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整合临床和基于冠状动脉病变的功能评估模型用于PCI患者长期风险预测的开发与验证

Development and validation of a model integrating clinical and coronary lesion-based functional assessment for long-term risk prediction in PCI patients.

作者信息

Wu Shao-Yu, Zhang Rui, Yuan Sheng, Cai Zhong-Xing, Guan Chang-Dong, Zou Tong-Qiang, Xie Li-Hua, Dou Ke-Fei

机构信息

State Key Laboratory of Cardiovascular Disease, Beijing, China.

Cardiometabolic Medicine Center, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

出版信息

J Geriatr Cardiol. 2024 Jan 28;21(1):44-63. doi: 10.26599/1671-5411.2024.01.007.

Abstract

OBJECTIVES

To establish a scoring system combining the ACEF score and the quantitative blood flow ratio (QFR) to improve the long-term risk prediction of patients undergoing percutaneous coronary intervention (PCI).

METHODS

In this population-based cohort study, a total of 46 features, including patient clinical and coronary lesion characteristics, were assessed for analysis through machine learning models. The ACEF-QFR scoring system was developed using 1263 consecutive cases of CAD patients after PCI in PANDA III trial database. The newly developed score was then validated on the other remaining 542 patients in the cohort.

RESULTS

In both the Random Forest Model and the DeepSurv Model, age, renal function (creatinine), cardiac function (LVEF) and post-PCI coronary physiological index (QFR) were identified and confirmed to be significant predictive factors for 2-year adverse cardiac events. The ACEF-QFR score was constructed based on the developmental dataset and computed as age (years)/EF (%) + 1 (if creatinine ≥ 2.0 mg/dL) + 1 (if post-PCI QFR ≤ 0.92). The performance of the ACEF-QFR scoring system was preliminarily evaluated in the developmental dataset, and then further explored in the validation dataset. The ACEF-QFR score showed superior discrimination (C-statistic = 0.651; 95% CI: 0.611-0.691, < 0.05 versus post-PCI physiological index and other commonly used risk scores) and excellent calibration (Hosmer-Lemeshow χ = 7.070; = 0.529) for predicting 2-year patient-oriented composite endpoint (POCE). The good prognostic value of the ACEF-QFR score was further validated by multivariable Cox regression and Kaplan-Meier analysis (adjusted HR = 1.89; 95% CI: 1.18-3.04; log-rank 0.01) after stratified the patients into high-risk group and low-risk group.

CONCLUSIONS

An improved scoring system combining clinical and coronary lesion-based functional variables (ACEF-QFR) was developed, and its ability for prognostic prediction in patients with PCI was further validated to be significantly better than the post-PCI physiological index and other commonly used risk scores.

摘要

目的

建立一种结合ACEF评分和定量血流比值(QFR)的评分系统,以改善接受经皮冠状动脉介入治疗(PCI)患者的长期风险预测。

方法

在这项基于人群的队列研究中,通过机器学习模型评估了总共46个特征,包括患者临床和冠状动脉病变特征以进行分析。ACEF-QFR评分系统是利用PANDA III试验数据库中1263例PCI术后CAD患者的连续病例开发的。然后在队列中的其他542例患者中对新开发的评分进行验证。

结果

在随机森林模型和DeepSurv模型中,年龄、肾功能(肌酐)、心功能(左心室射血分数)和PCI术后冠状动脉生理指标(QFR)均被识别并确认为2年不良心脏事件的重要预测因素。ACEF-QFR评分基于开发数据集构建,计算方式为年龄(岁)/射血分数(%) + 1(如果肌酐≥2.0mg/dL) + 1(如果PCI术后QFR≤0.92)。ACEF-QFR评分系统的性能在开发数据集中进行了初步评估,然后在验证数据集中进一步探索。ACEF-QFR评分在预测2年患者导向性复合终点(POCE)方面显示出卓越的区分度(C统计量 = 0.651;95%置信区间:0.611 - 0.691,与PCI术后生理指标和其他常用风险评分相比,P < 0.05)和良好的校准度(Hosmer-Lemeshow χ² = 7.070;P = 0.529)。在将患者分层为高危组和低危组后,通过多变量Cox回归和Kaplan-Meier分析(调整后风险比 = 1.89;95%置信区间:1.18 - 3.04;对数秩检验P = 0.01)进一步验证了ACEF-QFR评分良好的预后价值。

结论

开发了一种结合临床和基于冠状动脉病变的功能变量的改良评分系统(ACEF-QFR),并进一步验证了其对PCI患者的预后预测能力明显优于PCI术后生理指标和其他常用风险评分。

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