Frequency of cardioversions as an additional risk factor for stroke in atrial fibrillation - the FinCV-4 study.

IF 3.1 3区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Samuli Jaakkola, Tuomas O Kiviniemi, Jussi Jaakkola, Jussi-Pekka Pouru, Ilpo Nuotio, Tuija Vasankari, Juha E K Hartikainen, K E Juhani Airaksinen
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引用次数: 0

Abstract

Background: Patients with atrial fibrillation (AF) are selected for oral anticoagulation based on individual patient characteristics. There is little information on how clinical AF burden associates with the risk of ischaemic stroke or systemic embolism (SSE). The aim of this study was to explore the association of the frequency of cardioversions (CV) as a measure of clinical AF burden on the long-term SSE risk, with a focus on patients at intermediate stroke risk based on CHA2DS2-VASc score. For these patients, additional SSE risk stratification by assessing CV frequency may aid in the decision on whether to initiate oral anticoagulation.

Methods: This retrospective analysis of FinCV Study from years 2003-2010 included 2074 patients who were not using any oral anticoagulation (long term or temporary) after CVs and undergoing a total of 6534 CVs for AF from emergency departments of three hospitals. Two study groups were formed: high CV frequency (mean interval between CVs ≤12 months and low frequency (>12 months).

Results: A total of 107 SSEs occurred during a mean follow-up of 5.4 years. The event rates per 100 patient-years were 1.82 and 0.67 in high versus low CV frequency groups, respectively. After adjustment for CHA2DS2-VASc score, CV frequency independently predicted SSE (HR, 2.87 [95% CI, 1.47 to 5.64]; p = .002) at 3 years. Competing risk analysis also identified CV frequency (sHR, 2.70 [95% CI, 1.38-5.31]; p = .004) as an independent predictor for SSE. In patients with CHA2DS2-VASc score 1 and low CV frequency, the SSE risk was only 0.08 per 100 patient-years.

Conclusions: Frequency of CVs for symptomatic AF episodes provides additional information on stroke risk in AF patients with CHA2DS2-VASc score 1.Key messagesThis retrospective study offers a unique opportunity to observe the natural course of AF patients with infrequent episodes of clinical arrhythmia when they were not using OAC (before introduction of CHA2DS2-VASc score).Stroke or systemic embolism rate was very low (0.08 per 100 patient-years) in patients with one CHA2DS2-VASc point who visited the emergency room for cardioversion less than once a year.Frequency of cardioversions can be used for additional risk stratification in patients at intermediate risk of stroke based on CHA2DS2-VASc score.

心房颤动患者中风的额外风险因素--FinCV-4 研究。
背景:根据患者的个体特征选择心房颤动(房颤)患者进行口服抗凝治疗。关于临床房颤负担与缺血性中风或全身性栓塞(SSE)风险之间的关系,目前几乎没有相关信息。本研究旨在探讨作为临床房颤负担测量指标的心脏复律(CV)频率与长期 SSE 风险之间的关系,重点是根据 CHA2DS2-VASc 评分确定的中度卒中风险患者。对于这些患者,通过评估 CV 频率进行额外的 SSE 风险分层可能有助于决定是否开始口服抗凝药:这项对 FinCV 研究(2003-2010 年)的回顾性分析纳入了 2074 名患者,这些患者在接受 CV 治疗后未使用任何口服抗凝药物(长期或临时),他们因房颤在三家医院的急诊科共接受了 6534 次 CV 治疗。研究分为两组:高频率心房颤动(两次心房颤动之间的平均间隔≤12个月)和低频率心房颤动(>12个月):结果:在平均 5.4 年的随访期间,共发生了 107 例 SSE。高CV频率组和低CV频率组每100患者年的事件发生率分别为1.82和0.67。对 CHA2DS2-VASc 评分进行调整后,CV 频率可独立预测 3 年后的 SSE(HR,2.87 [95% CI,1.47 至 5.64];p = .002)。竞争风险分析也确定 CV 频率(sHR,2.70 [95% CI,1.38-5.31];p = .004)是 SSE 的独立预测因子。在CHA2DS2-VASc评分为1分且CV频率较低的患者中,SSE风险仅为每百患者年0.08:这项回顾性研究为观察临床心律失常发作不频繁的房颤患者在未使用 OAC 时(在引入 CHA2DS2-VASc 评分之前)的自然病程提供了一个独特的机会。根据CHA2DS2-VASc评分,中风风险患者的中风或全身性栓塞发生率非常低(每100例患者年0.08例)。
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来源期刊
Science and Technology of Welding and Joining
Science and Technology of Welding and Joining 工程技术-材料科学:综合
CiteScore
6.10
自引率
12.10%
发文量
79
审稿时长
1.7 months
期刊介绍: Science and Technology of Welding and Joining is an international peer-reviewed journal covering both the basic science and applied technology of welding and joining. Its comprehensive scope encompasses all welding and joining techniques (brazing, soldering, mechanical joining, etc.) and aspects such as characterisation of heat sources, mathematical modelling of transport phenomena, weld pool solidification, phase transformations in weldments, microstructure-property relationships, welding processes, weld sensing, control and automation, neural network applications, and joining of advanced materials, including plastics and composites.
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