 score. Further cohort validation is required to confirm generalizability.
Funder
2019 Medical Big Data and Artificial Intelligence Research and Development Project
Publisher
Springer Science and Business Media LLC