He Xingqiang, Tsung-Ying Tsai, Revaiah Pruthvi Chennigahoshalli, Wykrzykowska Joanna J, Rosseel Liesbeth, Sharif Faisal, Muramatsu Takashi, Reiber Johan Hc, Garg Scot, Miyashita Kotaro, Tobe Akihiro, Tao Ling, Onuma Yoshinobu, Serruys Patrick W
Department of Cardiology, University of Galway, University Road, Galway, H91 TK33, Ireland.
Department of Cardiology, Xijing Hospital, Xi'an, China.
Int J Cardiovasc Imaging. 2024 Dec;40(12):2469-2479. doi: 10.1007/s10554-024-03253-1. Epub 2024 Oct 12.
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.
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.
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.
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.
Registered on clinicaltrial.gov (NCT04923191 and NCT05117866).
越来越多的证据表明，经皮冠状动脉介入治疗（PCI）后较高的定量血流比值（QFR）与改善的临床预后相关，然而，目前尚无模型可预测血管造影成功的PCI术后QFR欠佳（<0.91）的情况。本研究旨在建立该领域的预测列线图。
本研究纳入了来自PIONEER IV试验中421例连续患者的450条血管，这些血管以1:1的比例随机分配至训练集（N = 225）和内部验证集（N = 225），并在来自ASET日本试验的95例连续患者的97条血管中进行外部验证。采用LASSO回归进行最佳特征选择，随后利用多因素逻辑回归构建列线图。通过受试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列线图的性能进行评估和验证。
构建的列线图纳入了一个新指标，即定量血流比值衍生的回撤压力梯度（QFR-PPG），以及四个传统参数：左前降支病变、术前QFR、参考血管直径和直径狭窄百分比。列线图在训练集、内部验证集和外部验证集中的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值与预测值之间具有很强的一致性。
使用包含PCI术前可用的五个变量的列线图，可以有效预测血管造影成功的PCI术后患者QFR欠佳的风险，其性能和临床预测价值证实了其在帮助临床医生进行决策和规划血运重建方面的实用性。
在clinicaltrial.gov上注册（NCT04923191和NCT05117866）。
