Department of Cardiology, the First Hospital of China Medical University, Shenyang, China.
Department of Medical Record Management Center, the First Hospital of China Medical University, Shenyang, China.
J Hypertens. 2022 Feb 1;40(2):264-273. doi: 10.1097/HJH.0000000000003003.
We aimed to establish and validate a user-friendly and clinically practical nomogram for estimating the probability of echocardiographic left ventricular hypertrophy (echo-LVH) indexed to BSA among hypertensive patients from northern China.
A total of 4954 hypertensive patients were recruited from a population-based cohort study from January 2012 to August 2013. The dataset was randomly split into two sets: training (n = 3303) and validation (n = 1651). Three nomograms were initially constructed. That is the Cornell product nomogram, the non-ECG nomogram, and the integrated nomogram which integrated non-ECG risk factors and Cornell-voltage duration product. The least absolute shrinkage and selection operator strategies were employed to screen for non-ECG features. The performance of the nomograms was evaluated using discrimination, calibration, and decision curve analysis (DCA). The net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were also calculated.
The AUCs, NRIs, IDIs, and DCA curves of the nomograms demonstrated that the integrated nomogram performed best among all three nomograms. The integrated nomogram incorporated age, sex, educational level, hypertension duration, SBP, DBP, eGFR, sleep duration, tea consumption, and the Cornell-voltage duration product. The AUC was 0.758 and had a good calibration (Hosmer-Lemeshow test, P = 0.73). Internal validation showed an acceptable AUC of 0.735 and good calibration was preserved (Hosmer-Lemeshow test, P = 0.19). The integrated nomogram was clinically beneficial across a range of thresholds of 10-50%.
The integrated nomogram is a convenient and reliable tool that enables early identification of hypertensive patients at high odds of LVH and can assist clinicians in their decision-making.
我们旨在建立并验证一个适用于中国北方高血压患者的、基于体表面积（BSA）的超声心动图左心室肥厚（echo-LVH）概率预测的简便实用的列线图。
我们从 2012 年 1 月至 2013 年 8 月的一项基于人群的队列研究中招募了 4954 名高血压患者。数据集被随机分为两组：训练集（n=3303）和验证集（n=1651）。我们最初构建了三个列线图：康奈尔乘积列线图、非心电图列线图和综合列线图，综合列线图整合了非心电图危险因素和康奈尔-电压持续时间乘积。我们采用最小绝对收缩和选择算子策略筛选非心电图特征。通过判别、校准和决策曲线分析（DCA）评估列线图的性能。还计算了净重新分类改善（NRI）和综合判别改善（IDI）。
列线图的 AUC、NRI、IDI 和 DCA 曲线表明，在所有三个列线图中，综合列线图表现最佳。综合列线图纳入了年龄、性别、教育程度、高血压病程、SBP、DBP、eGFR、睡眠时间、饮茶和康奈尔-电压持续时间乘积。AUC 为 0.758，校准良好（Hosmer-Lemeshow 检验，P=0.73）。内部验证显示 AUC 为 0.735，具有良好的校准（Hosmer-Lemeshow 检验，P=0.19）。综合列线图在 10-50%的多个截断值范围内具有临床获益。
综合列线图是一种方便可靠的工具，可用于早期识别 LVH 风险较高的高血压患者，并帮助临床医生做出决策。
