Rasmussen Laust D, Schmidt Samuel Emil, Knuuti Juhani, Spiro Jon, Rajwani Adil, Lopes Pedro M, Lima Maria Rita, Ferreira António M, Maaniitty Teemu, Saraste Antti, Newby David, Douglas Pamela S, Bøttcher Morten, Baskaran Lohendran, Winther Simon
Department of Cardiology, Gødstrup Hospital, Hospitalsparken 15, DK-7400 Herning, Denmark.
Department of Cardiology, Aalborg University Hospital, 9000 Aalborg, Denmark.
Eur Heart J Cardiovasc Imaging. 2025 Apr 30;26(5):802-813. doi: 10.1093/ehjci/jeaf049.
Models predicting the likelihood of obstructive coronary artery disease (CAD) on invasive coronary angiography exist. However, as stable patients with new-onset chest pain frequently have lower clinical likelihood and preferably undergo index testing by non-invasive tests such as coronary computed tomography angiography (CCTA), clinical likelihood models calibrated against observed obstructive CAD at CCTA are warranted. The aim was to develop CCTA-calibrated risk-factor- and coronary artery calcium score-weighted clinical likelihood models (i.e. RF-CLCCTA and CACS-CLCCTA models, respectively).
Based on age, sex, symptoms, and cardiovascular risk factors, an advanced machine learning algorithm utilized a training cohort (n = 38 269) of symptomatic outpatients with suspected obstructive CAD to develop both a RF-CLCCTA model and a CACS-CLCCTA model to predict observed obstructive CAD on CCTA. The models were validated in several cohorts (n = 28 340) and compared with a currently endorsed basic pre-test probability (Basic PTP) model. For both the training and pooled validation cohorts, observed obstructive CAD at CCTA was defined as >50% diameter stenosis. Observed obstructive CAD at CCTA was present in 6443 (22.7%) patients in the pooled validation cohort. While the Basic PTP underestimated the prevalence of observed obstructive CAD at CCTA, the RF-CLCCTA and CACS-CLCCTA models showed superior calibration. Compared with the Basic PTP model, the RF-CLCCTA and CACS-CLCCTA models showed superior discrimination (area under the receiver operating curves 0.71 [95% confidence interval (CI) 0.70-0.72] vs. 0.74 (95% CI 0.73-0.75) and 0.87 (95% CI 0.86-0.87), P < 0.001 for both comparisons).
CCTA-calibrated clinical likelihood models improve calibration and discrimination of observed obstructive CAD at CCTA.
存在预测侵入性冠状动脉造影时阻塞性冠状动脉疾病（CAD）可能性的模型。然而，由于新发胸痛的稳定患者临床可能性通常较低，且更倾向于通过冠状动脉计算机断层扫描血管造影（CCTA）等非侵入性检查进行初始检查，因此有必要建立根据CCTA时观察到的阻塞性CAD校准的临床可能性模型。目的是开发CCTA校准的风险因素和冠状动脉钙化评分加权临床可能性模型（即分别为RF-CLCCTA和CACS-CLCCTA模型）。
基于年龄、性别、症状和心血管危险因素，一种先进的机器学习算法利用一个有症状的疑似阻塞性CAD门诊患者训练队列（n = 38269）开发了RF-CLCCTA模型和CACS-CLCCTA模型，以预测CCTA上观察到的阻塞性CAD。这些模型在几个队列（n = 28340）中进行了验证，并与当前认可的基本检查前概率（Basic PTP）模型进行了比较。对于训练队列和汇总验证队列，CCTA上观察到的阻塞性CAD定义为直径狭窄>50%。汇总验证队列中有6443例（22.7%）患者在CCTA上观察到阻塞性CAD。虽然Basic PTP低估了CCTA上观察到的阻塞性CAD的患病率，但RF-CLCCTA和CACS-CLCCTA模型显示出更好的校准。与Basic PTP模型相比，RF-CLCCTA和CACS-CLCCTA模型显示出更好的辨别力（受试者操作曲线下面积分别为0.71 [95%置信区间（CI）0.70 - 0.72] 对0.74（95% CI 0.73 - 0.75）和0.87（95% CI 0.86 - 0.87），两种比较的P均<0.001）。
CCTA校准的临床可能性模型改善了CCTA上观察到的阻塞性CAD的校准和辨别力。
