Qin Chenlong, Peng Li, Liu Yun, Zhang Xiaoliang, Miao Shumei, Wei Zhiyuan, Feng Wei, Zhang Hongjian, Wan Cheng, Yu Yun, Lu Shan, Huang Ruochen, Zhang Xin
Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, China.
J Med Internet Res. 2024 Dec 30;26:e58686. doi: 10.2196/58686.
Primary hypertension (PH) poses significant risks to children and adolescents. Few prediction models for the risk of PH in children and adolescents currently exist, posing a challenge for doctors in making informed clinical decisions.
This study aimed to investigate the incidence and risk factors of PH in Chinese children and adolescents. It also aimed to establish and validate a nomogram-based model for predicting the next year's PH risk.
A training cohort (n=3938, between January 1, 2008, and December 31, 2020) and a validation cohort (n=1269, between January 1, 2021, and July 1, 2023) were established for model training and validation. An independent cohort of 576 individuals was established for external validation of the model. The result of the least absolute shrinkage and selection operator regression technique was used to select the optimal predictive features, and multivariate logistic regression to construct the nomogram. The performance of the nomogram underwent assessment and validation through the area under the receiver operating characteristic curve, concordance index, calibration curves, decision curve analysis, clinical impact curves, and sensitivity analysis.
The PH risk factors that we have ultimately identified include gender (odds ratio [OR] 3.34, 95% CI 2.88 to 3.86; P<.001), age (OR 1.11, 95% CI 1.08 to 1.14; P<.001), family history of hypertension (OR 42.74, 95% CI 23.07 to 79.19; P<.001), fasting blood glucose (OR 6.07, 95% CI 4.74 to 7.78; P<.001), low-density lipoprotein cholesterol (OR 2.03, 95% CI 1.60 to 2.57; P<.001), and uric acid (OR 1.01, 95% CI 1.01 to 1.01; P<.001), while factor breastfeeding (OR 0.04, 95% CI 0.03 to 0.05; P<.001) has been identified as a protective factor. Subsequently, a nomogram has been constructed incorporating these factors. Areas under the receiver operating characteristic curves of the nomogram were 0.892 in the training cohort, 0.808 in the validation cohort, and 0.790 in the external validation cohort. Concordance indexes of the nomogram were 0.892 in the training cohort, 0.808 in the validation cohort, and 0.790 in the external validation cohort. The nomogram has been proven to have good clinical benefits and stability in calibration curves, decision curve analysis, clinical impact curves, and sensitivity analysis. Finally, we observed noteworthy differences in uric acid levels and family history of hypertension among various subgroups, demonstrating a high correlation with PH. Moreover, the web-based calculator of the nomogram was built online.
We have developed and validated a stable and reliable nomogram that can accurately predict PH risk within the next year among children and adolescents in primary care and offer effective and cost-efficient support for clinical decisions for the risk prediction of PH.
原发性高血压(PH)对儿童和青少年构成重大风险。目前针对儿童和青少年PH风险的预测模型很少,这给医生做出明智的临床决策带来了挑战。
本研究旨在调查中国儿童和青少年PH的发病率及危险因素。同时旨在建立并验证基于列线图的模型,以预测次年的PH风险。
建立一个训练队列(n = 3938,2008年1月1日至2020年12月31日)和一个验证队列(n = 1269,2021年1月1日至2023年7月1日)用于模型训练和验证。建立一个由576名个体组成的独立队列用于模型的外部验证。采用最小绝对收缩和选择算子回归技术的结果来选择最佳预测特征,并通过多变量逻辑回归构建列线图。通过受试者操作特征曲线下面积、一致性指数、校准曲线、决策曲线分析、临床影响曲线和敏感性分析对列线图的性能进行评估和验证。
我们最终确定的PH危险因素包括性别(比值比[OR] 3.34,95%置信区间2.88至3.86;P <.001)、年龄(OR 1.11,95%置信区间1.08至1.14;P <.001)、高血压家族史(OR 42.74,95%置信区间23.07至79.19;P <.001)、空腹血糖(OR 6.07,95%置信区间4.74至7.78;P <.001)、低密度脂蛋白胆固醇(OR 2.03,95%置信区间1.60至2.57;P <.001)和尿酸(OR 1.01,95%置信区间1.01至1.01;P <.001),而母乳喂养因素(OR 0.04,95%置信区间0.03至0.05;P <.001)被确定为保护因素。随后,纳入这些因素构建了列线图。列线图在训练队列中的受试者操作特征曲线下面积为0.892,在验证队列中为0.808,在外部验证队列中为0.790。列线图在训练队列中的一致性指数为0.892,在验证队列中为0.808,在外部验证队列中为0.790。列线图在校准曲线、决策曲线分析、临床影响曲线和敏感性分析中均被证明具有良好的临床效益和稳定性。最后,我们观察到不同亚组之间尿酸水平和高血压家族史存在显著差异,表明与PH高度相关。此外,还在线构建了列线图的网络计算器。
我们开发并验证了一个稳定可靠的列线图,它可以准确预测基层医疗中儿童和青少年次年的PH风险,并为PH风险预测的临床决策提供有效且经济高效的支持。