BMC nephrology · 2025
Development of cardiovascular and all-cause mortality risk prediction models for maintenance hemodialysis patients based on metabolomics.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
You Lian-Lian, Dong Cui, Wang Zhi-Hong, Zhang Shuang, Zhang Yu, Kuai Ting-Ting, Xiao Jia, Liu Shu-Xin, Zeng Qing-Cheng
Аннотация
INTRODUCTION: The outcome of maintenance hemodialysis (MHD) remains poor, with cardiovascular death accounting for more than half of all-cause death cases. In this study, cardiovascular mortality and all-cause mortality prediction models were developed to investigate the predictive role of metabolites in MHD patients.
METHODS: Clinical and metabolomics data of 135 hemodialysis patients from a single center were collected with a 6-year follow-up. Univariate Cox regression and random forest were respectively applied to preliminarily screen clinical and metabolomics characteristics, followed by multivariate Cox regression for identifying features predicting cardiovascular or all-cause mortality. Multivariate Cox proportional regression risk models were constructed using clinical, metabolomics, and combined features. Subgroup survival differences were compared via risk score stratification.
RESULTS: The combined model showed significant superiority in predicting cardiovascular mortality (3-year AUC = 0.901, 5-year AUC = 0.876), surpassing the clinical-only model (0.868/0.826) and metabolomics-only model (0.659/0.641). For all-cause mortality, the combined model demonstrated modest improvement (0.859/0.834) but still outperformed the metabolomics model (0.534/0.653). Thirty 5-fold cross-validations confirmed stable performance. High-risk groups had significantly higher cumulative mortality than low-risk groups (p < 0.0001).
CONCLUSION: The metabolomics-alone model showed limited predictive performance, but its synergistic integration with clinical indicators further improved the predictive performance of mortality risk models, particularly for cardiovascular mortality.
METHODS: Clinical and metabolomics data of 135 hemodialysis patients from a single center were collected with a 6-year follow-up. Univariate Cox regression and random forest were respectively applied to preliminarily screen clinical and metabolomics characteristics, followed by multivariate Cox regression for identifying features predicting cardiovascular or all-cause mortality. Multivariate Cox proportional regression risk models were constructed using clinical, metabolomics, and combined features. Subgroup survival differences were compared via risk score stratification.
RESULTS: The combined model showed significant superiority in predicting cardiovascular mortality (3-year AUC = 0.901, 5-year AUC = 0.876), surpassing the clinical-only model (0.868/0.826) and metabolomics-only model (0.659/0.641). For all-cause mortality, the combined model demonstrated modest improvement (0.859/0.834) but still outperformed the metabolomics model (0.534/0.653). Thirty 5-fold cross-validations confirmed stable performance. High-risk groups had significantly higher cumulative mortality than low-risk groups (p < 0.0001).
CONCLUSION: The metabolomics-alone model showed limited predictive performance, but its synergistic integration with clinical indicators further improved the predictive performance of mortality risk models, particularly for cardiovascular mortality.