Construction and validation of a clinical prediction model for asymptomatic obstructive coronary stenosis in patients with carotid stenosis - Qin, Cuijie; Li, Chuang; Luo, Yunpeng; Li, Zhen; Cao, Hui (Frontiers in Cardiovascular Medicine, 2023)
Background Coronary artery stenosis occurs frequently in patients with carotid artery stenosis. We developed a clinical predictive model to investigate the clinical risk of asymptomatic obstructive coronary artery stenosis in patients with carotid artery stenosis ≥ 50%. Methods From January 2018 to January 2022, carotid stenosis patients hospitalized at the First Affiliated Hospital of Zhengzhou University's Department of Endovascular Surgery were subjected to a retrospective analysis of their clinical information and imaging results. Excluded criteria were patients with lacking data, symptomatic coronary stenosis, prior coronary artery bypass grafting, and coronary stent implantation. Patients were separated into case and control groups according to whether or not they had obstructive coronary stenosis. Independent predictors were screened using univariate and multivariate logistic regression, and their accuracy was confirmed using least absolute shrinkage and selection operator (LASSO) regression. A Nomogram prediction model was developed using the aforementioned filtered factors. The model's discrimination and specificity were evaluated using the receiver operating characteristic curve (ROC) and Hosmer-Lemeshow goodness-of-fit test. Internal validation employed the Bootstrap procedure. The clinical decision curve analysis (DCA) of the prediction model was developed to assess the clinical applicability of the model. Results The investigation included a total of 227 patients, of whom 132 (58.1%) had coronary artery stenosis. Hypertension, Grade I plaque, HbA1c ≥ 7.0%, MPV ≥ 9.2fl, and Fib ≥ 3.0 g/L were independent predictors, with OR values of (2.506, 0.219, 0.457, 1.876, 2.005), according to multivariate logistic regression. Risk factor screening and validation using lasso regression. The predictors chosen based on the optimal λ value are consistent with the predictors identified by multiple regression. The area under the ROC curve (AUC) of the model based on the above predictors was 0.701 (0.633–0.770), indicating that the model discriminated well. The calibration curve of the model closely matched the actual curve, and P > 0.05 in the Hosmer-Lemeshow goodness-of-fit test indicated the model's accuracy. The results of the DCA curve demonstrate the clinical applicability of the prediction model. Conclusion Hypertension, grade I plaque, HbA1c ≥ 7.0%, MPV ≥ 9.2 fl, and Fib ≥ 3.0 g/L are predictors of asymptomatic coronary stenosis in patients with carotid stenosis ≥50%. The diagnostic model is clinically applicable and useful for identifying patients at high risk.