A time-dependent predictive model for cardiocerebral vascular events in chronic hemodialysis patients: insights from a prospective study.

IF 3.1 3区医学 Q1 MEDICINE, GENERAL & INTERNAL

Frontiers in Medicine Pub Date : 2025-06-04 eCollection Date: 2025-01-01 DOI:10.3389/fmed.2025.1481866

Haowen Zhong, Mengbi Zhang, Yingye Xie, Yuqin Qin, Na Xie, Yuqiu Ye, Heng Li, Hongquan Peng, Xun Liu, Xiaoyan Su, Shaohong Li

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引用次数: 0

Abstract

Context: The conventional risk factors for cardiocerebral vascular events (CVCs) in non-Hemodialysis (HD) patients cannot be directly applied to HD patients due to the unique characteristics of this population. More accurate information on the risk of progression to CVCs is needed for clinical decisions.

Objective: To develop and validate time-dependent predictive models for the progression of CVCs in HD patients.

Design setting and participants: Development and validation of time-dependent predictive models using demographic, clinical, and laboratory data from 3 dialysis centers between 2017 and 2021. These models were developed using time-dependent Cox proportional hazards regression and assessed for discrimination using the concordance index, goodness of fit using the Akaike information criterion and net reclassification improvement.

Main outcome measures: CVCs included acute heart failure, acute hematencephalon, cardiac or brain-derived death, acute myocardial infarction, acute cerebral infarction, ischemic cardiomyopathy, unstable angina pectoris, and stable angina pectoris.

Results: The development and validation cohorts included 233 and 215 patients, respectively. The most accurate model included age, sex, hemoglobin, serum albumin, serum phosphate, white blood cell count, blood flow rate and ultrafiltration volume during HD (C index, 0.704; 95% CI, 0.639-0.768 in the development cohort and 0.775; 95% CI, 0.706-0.843 in the validation cohort). In the validation cohort, this model was more accurate than a model containing variables whose p value in the Cox proportional hazards regression was less than 0.05 (NRI: 0.351, 95% CI: -0.115-0.565).

Conclusion: A time-dependent model using routinely obtained laboratory tests can accurately predict progression to CVCs in HD patients.

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慢性血液透析患者心脑血管事件的时间依赖预测模型：来自前瞻性研究的见解。

背景：非血液透析（HD）患者心脑血管事件（CVCs）的传统危险因素由于该人群的独特特点，不能直接应用于HD患者。临床决策需要更准确的进展为心血管疾病风险的信息。目的：建立并验证HD患者cvc进展的时间依赖预测模型。设计环境和参与者：利用2017年至2021年间3个透析中心的人口统计、临床和实验室数据，开发和验证时间依赖的预测模型。这些模型采用时间相关的Cox比例风险回归建立，并采用一致性指数、赤池信息标准和净重分类改进来评估区分度。主要结局指标：CVCs包括急性心力衰竭、急性脑出血、心源性或脑源性死亡、急性心肌梗死、急性脑梗死、缺血性心肌病、不稳定型心绞痛和稳定型心绞痛。结果：开发和验证队列分别包括233名和215名患者。最准确的模型包括年龄、性别、血红蛋白、血清白蛋白、血清磷酸盐、白细胞计数、血流速率和HD期间的超滤体积(C指数为0.704；发展组的95% CI为0.639-0.768,0.775；验证队列中95% CI为0.706-0.843)。在验证队列中，该模型比Cox比例风险回归中p值小于0.05的变量模型更准确（NRI: 0.351, 95% CI: -0.115 ~ 0.565）。结论：使用常规实验室检查的时间依赖模型可以准确预测HD患者cvc的进展。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Medicine Medicine-General Medicine

CiteScore

5.10

自引率

5.10%

发文量

3710

审稿时长

12 weeks

期刊介绍： Frontiers in Medicine publishes rigorously peer-reviewed research linking basic research to clinical practice and patient care, as well as translating scientific advances into new therapies and diagnostic tools. Led by an outstanding Editorial Board of international experts, this multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide. In addition to papers that provide a link between basic research and clinical practice, a particular emphasis is given to studies that are directly relevant to patient care. In this spirit, the journal publishes the latest research results and medical knowledge that facilitate the translation of scientific advances into new therapies or diagnostic tools. The full listing of the Specialty Sections represented by Frontiers in Medicine is as listed below. As well as the established medical disciplines, Frontiers in Medicine is launching new sections that together will facilitate - the use of patient-reported outcomes under real world conditions - the exploitation of big data and the use of novel information and communication tools in the assessment of new medicines - the scientific bases for guidelines and decisions from regulatory authorities - access to medicinal products and medical devices worldwide - addressing the grand health challenges around the world