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

Abstract

Summary 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 R 2 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.

Predicting the earliest deviation in weight gain in the course towards manifest overweight in offspring exposed to obesity in pregnancy: a longitudinal cohort study

“…Using these modifiable factors [ 84 ], we developed a novel strategy to identify infants likely to deviate from the normal BMI growth pattern as a subclinical stage before establishing preschool overweight. Unlike previous methods that offered prediction of manifest overweight [ 56 , 85 ] and/or were applicable at a certain age only [ 22 ] and were developed for offspring born to women of heterogeneous BMI [ 22 ], we propose a novel sequential strategy of prediction and re-evaluation of higher-than-normal weight gain in “high-risk” offspring of mothers with obesity at ages 3 months, 1 year, and 2 years to guide pediatric decision-making (Fig. 4 ).…”

Weight transitions and psychosocial factors: A longitudinal cohort study of Finnish primary school children with overweight

“…Collected EHR data lacked important predictors for childhood obesity, such as early-life factors and parental SES, BMI and education ( Juonala et al, 2020 , Moreira et al, 2019 , Wang and Lim, 2012 , Welten et al, 2020 , Ziauddeen et al, 2018 ). Against our assumption, overweight issues of families were mainly unavailable in primary health care EHRs.…”

“…Early-life factors, known to be associated with childhood obesity ( Juonala et al, 2020 , Mattsson et al, 2019 , Schellong et al, 2012 , Woo Baidal et al, 2016 ), can be used in risk assessment ( Welten et al, 2020 , Ziauddeen et al, 2018 ) but may be unidentifiable during school health care visits. Family- and school-related factors later in childhood, such as bullying and study difficulties, prevail among children with obesity ( Kautiainen et al, 2009 , van Geel et al, 2014 ) and can potentially be ameliorated with appropriate support.…”

Development of a Prediction Model to Identify Children at Risk of Future Developmental Delay at Age 4 in a Population-Based Setting

“…However, our model should first be externally validated in another cohort to assess whether adaptations to specific populations are needed. Future studies in this field should examine the possibility of developing a dynamic prediction model [ 35 ] that can be applied within PCH, enabling a reassessment of the risk after incorporating new information into the model, which may further improve developmental surveillance. Future studies should also focus on implementing the calculator in practice, e.g., by including the calculator into the digitized (preventive) well-child care files, in several settings, and evaluate the effects of combining the calculator with targeted interventions.…”