Evidence map›Paper›PMID 42231232›Full record

ArticleBMC nephrology2026

Endothelial activation and stress index associated with in-hospital mortality risk in patients with end-stage renal disease: a retrospective analysis based on the MIMIC database and machine learning model development.

Liandong Chen, Bin Fu

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In one paragraph

Article in BMC nephrology, 2026. 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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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Liandong ChenThe Fifth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, 310053, China.
Bin FuInstitute of Digital Traditional Chinese Medicine, Zhejiang Chinese Medical University, 548 Binwen Road, Binjiang District, Hangzhou, Zhejiang, 310053, China. fubin@zcmu.edu.cn.

Funding

The Second Batch of Provincial-Level Teaching Reform Project for Postgraduates in Zhejiang Province During the 14th Five-Year Plan Period JGCG2024254Traditional Chinese Medicine Science and Technology Plan of Zhejiang Province 2026ZF42Youth Fund Project of Humanities and Social Sciences Research of Ministry of Education in 2024 24YJCZH448
6 · The paper itself

Abstract

backgroundEnd-stage renal disease (ESRD) is a severe chronic renal disorder with high mortality, requiring dialysis treatment or kidney transplantation. The endothelial activation and stress index (EASIX), reflecting inflammatory status and endothelial dysfunction, has demonstrated predictive value for outcomes in multiple diseases. However, its association with in-hospital mortality (IHM) risk in ESRD patients remains unclear, and machine learning prediction models remain unestablished.

methodsClinical data for ESRD patients were extracted from the MIMIC-IV (3.1) database. The outcome was IHM. The link between EASIX and IHM risk was explored using Cox proportional hazards regression, Kaplan-Meier survival curves, restricted cubic splines (RCS), and subgroup analysis. The Boruta algorithm and LASSO regression were employed to screen important features. Multiple models were established using machine learning algorithms, validated, and compared. SHAP analysis was applied to the optimal model.

resultsThe study included 997 ESRD patients, with 194 in-hospital deaths, accounting for 19.5% of the total cohort. The Kaplan-Meier curve revealed the highest IHM rate among patients with the highest EASIX level (Q4). Elevated EASIX levels showed a significant positive link with an enhanced IHM risk in ESRD patients (HR [95% CI] = 2.086 [1.697, 2.564]). RCS showed an approximate linear relationship, with this association consistent across multiple subgroups. The gradient boosting machine was identified as the optimal model (AUC of training set = 0.822, AUC of validation set = 0.763). SHAP analysis identified EASIX as a key contributor to IHM risk.

conclusionEASIX is significantly positively correlated with an elevated IHM risk in ESRD patients, making it a crucial predictor of IHM in ESRD. EASIX can serve as an early predictive tool to guide the identification, intervention, and prevention of adverse outcomes in ESRD. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Endothelium, VascularHospital MortalityKidney Failure, ChronicMachine LearningAgedDatabases, FactualFemaleHumansMaleMiddle AgedPredictive Learning ModelsRetrospective StudiesEndothelial activation and stress indexEnd-stage renal diseaseIn-hospital mortalityMachine learningMIMIC-IV

Identifiers

PMID42231232
PMCPMC13374280

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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.