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Dynamic prediction of childhood high blood pressure in a population-based birth cohort: a model development study

OBJECTIVES: To develop a dynamic prediction model for high blood pressure at the age of 9–10 years that could be applied at any age between birth and the age of 6 years in community-based child healthcare. DESIGN, SETTING AND PARTICIPANTS: Data were used from 5359 children in a population-based pros...

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Autores principales: Hamoen, Marleen, Vergouwe, Yvonne, Wijga, Alet H, Heymans, Martijn W, Jaddoe, Vincent W V, Twisk, Jos W R, Raat, Hein, de Kroon, Marlou L A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6252684/
https://www.ncbi.nlm.nih.gov/pubmed/30467134
http://dx.doi.org/10.1136/bmjopen-2018-023912
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author Hamoen, Marleen
Vergouwe, Yvonne
Wijga, Alet H
Heymans, Martijn W
Jaddoe, Vincent W V
Twisk, Jos W R
Raat, Hein
de Kroon, Marlou L A
author_facet Hamoen, Marleen
Vergouwe, Yvonne
Wijga, Alet H
Heymans, Martijn W
Jaddoe, Vincent W V
Twisk, Jos W R
Raat, Hein
de Kroon, Marlou L A
author_sort Hamoen, Marleen
collection PubMed
description OBJECTIVES: To develop a dynamic prediction model for high blood pressure at the age of 9–10 years that could be applied at any age between birth and the age of 6 years in community-based child healthcare. DESIGN, SETTING AND PARTICIPANTS: Data were used from 5359 children in a population-based prospective cohort study in Rotterdam, the Netherlands. OUTCOME MEASURE: High blood pressure was defined as systolic and/or diastolic blood pressure ≥95th percentile for gender, age and height. Using multivariable pooled logistic regression, the predictive value of characteristics at birth, and of longitudinal information on the body mass index (BMI) of the child until the age of 6 years, was assessed. Internal validation was performed using bootstrapping. RESULTS: 227 children (4.2%) had high blood pressure at the age of 9–10 years. Final predictors were maternal hypertensive disease during pregnancy, maternal educational level, maternal prepregnancy BMI, child ethnicity, birth weight SD score (SDS) and the most recent BMI SDS. After internal validation, the area under the receiver operating characteristic curve ranged from 0.65 (prediction at age 3 years) to 0.73 (prediction at age 5–6 years). CONCLUSIONS: This prediction model may help to monitor the risk of developing high blood pressure in childhood which may allow for early targeted primordial prevention of cardiovascular disease.
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spelling pubmed-62526842018-12-11 Dynamic prediction of childhood high blood pressure in a population-based birth cohort: a model development study Hamoen, Marleen Vergouwe, Yvonne Wijga, Alet H Heymans, Martijn W Jaddoe, Vincent W V Twisk, Jos W R Raat, Hein de Kroon, Marlou L A BMJ Open Public Health OBJECTIVES: To develop a dynamic prediction model for high blood pressure at the age of 9–10 years that could be applied at any age between birth and the age of 6 years in community-based child healthcare. DESIGN, SETTING AND PARTICIPANTS: Data were used from 5359 children in a population-based prospective cohort study in Rotterdam, the Netherlands. OUTCOME MEASURE: High blood pressure was defined as systolic and/or diastolic blood pressure ≥95th percentile for gender, age and height. Using multivariable pooled logistic regression, the predictive value of characteristics at birth, and of longitudinal information on the body mass index (BMI) of the child until the age of 6 years, was assessed. Internal validation was performed using bootstrapping. RESULTS: 227 children (4.2%) had high blood pressure at the age of 9–10 years. Final predictors were maternal hypertensive disease during pregnancy, maternal educational level, maternal prepregnancy BMI, child ethnicity, birth weight SD score (SDS) and the most recent BMI SDS. After internal validation, the area under the receiver operating characteristic curve ranged from 0.65 (prediction at age 3 years) to 0.73 (prediction at age 5–6 years). CONCLUSIONS: This prediction model may help to monitor the risk of developing high blood pressure in childhood which may allow for early targeted primordial prevention of cardiovascular disease. BMJ Publishing Group 2018-11-21 /pmc/articles/PMC6252684/ /pubmed/30467134 http://dx.doi.org/10.1136/bmjopen-2018-023912 Text en © Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Public Health
Hamoen, Marleen
Vergouwe, Yvonne
Wijga, Alet H
Heymans, Martijn W
Jaddoe, Vincent W V
Twisk, Jos W R
Raat, Hein
de Kroon, Marlou L A
Dynamic prediction of childhood high blood pressure in a population-based birth cohort: a model development study
title Dynamic prediction of childhood high blood pressure in a population-based birth cohort: a model development study
title_full Dynamic prediction of childhood high blood pressure in a population-based birth cohort: a model development study
title_fullStr Dynamic prediction of childhood high blood pressure in a population-based birth cohort: a model development study
title_full_unstemmed Dynamic prediction of childhood high blood pressure in a population-based birth cohort: a model development study
title_short Dynamic prediction of childhood high blood pressure in a population-based birth cohort: a model development study
title_sort dynamic prediction of childhood high blood pressure in a population-based birth cohort: a model development study
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6252684/
https://www.ncbi.nlm.nih.gov/pubmed/30467134
http://dx.doi.org/10.1136/bmjopen-2018-023912
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