【摘要】 目的：心血管疾病(cardiovascular disease,CVD)严重威胁全球人口健康。对无症状人群进行动脉硬化评估可有效识别CVD的高危个体。本研究旨在构建预测无症状人群血管斑块发生风险的个体化列线图模型。方法：回顾性纳入2022年1月至2024年6月在湘雅三医院健康管理中心完成CVD筛查的5 655名体检者。采用简单随机抽样法按照8?2的比例将研究对象分为训练集(n=4 524)和验证集(n=1 131)。收集并比较2组的一般资料与临床指标。通过多因素Logistic回归分析确定无症状人群血管斑块的独立关联因素，并构建列线图预测模型。采用受试者操作特征(receiver operating characteristic,ROC)曲线、Hosmer-Lemeshow校准曲线及决策曲线分析(decision curve analysis,DCA)评估模型的预测效能和临床实用性。结果：研究对象的平均年龄为52岁，其中男性3 400名(60.12%)。血管斑块总检出率为49.87%(2 820/5 655)。训练集与验证集的各项临床指标差异均无统计学意义(均P>0.05)。多因素Logistic回归分析结果显示：年龄、收缩压、高密度脂蛋白、低密度脂蛋白、脂蛋白a、男性、吸烟史、高血压病史和糖尿病病史是无症状人群血管斑块的独立关联因素(均P<0.05)。列线图模型预测训练集和验证集血管斑块风险的ROC-曲线下面积(area under the curve,AUC)分别为0.778(95%CI 0.765～0.791,P<0.001)和0.760(95%CI 0.732～0.787,P<0.001)。Hosmer-Lemeshow拟合优度检验显示模型校准度良好(训练集：P=0.628；验证集：P=0.561)。Bootstrap法绘制的校准图显示预测概率与实际概率贴合良好。DCA曲线结果表明：当研究对象的阈值概率在0.02～0.99时，应用该列线图预测血管斑块风险具有临床净获益。结论：基于经济、易获取的体检指标构建的血管斑块列线图预测模型具有良好的预测效能，有助于早期识别和干预无症状心血管疾病高危人群。更多还原

【Abstract】 Objective: Cardiovascular disease(CVD) poses a major threat to global health. Evaluating atherosclerosis in asymptomatic individuals can help identify those at high risk of CVD. This study aims to establish an individualized nomogram prediction model to estimate the risk of vascular plaque formation in asymptomatic individuals.Methods: A total of 5 655 participants who underwent CVD screening at the Health Management Center of The Third Xiangya Hospital, Central South University, between January 2022 and June 2024 we retrospectively enrolled. Using simple random sampling, participants were divided into a training set(n=4 524) and a validation set(n=1 131) in an 8 ? 2 ratio. Demographic and clinical data were collected and compared between groups. Multivariate logistic regression analysis was used to identify independent factors associated with vascular plaques and to construct a nomogram prediction model. The predictive performance and clinical utility of the model were evaluated using receiver operating characteristic(ROC) curves, the Hosmer-Lemeshow goodness-of-fit test, calibration plots, and decision curve analysis(DCA).Results: The mean age of participants was 52 years old. There were 3 400 males(60.12%). The overall detection rate of vascular plaque in the screening population was 49.87%(2 820/5 655). No statistically significant differences were observed in clinical indicators between the training and validation sets(all P>0.05). Multivariate Logistic regression analysis identified age, systolic blood pressure, high-density lipoprotein(HDL), lowdensity lipoprotein(LDL), lipoprotein(a), male sex, smoking history, hypertension history, and diabetes history as independent risk factors for vascular plaque in asymptomatic individuals(all P<0.05). The area under the curve(AUC) of the nomogram model for predicting vascular plaque risk were 0.778(95% CI 0.765 to 0.791, P<0.001) in the training set and 0.760(95% CI 0.732 to 0.787, P<0.001) in the validation set. The HosmerLemeshow goodness-of-fit test indicated good model calibration(training set: P=0.628; validation set: P=0.561). The calibration curve plotted using the Bootstrap method demonstrated good agreement between predicted probabilities and actual probabilities. DCA showed that the nomogram provided a clinical net benefit for predicting vascular plaque risk when the threshold probability ranged from 0.02 to 0.99.Conclusion: The nomogram prediction model for vascular plaque risk, constructed using readily available and cost-effective physical examination indicators, exhibited good predictive performance. This model can assist in the early identification and intervention of asymptomatic individuals at high risk for cardiovascular disease.更多还原