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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.
Liang, Jing-Hong; Zhao, Yu; Chen, Yi-Can; Huang, Shan; Zhang, Shu-Xin; Jiang, Nan; Kakaer, Aerziguli; Chen, Ya-Jun.
Afiliación
  • Liang JH; Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Zhao Y; Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Chen YC; Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Huang S; Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Zhang SX; Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Jiang N; Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Kakaer A; Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Chen YJ; Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Front Cardiovasc Med ; 9: 884508, 2022.
Article en En | MEDLINE | ID: mdl-35811689
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 the 95th percentile, using age, gender, and height-specific cut-off 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 (69.9%) participants and subsequently validated by an external group with 103,190 (30.1%) participants. Results: Of 342,736 children and adolescents, 55,480 (16.2%) 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, birth weight, 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 HBP among youths in primary care.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Cardiovasc Med Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Cardiovasc Med Año: 2022 Tipo del documento: Article País de afiliación: China