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
Abstract Objective The 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. Methods Clinical data from GCA patients were extracted from...
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BMC
2025-02-01
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| Series: | Arthritis Research & Therapy |
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| Online Access: | https://doi.org/10.1186/s13075-025-03477-z |
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| author | Si Chen Rui Nie Xiaoran Shen Yan Wang Haixia Luan Xiaoli Zeng Yanhua Chen Hui Yuan |
| author_facet | Si Chen Rui Nie Xiaoran Shen Yan Wang Haixia Luan Xiaoli Zeng Yanhua Chen Hui Yuan |
| author_sort | Si Chen |
| collection | DOAJ |
| description | Abstract Objective The 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. Methods Clinical 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. Results A 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. Conclusions Older 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. |
| format | Article |
| id | doaj-art-4eac62636164464b8b6a2635081f8872 |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-02-01 |
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| series | Arthritis Research & Therapy |
| spelling | doaj-art-4eac62636164464b8b6a2635081f88722025-02-09T12:48:46ZengBMCArthritis Research & Therapy1478-63622025-02-0127111510.1186/s13075-025-03477-zAssociations 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 studySi Chen0Rui Nie1Xiaoran Shen2Yan Wang3Haixia Luan4Xiaoli Zeng5Yanhua Chen6Hui Yuan7Department of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical UniversitySchool of Computer and Artificial Intelligence, Zhengzhou UniversityDepartment of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical UniversityAbstract Objective The 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. Methods Clinical 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. Results A 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. Conclusions Older 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.https://doi.org/10.1186/s13075-025-03477-zGiant cell arteritisAgeRed cell distribution widthMediation analysesMachine learning |
| spellingShingle | Si Chen Rui Nie Xiaoran Shen Yan Wang Haixia Luan Xiaoli Zeng Yanhua Chen Hui Yuan 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 Arthritis Research & Therapy Giant cell arteritis Age Red cell distribution width Mediation analyses Machine learning |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Giant cell arteritis Age Red cell distribution width Mediation analyses Machine learning |
| url | https://doi.org/10.1186/s13075-025-03477-z |
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