Childhood prediction models for hypertension later in life:A systematic review

BACKGROUND: Hypertension, even during childhood, increases the risk of developing atherosclerosis and cardiovascular disease. Therefore, starting prevention of hypertension early in the life course could be beneficial. Prediction models might be useful for identifying children at increased risk of developing hypertension, which may enable targeted primordial prevention of cardiovascular disease. OBJECTIVE: To provide an overview of childhood prediction models for future hypertension. METHODS: Embase and Medline were systematically searched. Studies were included that were performed in the general population, and that reported on development or validation of a multivariable model for children to predict future high blood pressure, prehypertension or hypertension. Data were extracted using the CHARMS checklist for prediction modelling studies. RESULTS: Out of 12 780 reviewed records, six studies were included in which 18 models were presented. Five studies predicted adulthood hypertension, and one predicted adolescent prehypertension/hypertension. BMI and current blood pressure were most commonly included as predictors in the final models. Considerable heterogeneity existed in timing of prediction (from early childhood to late adolescence) and outcome measurement. Important methodological information was often missing, and in four studies information to apply the model in new individuals was insufficient. Reported area under the ROC curves ranged from 0.51 to 0.74. As none of the models were validated, generalizability could not be confirmed. CONCLUSION: Several childhood prediction models for future hypertension were identified, but their value for practice remains unclear because of suboptimal methods, limited information on performance, or the lack of external validation. Further validation studies are indicated.This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0

Childhood prediction models for hypertension later in life: a systematic review.

Abstract BACKGROUND: Hypertension, even during childhood, increases the risk of developing atherosclerosis and cardiovascular disease. Therefore, starting prevention of hypertension early in the life course could be beneficial. Prediction models might be useful for identifying children at increased risk of developing hypertension, which may enable targeted primordial prevention of cardiovascular disease. OBJECTIVE: To provide an overview of childhood prediction models for future hypertension. METHODS: Embase and Medline were systematically searched. Studies were included that were performed in the general population, and that reported on development or validation of a multivariable model for children to predict future high blood pressure, prehypertension or hypertension. Data were extracted using the CHARMS checklist for prediction modelling studies. RESULTS: Out of 12 780 reviewed records, six studies were included in which 18 models were presented. Five studies predicted adulthood hypertension, and one predicted adolescent prehypertension/hypertension. BMI and current blood pressure were most commonly included as predictors in the final models. Considerable heterogeneity existed in timing of prediction (from early childhood to late adolescence) and outcome measurement. Important methodological information was often missing, and in four studies information to apply the model in new individuals was insufficient. Reported area under the ROC curves ranged from 0.51 to 0.74. As none of the models were validated, generalizability could not be confirmed. CONCLUSION: Several childhood prediction models for future hypertension were identified, but their value for practice remains unclear because of suboptimal methods, limited information on performance, or the lack of external validation. Further validation studies are indicated

Associations of Maternal Educational Level, Proximity to Green Space During Pregnancy, and Gestational Diabetes With Body Mass Index From Infancy to Early Adulthood:A Proof-of-Concept Federated Analysis in 18 Birth Cohorts

International sharing of cohort data for research is important and challenging. We explored the feasibility of multicohort federated analyses by examining associations between 3 pregnancy exposures (maternal education, exposure to green vegetation, and gestational diabetes) and offspring body mass index (BMI) from infancy to age 17 years. We used data from 18 cohorts (n = 206,180 mother-child pairs) from the EU Child Cohort Network and derived BMI at ages 0-1, 2-3, 4-7, 8-13, and 14-17 years. Associations were estimated using linear regression via 1-stage individual participant data meta-analysis using DataSHIELD. Associations between lower maternal education and higher child BMI emerged from age 4 and increased with age (difference in BMI z score comparing low with high education, at age 2-3 years = 0.03 (95% confidence interval (CI): 0.00, 0.05), at 4-7 years = 0.16 (95% CI: 0.14, 0.17), and at 8-13 years = 0.24 (95% CI: 0.22, 0.26)). Gestational diabetes was positively associated with BMI from age 8 years (BMI z score difference = 0.18, 95% CI: 0.12, 0.25) but not at younger ages; however, associations attenuated towards the null when restricted to cohorts that measured gestational diabetes via universal screening. Exposure to green vegetation was weakly associated with higher BMI up to age 1 year but not at older ages. Opportunities of cross-cohort federated analyses are discussed.</p

Dynamic prediction model to identify young children at high risk of future overweight:Development and internal validation in a cohort study

BACKGROUND: Primary prevention of overweight is to be preferred above secondary prevention, which has shown moderate effectiveness.OBJECTIVE: To develop and internally validate a dynamic prediction model to identify young children in the general population, applicable at every age between birth and age 6, at high risk of future overweight (age 8).METHODS: Data were used from the Prevention and Incidence of Asthma and Mite Allergy birth cohort, born in 1996 to 1997, in the Netherlands. Participants for whom data on the outcome overweight at age 8 and at least three body mass index SD scores (BMI SDS) at the age of ≥3 months and ≤6 years were available, were included (N = 2265). The outcome of the prediction model is overweight (yes/no) at age 8 (range 7.4-10.5 years), defined according to the sex- and age-specific BMI cut-offs of the International Obesity Task Force.RESULTS: After backward selection in a Generalized Estimating Equations analysis, the prediction model included the baseline predictors maternal BMI, paternal BMI, paternal education, birthweight, sex, ethnicity and indoor smoke exposure; and the longitudinal predictors BMI SDS, and the linear and quadratic terms of the growth curve describing a child's BMI SDS development over time, as well as the longitudinal predictors' interactions with age. The area under the curve of the model after internal validation was 0.845 and Nagelkerke R2 was 0.351.CONCLUSIONS: A dynamic prediction model for overweight was developed with a good predictive ability using easily obtainable predictor information. External validation is needed to confirm that the model has potential for use in practice.</p

Repeatedly measured predictors: a comparison of methods for prediction modeling

Abstract Background In literature, not much emphasis has been placed on methods for analyzing repeatedly measured independent variables, even less so for the use in prediction modeling specifically. However, repeated measurements could especially be interesting for the construction of prediction models. Therefore, our objective was to evaluate different methods to model a repeatedly measured independent variable and a long-term fixed outcome variable into a prediction model. Methods Six methods to handle a repeatedly measured predictor were applied to develop prediction models. Methods were evaluated with respect to the models’ predictive quality (explained variance R 2 and the area under the curve (AUC)) and their properties were discussed. The models included overweight and BMI-standard deviation score (BMI-SDS) at age 10 years as outcome and seven BMI-SDS measurements between 0 and 5.5 years as longitudinal predictor. Methods for comparison encompassed developing models including: all measurements; a single (here: the last) measurement; a mean or maximum value of all measurements; changes between subsequent measurements; conditional measurements; and growth curve parameters. Results All methods, except for using the maximum or mean, resulted in prediction models for overweight of similar predictive quality, with adjusted Nagelkerke R 2 ranging between 0.230 and 0.244 and AUC ranging between 0.799 and 0.807. Continuous BMI-SDS prediction showed similar results. Conclusions The choice of method depends on hypothesized predictor-outcome associations, available data, and requirements of the prediction model. Overall, the growth curve method seems to be the most flexible method capable of incorporating longitudinal predictor information without loss in predictive quality

Additional file 1: of Repeatedly measured predictors: a comparison of methods for prediction modeling

Figure showing the structure of the original Terneuzen Birth Cohort data, the broken stick data, and the multiple imputed-data; Timing of BMI-SDS measurement for five selected participants of the Terneuzen Birth Cohort. (PDF 35 kb