Evidence map›Paper›PMID 41168709›Full record

ArticleBMC gastroenterology2025

Endoscopic intervention for patients of renal insufficiency with upper gastrointestinal bleeding: efficacy evaluation and predictive model construction.

Yongle Huang, Qia Chen, Rirong Chen, Minhu Chen, Xianwen Guo, Zhen Ding

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Article in BMC gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Yongle Huang *Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Qia Chen *Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Rirong ChenDepartment of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Minhu ChenDepartment of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Xianwen GuoDepartment of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. 398919829@qq.com.
Zhen DingDepartment of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. docd720@126.com.

Funding

National Natural Science Foundation of China 82270675
6 · The paper itself

Abstract

objectiveTo explore disease severity thresholds beyond which endoscopic intervention fails to reduce mortality in the context of renal insufficiency and upper gastrointestinal bleeding (UGIB), and to develop predictive models for mortality.

methodsThis study retrospectively collected data from the Medical Information Mart for Intensive Care (MIMIC-IV). Disease severity thresholds were explored using restricted cubic splines. Patients in our hospital (n = 37) were included as an external validation cohort. Logistic regression was used to identify independent factors for mortality. Predictive models were developed using Logistic regression, LASSO, XGBoost, and random forest algorithms. Models were evaluated in the hospital cohort and the National Inpatient Sample (NIS, n = 18248) database.

resultsIn the context of renal insufficiency and UGIB, compared with the non-endoscopic group (n = 372), patients receiving endoscopic intervention (n = 1177) had lower in-hospital mortality (12.74% vs. 21.77%, p < 0.001). However, no significant difference was observed when creatinine exceeding 354 µmol/L (p = 0.075). In the hospital cohort, no improvement on mortality, advanced respiratory or circulatory support was observed in patients with creatinine over 354 µmol/L (p = 0.335). For patients receiving endoscopic intervention, hemorrhagic stroke, disseminated intravascular coagulation, acute respiratory failure, hemodialysis, non-ST-elevation myocardial infarction, hypovolemic shock, acute kidney injury, and age emerged as independent risk factors for mortality. The Logistic predictive model was developed, achieving an area under the receiver operating characteristic curve (AUROC) of 0.851, 0.809, and 1.000 in the test set, the NIS cohort, and the hospital cohort, respectively.

conclusionsIn the context of renal insufficiency and UGIB, endoscopic intervention failed to improve mortality when creatinine > 354 µmol/L, and comprehensive evaluations should be made in such cases. Independent risk factors for mortality in patients receiving endoscopic intervention were revealed, and the Logistic model stratified risk of mortality effectively.

Indexed as

Endoscopy, GastrointestinalGastrointestinal HemorrhageRenal InsufficiencyAgedCreatinineFemaleHospital MortalityHumansLogistic ModelsMaleMiddle AgedPrediction AlgorithmsRetrospective StudiesRisk FactorsROC CurveSeverity of Illness IndexCreatinineEndoscopic InterventionIn-hospital MortalityPredictive ModelRenal InsufficiencyUpper Gastrointestinal Bleeding

Identifiers

PMID41168709
PMCPMC12574185

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.