Chen Jun-Yu, Zhu Ming-Zhen, Xiong Tao, Wang Zi-Yao, Chang Qing
Department of Cardiovascular Surgery, the Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China.
Department of Cardiovascular Surgery, Qilu Hospital of Shandong University, Jinan, China.
J Thorac Dis. 2022 Oct;14(10):4044-4054. doi: 10.21037/jtd-22-1157.
Calcific aortic valve stenosis (CAVS) is a common valvular heart disease, but there are limited reports on the construction of prediction models for CAVS. This study aimed to investigate the risk factors for CAVS and construct a predictive model for CAVS based on its common clinical features.
Patients with CAVS who underwent surgical treatment in our hospital from 2016 to 2020 and those who underwent physical examination during the same period were retrospectively studied and placed in the CAVS group and normal group based on the area of aortic valve orifice less than or more than 3 cm. A total of 548 patients were included in this study, including 106 CAVS patients and 442 normal patients. Subjects were randomly divided into training and validation sets at a 7:3 ratio. The features were dimensionally reduced using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm in the training set, and the optimal clinical features were selected. The independent predictors of patients with CAVS were determined by univariate and multivariate logistic regression, and nomogram was constructed. The calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to evaluate the model in both the training set and the validation set.
In this study, 11 independent predictors were distinguished by multivariate logistic regression analysis: history of hypertension, history of carotid atherosclerosis, age, diastolic blood pressure, C-reactive protein, direct bilirubin, alkaline phosphatase, low-density lipoprotein (LDL), lipoprotein(a) [Lp(a)], uric acid, and cystatin C. A nomogram was constructed using the above indicators. The model was well-calibrated and showed good discrimination and accuracy [the area under the curve (AUC) =0.981] in the training set, with a sensitivity of 91.89% and a specificity of 95.48%. More importantly, the nomogram displayed a good performance in the validation set (AUC =0.955, 95% CI: 0.925-0.985), with a sensitivity of 93.75% and a specificity of 84.09%. Additionally, DCA revealed that the nomogram had high clinical practicability.
This study successfully established a risk prediction model for CAVS based on 11 conveniently accessible clinical indicators, which might easily be used for individualized risk assessment of CAVS.
钙化性主动脉瓣狭窄（CAVS）是一种常见的心脏瓣膜疾病，但关于CAVS预测模型构建的报道有限。本研究旨在探讨CAVS的危险因素，并基于其常见临床特征构建CAVS预测模型。
回顾性研究2016年至2020年在我院接受手术治疗的CAVS患者以及同期接受体检的患者，根据主动脉瓣口面积小于或大于3cm将其分为CAVS组和正常组。本研究共纳入548例患者，其中CAVS患者106例，正常患者442例。将受试者按7:3的比例随机分为训练集和验证集。在训练集中使用最小绝对收缩和选择算子（LASSO）算法对特征进行降维，选择最佳临床特征。通过单因素和多因素逻辑回归确定CAVS患者的独立预测因素，并构建列线图。使用校准曲线、受试者工作特征（ROC）曲线和决策曲线分析（DCA）在训练集和验证集中评估模型。
本研究通过多因素逻辑回归分析鉴别出11个独立预测因素：高血压病史、颈动脉粥样硬化病史、年龄、舒张压、C反应蛋白、直接胆红素、碱性磷酸酶、低密度脂蛋白（LDL）、脂蛋白（a）[Lp（a）]、尿酸和胱抑素C。使用上述指标构建列线图。该模型在训练集中校准良好，具有良好的区分度和准确性[曲线下面积（AUC）=0.981]，灵敏度为91.89%，特异度为95.48%。更重要的是，列线图在验证集中表现良好（AUC =0.955，95%CI：0.925 - 0.985），灵敏度为93.75%，特异度为84.09%。此外，DCA显示列线图具有较高的临床实用性。
本研究成功基于11个易于获取的临床指标建立了CAVS风险预测模型，该模型可能易于用于CAVS的个体化风险评估。
