Academic Unit of Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, Southampton, UK.
Academic Unit of Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK.
Obes Rev. 2018 Mar;19(3):302-312. doi: 10.1111/obr.12640. Epub 2017 Dec 19.
Childhood obesity is a serious public health challenge, and identification of high-risk populations with early intervention to prevent its development is a priority. We aimed to systematically review prediction models for childhood overweight/obesity and critically assess the methodology of their development, validation and reporting.
Medline and Embase were searched systematically for studies describing the development and/or validation of a prediction model/score for overweight and obesity between 1 to 13 years of age. Data were extracted using the Cochrane CHARMS checklist for Prognosis Methods.
Ten studies were identified that developed (one), developed and validated (seven) or externally validated an existing (two) prediction model. Six out of eight models were developed using automated variable selection methods. Two studies used multiple imputation to handle missing data. From all studies, 30,475 participants were included. Of 25 predictors, only seven were included in more than one model with maternal body mass index, birthweight and gender the most common.
Several prediction models exist, but most have not been externally validated or compared with existing models to improve predictive performance. Methodological limitations in model development and validation combined with non-standard reporting restrict the implementation of existing models for the prevention of childhood obesity.
儿童肥胖是一个严重的公共卫生挑战,优先考虑识别高危人群并进行早期干预以预防其发展。我们旨在系统地回顾儿童超重/肥胖的预测模型,并批判性地评估其开发、验证和报告的方法。
系统地在 Medline 和 Embase 中搜索描述 1 至 13 岁儿童超重和肥胖预测模型/评分的开发和/或验证的研究。使用 Cochrane CHARMS 预后方法检查表提取数据。
确定了十项研究,其中一项研究开发了(一项)、开发和验证了(七项)或外部验证了现有的(两项)预测模型。其中六个模型使用自动变量选择方法开发。两项研究使用多重插补处理缺失数据。从所有研究中,共纳入 30475 名参与者。在 25 个预测因素中,只有七个因素被纳入了多个模型,其中母亲的体重指数、出生体重和性别最为常见。
存在一些预测模型,但大多数模型尚未经过外部验证或与现有模型进行比较,以提高预测性能。模型开发和验证中的方法学限制以及非标准报告限制了现有模型在预防儿童肥胖方面的实施。