Evidence map›Paper›PMID 39923097›Full record

ArticleArthritis research & therapy2025

Associations between age, red cell distribution width and 180-day and 1-year mortality in giant cell arteritis patients: mediation analyses and machine learning in a cohort study.

Si Chen, Rui Nie, Xiaoran Shen, Yan Wang, Haixia Luan, Xiaoli Zeng, Yanhua Chen, Hui Yuan

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Article in Arthritis research & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Si ChenDepartment of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical University, Anzhen Road No. 2, Chaoyang District, Beijing, 100029, China.
Rui NieDepartment of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical University, Anzhen Road No. 2, Chaoyang District, Beijing, 100029, China.
Xiaoran ShenDepartment of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical University, Anzhen Road No. 2, Chaoyang District, Beijing, 100029, China.
Yan WangDepartment of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical University, Anzhen Road No. 2, Chaoyang District, Beijing, 100029, China.
Haixia LuanDepartment of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical University, Anzhen Road No. 2, Chaoyang District, Beijing, 100029, China.
Xiaoli ZengDepartment of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical University, Anzhen Road No. 2, Chaoyang District, Beijing, 100029, China.
Yanhua ChenSchool of Computer and Artificial Intelligence, Zhengzhou University, No. 100, Science Avenue, High-Tech Zone, Zhengzhou, Henan, 450001, China. ieyanhuachen@zzu.edu.cn.
Hui YuanDepartment of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical University, Anzhen Road No. 2, Chaoyang District, Beijing, 100029, China. 18911662931@189.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe aim of this study was to investigate the correlation between age, red cell distribution width (RDW) levels, and 180-day and 1-year mortality in giant cell arteritis (GCA) patients hospitalized or admitted to the ICU.

methodsClinical data from GCA patients were extracted from the MIMIC-IV (3.0) database. Logistic and Cox regression analyses, Kaplan-Meier (KM) survival analysis, restricted cubic spline (RCS) analysis, and mediation effect analysis were employed to investigate the association between age, RDW levels, and 180-day and 1-year mortality in GCA patients hospitalized or admitted to the ICU. Predictive models were constructed using machine learning algorithms, and SHapley Additive exPlanations (SHAP) analysis was applied to evaluate the contributions of age and RDW levels to mortality in this patient population.

resultsA total of 228 GCA patients were eligible for analysis. Our study identified both age and RDW levels (both with OR > 1, P < 0.05) as significant predictors of 180-day and 1-year mortality in GCA patients hospitalized or admitted to the ICU using multivariate logistic regression analysis. In multivariate Cox regression analysis, both age and RDW (both with HR > 1, P < 0.05) also emerged as prognostic risk factors for 180-day and 1-year mortality in this patient population. KM survival analysis further showed that GCA patients hospitalized or admitted to the ICU with higher age or elevated RDW levels had significantly lower survival rates compared to younger patients or those with lower RDW levels (P < 0.0001). Moreover, RCS analysis indicated a strong nonlinear relationship between RDW levels (threshold: 17.53%) and 1-year mortality in this population. Additionally, RDW levels were found to modestly mediate the relationship between age (per 10-year increase) and 180-day or 1-year mortality in GCA patients hospitalized or admitted to the ICU. The results of the machine learning analysis indicated that the model built using the random forest algorithm performed the best, with an area under the curve of 0.879. Furthermore, SHAP analysis revealed that both age and RDW levels made significant contributions to the prediction of mortality in GCA patients hospitalized or admitted to the ICU.

conclusionsOlder age and higher RDW levels were identified as independent risk factors for increased 180-day and 1-year mortality in GCA patients hospitalized or admitted to the ICU. Furthermore, elevated RDW levels modestly mediated the relationship between age and 180-day or 1-year mortality in this patient population.

Indexed as

Erythrocyte IndicesGiant Cell ArteritisMachine LearningAgedAged, 80 and overAge FactorsCohort StudiesFemaleHumansKaplan-Meier EstimateMaleMiddle AgedPrognosisAgeGiant cell arteritisMachine learningMediation analysesRed cell distribution width

Identifiers

PMID39923097
PMCPMC11806560

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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.