Evidence map›Paper›PMID 41094565›Full record

ArticleEuropean journal of medical research2025

Development and validation of a dynamic nomogram for predicting in-hospital mortality in acute massive cerebral infarction: a retrospective study in a Chinese population.

Xuhui Liu, Xujie Wang, Rongfei Xie, Zhaohui Liu, Shasha Liang, Jinyin Bai, Chunjian Ma

Abstract readValidation Study
In one paragraph

Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xuhui Liu *Department of Neurology, The Second Hospital of Lanzhou University, 82 Cuiying Men, Chengguan District, Lanzhou, 730030, Gansu, China.
Xujie Wang *Department of Emergency ICU, The Affiliated Hospital of Qinghai University, 29 Tongren Road, Chengxi District, Xining, 810001, Qinghai, China.
Rongfei XieDepartment of Emergency ICU, The Affiliated Hospital of Qinghai University, 29 Tongren Road, Chengxi District, Xining, 810001, Qinghai, China.
Zhaohui LiuClinical Medical College of Tianjin Medical University, 167 Dagang Xueyuan Road, Binhai New Area, Tianjin, 300270, China.
Shasha LiangDepartment of Emergency ICU, The Affiliated Hospital of Qinghai University, 29 Tongren Road, Chengxi District, Xining, 810001, Qinghai, China.
Jinyin BaiDepartment of ICU, The Affiliated Hospital of Qinghai University, 29 Tongren Road, Chengxi District, Xining, 810001, Qinghai, China.
Chunjian MaDepartment of Emergency ICU, The Affiliated Hospital of Qinghai University, 29 Tongren Road, Chengxi District, Xining, 810001, Qinghai, China. mcjeicu@163.com.

Funding

Qinghai University 2025-GMKY-14
6 · The paper itself

Abstract

backgroundMassive cerebral infarction (MCI) is a severe form of ischemic stroke that can result in adverse outcomes, including death. This study aimed to identify the independent risk factors associated with MCI mortality by developing a multivariate model using stepwise logistic regression analysis.

methodsThis retrospective study included 159 hospitalized patients between January 15, 2022, and October 20, 2023. The diagnosis of MCI was based on clinical symptoms, the National Institutes of Health Stroke Scale (NIHSS), the Glasgow Coma Scale (GCS), and brain MRI. Potential mortality-related predictors were identified by analyzing patient histories, coagulation profiles, renal function, and serum biochemical indicators such as fasting blood glucose (FBG), homocysteine (HCY), and hemoglobin (Hb).

resultsAmong the 159 patients, optimized multivariate logistic regression analysis revealed that smoking (OR = 10.48, 95% CI 2.85-42.80), FBG (OR = 1.97, 95% CI 1.45-2.82), HCY (OR = 8.62, 95% CI 1.29-76.21), Hb (OR = 0.96, 95% CI 0.94-0.99), and GCS score (OR = 0.67, 95% CI 0.52-0.83) were significantly associated with in-hospital mortality (all P < 0.05). The model showed good discrimination (AUC = 0.943, 95% CI 0.903-0.982), with a marginal R-squared (R

conclusionSmoking, elevated FBG and HCY, low Hb, and lower GCS scores were identified as independent predictors of mortality in MCI patients. Managing these factors may help reduce the risk of death.

Indexed as

Cerebral InfarctionHospital MortalityNomogramsAgedAged, 80 and overChinaEast Asian PeopleFemaleGlasgow Coma ScaleHumansMaleMiddle AgedPrognosisRetrospective StudiesRisk FactorsLogistic regressionMassive cerebral infarction (MCI)MortalityNomogramRisk factors

Identifiers

PMID41094565
PMCPMC12529824

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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.