AUTHOR=Liang Jing-Hong , Zhao Yu , Chen Yi-Can , Huang Shan , Zhang Shu-Xin , Jiang Nan , Kakaer Aerziguli , Chen Ya-Jun 

TITLE=Development and Validation of a Nomogram-Based Prognostic Model to Predict High Blood Pressure in Children and Adolescents—Findings From 342,736 Individuals in China

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.884508

DOI=10.3389/fcvm.2022.884508

ISSN=2297-055X

ABSTRACT=Objectives: Predicting the potential risk factors of High blood pressure(HBP) among children and adolescents is still a knowledge gap. Our study aimed to establish and validate a nomogram-based model for identifying youths at risk of developing HBP.
Methods: HBP was defined as systolic blood pressure or diastolic blood pressure above 95th percentile, using age, gender and height-specific cut-points. Penalized regression with Lasso was used to identify the strongest predictors of HBP. Internal validation was conducted by a 5-fold cross-validation and bootstrapping approach. The predictive variables and the advanced nomogram plot were identified by conducting univariate and multivariate logistic regression analyses. A nomogram was constructed by a training group comprised of 239,546(69.89%)participants and subsequently validated by an external group with 103,190(30.11%)participants. 
Results: Of 342,736 children and adolescents, 55,480(16.19%) youths were identified with HBP with mean age 11.51±1.45 years and 183,487 were boys(53.5%). Nine significant relevant predictors were identified including: age, gender, weight status, birthweight, breastfeeding, gestational hypertension, family history of obesity and hypertension and physical activity. Acceptable discrimination[Area under the receiver operating characteristic curve(AUC):0.742(Development group), 0.740(Validation group)] and good calibration(Hosmer and Lemeshow statistics, P ＞0.05) were observed in our models. An available web-based nomogram was built online on https://hbpnomogram.shinyapps.io/Dyn_Nomo_HBP/.
Conclusions: This model composed of age, gender, early life factors, family history of the disease, and lifestyle factors may predict the risk of HBP among youths, which has developed a promising nomogram that may aid in more accurately identifying the HBP among youths in primary care.