Nomogram based on virtual hyperemic pullback pressure gradients for predicting the suboptimal post-PCI QFR outcome after stent implantation.

Xingqiang He, Tsai Tsung-Ying, Pruthvi Chennigahoshalli Revaiah, Joanna J Wykrzykowska, Liesbeth Rosseel, Faisal Sharif, Takashi Muramatsu, Johan Hc Reiber, Scot Garg, Kotaro Miyashita, Akihiro Tobe, Ling Tao, Yoshinobu Onuma, Patrick W Serruys
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Abstract

Background: Growing evidence shows an association between higher post-PCI quantitative flow ratios (QFR) and improved clinical prognosis, however, no models are available to predict suboptimal QFRs (< 0.91) after angiographically successful PCI. This study aims to establish a prediction nomogram for this domain.

Methods: This study included 450 vessels derived from 421 consecutive patients enrolled in the PIONEER IV trial, which were randomly assigned in a 1:1 ratio to a training (N = 225) and internal validation (N = 225) set, with external validation performed in 97 vessels from 95 consecutive patients enrolled in the ASET Japan trial. LASSO regression was used for optimal feature selection, and multivariate logistic regression was subsequently utilized to construct the nomogram. The performance of the nomograms was assessed and validated by area under the receiver operating characteristics curve (AUC), calibration curves, decision curve analysis, and clinical impact curves.

Results: The nomogram was constructed incorporating a novel metric, quantitative flow ratio derived pullback pressure gradient (QFR-PPG), alongside four conventional parameters: left anterior descending artery disease, pre-procedural QFR, reference vessel diameter, and percent diameter stenosis. AUCs of the nomogram were 0.866 (95%CI:0.818-0.914), 0.784 (95% CI:0.722-0.847), and 0.781 (95% CI:0.682-0.879) in the training, internal validation and external validation sets, respectively. Bias-corrected curves revealed a strong consistency between actual observations and prediction.

Conclusion: The risk of a suboptimal post-PCI QFR in patients after angiographically successful PCI can be effectively predicted using a nomogram incorporating five variables available pre-PCI, with its performance and clinical predictive value confirming its utility in helping clinicians with decision-making and planning revascularization.

Trial registration: Registered on clinicaltrial.gov (NCT04923191 and NCT05117866).

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基于虚拟充盈回拉压力梯度的提名图,用于预测支架植入后心血管造影术后 QFR 的次优结果。
背景:越来越多的证据表明,PCI 后较高的定量血流比率(QFR)与临床预后改善之间存在关联,然而,目前尚无模型可用于预测次优 QFR(方法:本研究纳入了 PIONEER IV 试验中 421 名连续入组患者的 450 个血管,这些血管按 1:1 的比例随机分配到训练集(N = 225)和内部验证集(N = 225)中,并在 ASET 日本试验中 95 名连续入组患者的 97 个血管中进行了外部验证。LASSO 回归用于优化特征选择,多变量逻辑回归用于构建提名图。通过接收者操作特征曲线下面积(AUC)、校准曲线、决策曲线分析和临床影响曲线对提名图的性能进行了评估和验证:在构建提名图时,除了四个传统参数(左前降支动脉疾病、术前定量血流比、参考血管直径和直径狭窄百分比)外,还加入了一个新指标--定量血流比衍生的回拉压力梯度(QFR-PPG)。在训练集、内部验证集和外部验证集中,提名图的AUC分别为0.866(95%CI:0.818-0.914)、0.784(95%CI:0.722-0.847)和0.781(95%CI:0.682-0.879)。偏差校正曲线显示,实际观察结果与预测结果之间具有很强的一致性:结论:血管造影成功的PCI患者PCI术后QFR不达标的风险可通过结合PCI术前的五个变量的提名图进行有效预测,其性能和临床预测价值证实了其在帮助临床医生决策和规划血管重建方面的实用性:试验注册:已在 clinicaltrial.gov 上注册(NCT04923191 和 NCT05117866)。
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