Abstract
AbstractBackgroundPredicting 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 children and adolescents at risk of developing HBP based on a population-based prospective study.MethodsHypertension 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 hypertension. Internal validation was conducted by 5-fold cross-validation and bootstrapping approach. The predictive variables were identified along with the advanced nomogram plot by conducting univariate and multivariate logistic regression analyses. A nomogram was constructed by training group comprised of 239,546(69.89%)participants and subsequently validated by externally group with 103,190(30.11%)participants.ResultsOf 342,736 children and adolescents, a total of 55,480(16.19%) youths were identified with HBP with mean age 11.51±1.45 year 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, family history of hypertension and physical activity. An 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.ConclusionsThis model composed of age, gender, early life factors, family history of disease, and lifestyle factors may predict the risk of HBP among children and adolescents, which has developed a promising nomogram that may aid in more accurately for identifying the HBP among youths in primary care.Funding SourcesThe work was supported by the National Natural Science Foundation of China (No. 81673193).
Publisher
Cold Spring Harbor Laboratory
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