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Updated: Oct 11, 2025

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
Published on: February 16, 2024
Validation and modification of existing mortality prediction models for lower gastrointestinal bleeding: a
Hyun Seok Lee1, Hee Seok Moon2, In Sun Kwon3
1Division of Gastroenterology, Department of Internal Medicine, Chungnam National University Hospital, Chungnam National University School of Medicine, 282, Munhwa-ro, Juong-gu, Daejeon, 35015, Republic of Korea.
Background:
Lower gastrointestinal bleeding (LGIB) often subsides without medical intervention; however, in some cases, the bleeding does not stop and the patient's condition worsens. Therefore, predicting severe LGIB in advance can aid treatment. This study aimed to evaluate variables related to mortality from LGIB and propose a scoring system.
Methods:
In this retrospective study, we reviewed the medical records of patients who visited the emergency room with hematochezia between January 2016 and December 2020. Through regression analysis of comorbidities, medications, vital signs, laboratory investigations, and duration of hospital stay, variables related to LGIB-related mortality were evaluated. A scoring system was developed and the appropriateness with an area under the receiver operating characteristics curve (AUROC) was evaluated and compared with other existing models.
Results:
A total of 932 patients were hospitalized for LGIB. Variables associated with LGIB-related mortality were the presence of cancer, heart rate > 100 beats/min, blood urea nitrogen level ≥ 30 mg/dL, an international normalized ratio > 1.50, and albumin level ≤ 3.0 g/dL. The AUROCs of the models CNUH-4 and CNUH-5 were 0.890 (p < 0.001; cutoff, 2.5; 95% confidence interval, 0.0851-0.929) and 0.901 (p < 0.001; cutoff, 3.5; 95% confidence interval, 0.869-0.933), respectively.
Conclusions:
The model developed for predicting the risk of LGIB-related mortality is simple and easy to apply clinically. The AUROC of the model was better than that of the existing models.

