Shen Xuechun, He Wei, Sun Jinyu, Zhang Zuhong, Li Qiushuang, Zhang Haiyan, Long Mingzhi
Department of Cardiology, The Second Affiliated Hospital of Nanjing Medical University, 210011 Nanjing, Jiangsu, China.
Department of Geriatrics, The Second Affiliated Hospital of Nanjing Medical University, 210011 Nanjing, Jiangsu, China.
Rev Cardiovasc Med. 2023 Jan 31;24(2):35. doi: 10.31083/j.rcm2402035. eCollection 2023 Feb.
Early identification of individuals at a high risk of cardiovascular disease (CVD) is crucial. This study aimed to construct a nomogram for CVD risk prediction in the general population.
This retrospective study analyzed the data between January 2012 and September 2020 at the Physical Examination Center of the Second Affiliated Hospital of Nanjing Medical University (randomized 7:3 to the training and validation cohorts). The outcome was the occurrence of CVD events, which were defined as sudden cardiac death or any death related to myocardial infarction, acute exacerbation of heart failure, or stroke. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to screen the significant variables related to CVD.
Among the 537 patients, 54 had CVD (10.1%). The median cardiac myosin-binding protein-C (cMyBP-C) level in the CVD group was higher than in the no-CVD group (42.25 pg/mL VS 25.00 pg/mL, = 0.001). After LASSO selection and multivariable analysis, cMyBP-C (Odds ratio [OR] = 1.004, 95% CI [CI, confidence interval]: 1.000-1.008, = 0.035), age (OR = 1.023, 95% CI: 0.999-1.048, = 0.062), diastolic blood pressure (OR = 1.025, 95% CI: 0.995-1.058, = 0.103), cigarettes per day (OR = 1.066, 95% CI: 1.021-1.113, = 0.003), and family history of CVD (OR = 2.219, 95% CI: 1.003-4.893, = 0.047) were associated with future CVD events ( 0.200). The model, including cMyBP-C, age, diastolic blood pressure, cigarettes per day, and family history of CVD, displayed a high predictive ability with an area under the curve (AUC) of 0.816 (95% CI: 0.714-0.918) in the training cohort (specificity and negative predictive value of 0.92 and 0.96) and 0.774 (95% CI: 0.703-0.845) in the validation cohort.
A nomogram based on cMyBP-C, age, diastolic blood pressure, cigarettes per day, and family history of CVD was constructed. The model displayed a high predictive ability.
早期识别心血管疾病（CVD）高危个体至关重要。本研究旨在构建一个用于预测普通人群CVD风险的列线图。
这项回顾性研究分析了南京医科大学第二附属医院体检中心2012年1月至2020年9月的数据（随机按7:3分为训练队列和验证队列）。结局为CVD事件的发生，其定义为心源性猝死或任何与心肌梗死、心力衰竭急性加重或中风相关的死亡。应用最小绝对收缩和选择算子（LASSO）方法及多因素逻辑回归筛选与CVD相关的显著变量。
在537例患者中，54例发生CVD（10.1%）。CVD组中心肌肌球蛋白结合蛋白C（cMyBP-C）水平中位数高于无CVD组（42.25 pg/mL对25.00 pg/mL，P = 0.001）。经过LASSO筛选和多变量分析，cMyBP-C（比值比[OR]=1.004，95%置信区间[CI]：1.000 - 1.008，P = 0.035）、年龄（OR = 1.023，95% CI：0.999 - 1.048，P = 0.062）、舒张压（OR = 1.025，95% CI：0.995 - 1.058，P = 0.103）、每日吸烟量（OR = 1.066，95% CI：1.021 - 1.113，P = 0.003）以及CVD家族史（OR = 2.219，95% CI：1.003 - 4.893，P = 0.047）与未来CVD事件相关（P < 0.200）。包含cMyBP-C、年龄、舒张压、每日吸烟量和CVD家族史的模型在训练队列中显示出较高的预测能力，曲线下面积（AUC）为0.816（95% CI：0.714 - 0.918）（特异性和阴性预测值分别为0.92和0.96），在验证队列中AUC为0.774（95% CI：0.703 - 0.845）。
构建了一个基于cMyBP-C、年龄、舒张压、每日吸烟量和CVD家族史的列线图。该模型显示出较高的预测能力。
