Establishment and validation of post-PCI nomogram in elderly patients with acute coronary syndromes.

IF 2.8 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Frontiers in Cardiovascular Medicine Pub Date : 2025-03-07 eCollection Date: 2025-01-01 DOI:10.3389/fcvm.2025.1529476
Xing-Yu Zhu, Zhi-Meng Jiang, Xiao Li, Fei-Fei Su, Jian-Wei Tian
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引用次数: 0

Abstract

Objective: The objective of this study was to create and validate a clinical prediction model for the incidence of major adverse cardiovascular events (MACE) within one year after percutaneous coronary intervention (PCI) in elderly patients diagnosed with acute coronary syndromes (ACS).

Methods: The study will use 70% of the 738 patients for model training and the remaining 30% for model validation. The feature recursive elimination algorithm (RFE) and the least absolute shrinkage selection operator (LASSO) regression technique will be used to identify the best combination of features. We compare the clinical prediction model we constructed with GRACE in terms of discrimination, calibration, recall, and clinical impact.

Results: We used the RFE and LASSO regression technique to select 8 key variables from 44 candidates for our predictive model. The predictive model was found to have a good fit based on the Hosmer-Lemeshow test results (χ 2 = 6.245). Additionally, the Brier score of the clinical prediction model was 0.1502, confirming its accuracy. When comparing our clinical prediction model to the widely used GRACE scoring system, the results showed that our model had slightly better predictive efficacy for the dataset involved in this study. The NRI was 0.6166, NRI + was 0.2262, NRI- was 0.3904, and IDI was 0.1272, with a P value of <0.001. The validation set's AUC was 0.787, indicating the prediction model has high differentiation and discriminative ability.

Conclusion: This model assists in the early identification of the risk of MACE within one year after PCI for ACS in elderly patients.

老年急性冠脉综合征患者pci后心电图的建立与验证。
目的:本研究旨在建立并验证老年急性冠脉综合征(ACS)患者经皮冠状动脉介入治疗(PCI)后1年内主要心血管不良事件(MACE)发生率的临床预测模型。方法:本研究将738例患者中的70%用于模型训练,剩余30%用于模型验证。将使用特征递归消除算法(RFE)和最小绝对收缩选择算子(LASSO)回归技术来识别特征的最佳组合。我们在鉴别、校准、召回率和临床影响方面比较了我们与GRACE构建的临床预测模型。结果:我们使用RFE和LASSO回归技术从44个候选变量中选择8个关键变量用于我们的预测模型。根据Hosmer-Lemeshow检验结果,预测模型拟合良好(χ 2 = 6.245)。此外,临床预测模型的Brier评分为0.1502,证实了其准确性。将我们的临床预测模型与广泛使用的GRACE评分系统进行比较,结果显示我们的模型对本研究涉及的数据集的预测效果略好。NRI为0.6166,NRI +为0.2262,NRI-为0.3904,IDI为0.1272,P值为。结论:该模型有助于早期识别老年ACS患者PCI术后1年内发生MACE的风险。
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来源期刊
Frontiers in Cardiovascular Medicine
Frontiers in Cardiovascular Medicine Medicine-Cardiology and Cardiovascular Medicine
CiteScore
3.80
自引率
11.10%
发文量
3529
审稿时长
14 weeks
期刊介绍: Frontiers? Which frontiers? Where exactly are the frontiers of cardiovascular medicine? And who should be defining these frontiers? At Frontiers in Cardiovascular Medicine we believe it is worth being curious to foresee and explore beyond the current frontiers. In other words, we would like, through the articles published by our community journal Frontiers in Cardiovascular Medicine, to anticipate the future of cardiovascular medicine, and thus better prevent cardiovascular disorders and improve therapeutic options and outcomes of our patients.
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