就 "动态动脉僵化指数与心房颤动发病率之间的关系 "发表评论。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-08-06 DOI:10.1002/clc.24333
Mustafa Candemir, Emrullah Kızıltunç
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引用次数: 0

摘要

我们饶有兴趣地阅读了 Boos 等人[1]进行的这项中位持续时间为 4 年的观察性队列研究。在这项研究中,我们发现动态动脉僵化指数(AASI)是房颤发生的一个独立预测因素[1]。首先,我们要祝贺这篇文章的作者唤起了人们的意识,即通过非卧床血压监测(ABPM)获得的参数(如 AASI)对许多重要疾病具有独立的预测价值[2, 3]。众所周知,高血压、心力衰竭和糖尿病等疾病患者的诊断时间可能会影响 AASI,而 AASI 可提供动脉僵化的信息[3, 4]。因此,心房颤动组和非心房颤动组的这些疾病(高血压、心衰和糖尿病)的诊断持续时间是否存在统计学意义上的显著差异?我们知道这些疾病对 AASI 有影响[2]。因此,我们认为将这些疾病作为混杂变量纳入 Cox 回归分析是合适的。作者表示,由于房颤发生率为 9.1%(n = 75),因此他们限制了回归模型中包含的变量数量。然而,在回归分析中,每个变量的事件数可以在 5-9 之间。众所周知,这种分析结果是正确的[5]。因此,回归分析中评估的变量数量可以增加到 8 个。最后,组间 β 受体阻滞剂使用率的差异可能会导致 AASI 导致组间差异具有统计学意义。将研究结果从所用药物的影响中净化出来,也能更好地解释研究结果。尽管有这些意见,但我们同意这项研究将对文献做出巨大贡献。
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Comment on “The Relationship Between Ambulatory Arterial Stiffness Index and Incident Atrial Fibrillation”

We read with great interest this observational cohort study with a median duration of 4 years by Boos et al. [1]. In this study, ambulatory arterial stiffness index (AASI) was found to be an independent predictor of the development of AF [1]. First of all, we would like to congratulate the authors of this article for raising awareness that parameters (such as AASI) obtained from ambulatory blood pressure monitoring (ABPM) have independent predictive value in many important diseases [2, 3]. We thought some points should be clarified so we decided to add some helpful comments on this article.

It is known that the diagnosis duration of patients with diseases like hypertension, heart failure, and diabetes may affect AASI, which provides information about arterial stiffness [3, 4]. Therefore, was there a statistically significant difference between the diagnosis duration of these diseases (hypertension, heart failure, and diabetes) in the AF and non-AF groups?

In addition, the incidence of heart failure and ischemic stroke was higher in the AF group in the study. We know that these diseases have an impact on AASI [2]. Therefore, we think that it would be appropriate to include these diseases as confounding variables in the Cox regression analysis. The authors said that they limited the number of variables included in the regression model because the AF incidence was 9.1% (n = 75). However, in regression analysis, the number of events per variable can be between 5–9. It is known that the results of this analysis are correct [5]. Therefore, the number of variables evaluated in the regression analysis could have been increased to eight. Finally, the difference in β-blocker use rates between groups may have caused AASI to lead to a statistically significant difference between the groups. Purifying the study results from the effects of the drugs used would also enable better interpretation of the results.

Despite these comments, we agree that this study will contribute greatly to the literature.

The authors declare no conflicts of interest.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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