心房颤动射频消融术患者体内非分叶肝素的群体药代动力学和活化凝血时间的多变量分析。

Celine Konecki, François Lesaffre, Sophie Guillou, Catherine Feliu, Florine Dubuisson, Moad Labdaoui, Laurent Faroux, Zoubir Djerada
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引用次数: 0

摘要

导言:心房颤动(房颤)会增加心血管疾病的发病率和死亡率。为了降低血栓形成和出血风险,同时由于非分叶肝素(UFH)效果的高变异性,在房颤射频导管消融术(RFCA)中使用活化凝血时间(ACT)来指导 UFH 剂量。本研究旨在建立一个人群 PK-PD 模型并进行多变量分析,以确定与 UFH 的个体间变异相关的最重要的协变量:分析了668名接受RFCA治疗的患者的电子病历,包括相关的协变量。采用混合效应 PK-PD 模型分析了 UFH 剂量和 ACT 之间的关系以及主要协变量的影响。然后利用多变量分析确定了UFH给药15分钟后ACT(ACT15)的预测因素:结果:具有线性消除作用的二室 PK 模型和具有基线和正交性的直接 Emax PD 模型最能描述观察到的 ACT 值。达比加群、华法林或氟啶酮的预处理对基线ACT有显著影响。维生素 K 拮抗剂或低分子量肝素的预处理可解释 Emax 变异。多变量模型确定基线 ACT、初始 UFH 剂量和既往抗凝剂是 ACT 的主要预测因素15。通过重新取样和外部验证对模型进行的评估显示,ACT15预测准确:本研究首次提出了描述 RFCA 期间 UFH 剂量与 ACT 之间关系的人群 PK-PD 模型,并进行了多变量分析。此外,还根据患者和手术特征开发了 ACT15 和 UFH 剂量的预测计算器,从而加强了 RFCA 期间的个性化抗凝管理。
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Population pharmacokinetics of unfractionated heparin and multivariable analysis of activated clotting time in patients undergoing radiofrequency ablation of atrial fibrillation.

Introduction: Atrial fibrillation (AF) increases cardiovascular morbidity and mortality. To reduce thrombosis and bleeding risks, and due to high variability of unfractionated heparin (UFH) effect, activated clotting time (ACT) is used during radiofrequency catheter ablation (RFCA) of AF to guide UFH dose. This study aimed to develop a population PK-PD model and perform multivariable analysis in order to identify the most significant covariates associated with interindividual variability of UFH.

Methods: Electronic medical records from 668 patients undergoing RFCA were analyzed, including relevant covariates. The relationship between UFH dose and ACT and the impact of the main covariates were characterized using a mixed-effect PK-PD model. Multivariable analysis was then used to identify predictors of ACT 15 minutes after UFH administration (ACT15).

Results: A two-compartment PK model with linear elimination and a direct Emax PD model with a baseline and sigmoidicity best described the observed ACT values. Pretreatment with dabigatran, warfarin, or fluindione significantly influenced baseline ACT. Pretreatment with vitamin K antagonists or low molecular weight heparin explained Emax variability. The multivariable model identified baseline ACT, initial UFH dose, and previous anticoagulant as the main predictors of ACT15. Model evaluation through resampling and external validation showed accurate ACT15 predictions.

Conclusion: This study presents the first population PK-PD model characterizing the relationship between UFH doses and ACT during RFCA, along with multivariable analysis. Additionally, predictive calculators for ACT15 and UFH dose based on patient and procedural characteristics were developed, enhancing personalized anticoagulation management during RFCA.

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