Copeptin 与维持性血液透析患者的主要心血管不良事件有关。

IF 3.2 3区 医学 Q2 MEDICAL LABORATORY TECHNOLOGY
Zhen Zhang , Lin Zhang , Xinyue Dong , Bo Shen , Fangfang Xiang , Xuesen Cao , Jinbo Yu , Yaqiong Wang , Xiaoqiang Ding , Yuxin Nie
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引用次数: 0

摘要

背景:必须进行血液透析的终末期肾病(ESRD)具有很大的心血管风险,心血管疾病(CVD)是导致死亡的主要原因。copeptin 等生物标志物已成为 CKD 患者心血管压力和预后的潜在指标:本研究旨在评估 copeptin 在预测血液透析患者主要不良心血管事件 (MACE) 方面的预后价值,以及传统的心脏生物标志物:方法:入组接受维持性血液透析的 ESRD 患者。方法:对接受维持性血液透析的 ESRD 患者进行登记,测量其 Copeptin 水平,并对患者的 MACEs(心血管死亡、心肌梗死、中风或心衰相关住院)进行随访。在对相关协变量进行调整后,采用 Cox 比例危险模型评估 copeptin 与结果之间的关系:在中位随访 22.7 个月的 351 名患者中, copeptin 水平升高与 MACE 风险增加显著相关(HR 1.519,95 % CI 1.140 至 2.023;p = 0.00425)。Copeptin 在多种统计检验中均显示出预测能力(Log-rank p = 0.024;Gehan p 结论:Copeptin 水平升高可独立预测心肌梗死风险:copeptin 水平升高可独立预测血液透析患者的不良心血管预后。将 copeptin 与传统的心脏生物标志物相结合,可完善这一高风险人群的风险分层并指导个性化治疗策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Copeptin associates with major adverse cardiovascular events in patients on maintenance hemodialysis

Background

End-stage renal disease (ESRD) necessitating hemodialysis pose substantial cardiovascular risks, with cardiovascular disease (CVD) as a leading cause of mortality. Biomarkers like copeptin have emerged as potential indicators of cardiovascular stress and prognosis in CKD populations.

Objective

This study aimed to assess the prognostic value of copeptin in predicting major adverse cardiovascular events (MACEs) among hemodialysis patients, alongside traditional cardiac biomarkers.

Methods

ESRD patients undergoing maintenance hemodialysis were enrolled. Copeptin levels were measured, and patients were followed for MACEs, defined as cardiovascular deaths, myocardial infarction, stroke, or heart failure-related hospitalizations. Cox proportional-hazards models were used to evaluate the association between copeptin and outcomes, adjusting for relevant covariates.

Results

Among 351 patients followed for a median of 22.7 months, elevated copeptin levels were significantly associated with an increased risk of MACEs (HR 1.519, 95 % CI 1.140 to 2.023; p = 0.00425). Copeptin demonstrated predictive capability across multiple statistical tests (Log-rank p = 0.024; Gehan p < 0.001; Tarone-Ware p < 0.001; Peto-Peto p = 0.027), although significance was attenuated in pairwise comparisons post-adjustment for multiple testing. Combining copeptin with NT-proBNP or hs-cTnT further enhanced risk stratification for MACEs.

Conclusion

Elevated copeptin levels independently predict adverse cardiovascular outcomes in hemodialysis patients. Integrating copeptin with traditional cardiac biomarkers may refine risk stratification and guide personalized therapeutic strategies in this high-risk population.

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来源期刊
Clinica Chimica Acta
Clinica Chimica Acta 医学-医学实验技术
CiteScore
10.10
自引率
2.00%
发文量
1268
审稿时长
23 days
期刊介绍: The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.
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