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支架内梯度对 PCI 后结局的影响:来自 HAWKEYE 子研究的结果。

Impact of trans-stent gradient on outcome after PCI: results from a HAWKEYE substudy.

机构信息

Cardiovascular Institute, Azienda Ospedaliero-Universitaria di Ferrara, S. Anna, Via Aldo Moro 8, 44124, Cona, FE, Italy.

Central Arkansas VA Health System, Little Rock, AR, USA.

出版信息

Int J Cardiovasc Imaging. 2022 Dec;38(12):2819-2827. doi: 10.1007/s10554-022-02708-7. Epub 2022 Aug 22.

Abstract

To test whether quantitative flow ratio (QFR)-based trans-stent gradient (TSG) is associated with adverse clinical events at follow-up. A post-hoc analysis of the multi-center HAWKEYE study was performed. Vessels post-PCI were divided into four groups (G) as follows: G1: QFR ≥ 0.90 TSG = 0 (n = 412, 54.8%); G2: QFR ≥ 0.90, TSG > 0 (n = 216, 28.7%); G3: QFR < 0.90, TSG = 0 (n = 37, 4.9%); G4: QFR < 0.90, TSG > 0 (n = 86, 11.4%). Cox proportional hazards regression model was used to analyze the effect of baseline and prognostic variables. The final reduced model was obtained by backward stepwise variable selection. Receiver operating characteristic (ROC) was plotted and area under the curve (AUC) was calculated and reported. Overall, 449 (59.8%) vessels had a TSG = 0 whereas (40.2%) had TSG > 0. Ten (2.2%) vessel-oriented composite endpoint (VOCE) occurred in vessels with TSG = 0, compared with 43 (14%) in vessels with TSG > 0 (p < 0.01). ROC analysis showed an AUC of 0.74 (95% CI: 0.67 to 0.80; p < 0.001). TSG > 0 was an independent predictor of the VOCE (HR 2.95 [95% CI 1.77-4.91]). The combination of higher TSG and lower final QFR (G4) showed the worst long-term outcome while low TSG and high QFR showed the best outcome (G1) while either high TSG or low QFR (G2, G3) showed intermediate and comparable outcomes. Higher trans-stent gradient was an independent predictor of adverse events and identified a subgroup of patients at higher risk for poor outcomes even when vessel QFR was optimal (> 0.90).

摘要

为了检验基于定量血流比(QFR)的跨支架梯度(TSG)是否与随访期间的不良临床事件有关。对多中心 HAWKEYE 研究进行了事后分析。PCI 后的血管分为四组(G):G1:QFR≥0.90,TSG=0(n=412,54.8%);G2:QFR≥0.90,TSG>0(n=216,28.7%);G3:QFR<0.90,TSG=0(n=37,4.9%);G4:QFR<0.90,TSG>0(n=86,11.4%)。采用 Cox 比例风险回归模型分析基线和预后变量的影响。通过向后逐步变量选择获得最终简化模型。绘制了接收者操作特征(ROC)曲线,并计算和报告曲线下面积(AUC)。总体而言,449 个(59.8%)血管的 TSG=0,而(40.2%)血管的 TSG>0。在 TSG=0 的血管中,有 10 个(2.2%)血管导向复合终点(VOCE)发生,而在 TSG>0 的血管中,有 43 个(14%)发生(p<0.01)。ROC 分析显示 AUC 为 0.74(95%CI:0.67 至 0.80;p<0.001)。TSG>0 是 VOCE 的独立预测因子(HR 2.95 [95%CI 1.77-4.91])。较高的 TSG 和较低的最终 QFR(G4)组合显示出最差的长期结果,而较低的 TSG 和较高的 QFR 显示出最佳的结果(G1),而较高或较低的 TSG 和 QFR(G2,G3)显示出中等且相当的结果。较高的跨支架梯度是不良事件的独立预测因子,并确定了即使血管 QFR 最佳(>0.90),预后不良风险较高的患者亚组。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a337/9708807/2655c06fcbdc/10554_2022_2708_Fig1_HTML.jpg

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