Chen Meixiang, Li Pengfei, Huang Yuekang, Li Shuang, Ruan Zheng, Qin Changyu, Huang Jianyu, Wang Ruixin, Lin Zhongqiu, Liu Peng, Xu Lin
The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China.
General Hospital of the Southern Theatre Command, Chinese People's Liberation Army (PLA), Guangzhou, Guangdong, China.
Front Cardiovasc Med. 2022 Dec 19;9:1013563. doi: 10.3389/fcvm.2022.1013563. eCollection 2022.
Patients with non-ST-segment coronary artery syndrome (NSTE-ACS) have significant heterogeneity in their coronary arteries. A better assessment of significant coronary artery stenosis (SCAS) in low-to-intermediate risk NSTE-ACS patients would help identify who might benefit from invasive coronary angiography (ICA). Our study aimed to develop a multivariable-based model for pretesting SCAS in suspected NSTE-ACS with low-to-intermediate risk.
This prediction nomogram was constructed retrospectively in 469 suspected NSTE-ACS patients with low-to-intermediate risk. Patients were divided into a development group ( = 331, patients admitted to hospital before 1 May 2021) and a temporal validation group ( = 138, patients admitted to hospital since 1 May 2021). The outcome was existing SCAS, including left main artery stenosis ≥50% or any subepicardial coronary artery stenosis ≥70%, all confirmed by invasive coronary angiography. Pretest predictors were selected using Least Absolute Shrinkage and Selection Operator (LASSO) and stepwise logistic regression.
Derivation analyses from the development group ( = 331, admitted before 1 May 2021) generated the 7 strongest predictors out of 25 candidate variables comprising smoker, diabetes, heart rate, cardiac troponin T, N-terminal pro-B-type natriuretic peptide, high-density lipoprotein cholesterol, and left atrial diameter. This nomogram model showed excellent discrimination ability with an area under the receiver operating characteristic curve (AUC) of 0.83 in the development set and 0.79 in the validation dataset. Good calibration was generally displayed, although it slightly overestimated patients' SCAS risk in the validation group. Decision curve analysis demonstrated the clinical benefit of this model, indicating its value in clinical practice. Furthermore, an optimal cut-off of prediction probability was assigned as 0.61 according to the Youden index.
A prediction nomogram consisting of seven readily available clinical parameters was established to pretest the probability of SCAS in suspected NSTE-ACS patients with low-to-intermediate risk, which may serve as a cost-effective risk stratification tool and thus assist in initial decision making.
非ST段抬高型冠状动脉综合征（NSTE-ACS）患者的冠状动脉存在显著异质性。更好地评估低至中度风险NSTE-ACS患者的严重冠状动脉狭窄（SCAS），有助于确定哪些患者可能从侵入性冠状动脉造影（ICA）中获益。我们的研究旨在开发一种基于多变量的模型，用于对疑似低至中度风险NSTE-ACS患者进行SCAS预测试。
该预测列线图是对469例疑似低至中度风险NSTE-ACS患者进行回顾性构建的。患者被分为开发组（n = 331，2021年5月1日前入院的患者）和时间验证组（n = 138，2021年5月1日后入院的患者）。结局指标为存在SCAS，包括左主干动脉狭窄≥50%或任何心外膜下冠状动脉狭窄≥70%，均通过侵入性冠状动脉造影确诊。使用最小绝对收缩和选择算子（LASSO）和逐步逻辑回归选择预测试预测因子。
开发组（n = 331，2021年5月1日前入院）的推导分析从25个候选变量中产生了7个最强预测因子，包括吸烟者、糖尿病、心率、心肌肌钙蛋白T、N末端脑钠肽前体、高密度脂蛋白胆固醇和左心房直径。该列线图模型在开发集中显示出优异的区分能力，受试者操作特征曲线（AUC）下面积为0.83，在验证数据集中为0.79。尽管在验证组中该模型略微高估了患者的SCAS风险，但总体显示出良好的校准。决策曲线分析证明了该模型的临床益处，表明其在临床实践中的价值。此外，根据约登指数，预测概率的最佳截断值设定为0.61。
建立了一个由七个易于获得的临床参数组成的预测列线图，用于对疑似低至中度风险NSTE-ACS患者进行SCAS概率预测试，这可能是一种具有成本效益的风险分层工具，从而有助于初步决策。
