Public Health and Epidemiology Group, Department of Health Science and Technology, Aalborg University, Aalborg East, Denmark.
Unit of Clinical Biostatistics, Aalborg University Hospital, Aalborg, Denmark.
Pediatr Obes. 2021 Oct;16(10):e12790. doi: 10.1111/ijpo.12790. Epub 2021 Mar 29.
Information on postnatal weight gain is important for predicting later overweight and obesity, but it is unclear whether inclusion of this postnatal predictor improves the predictive performance of a comprehensive model based on prenatal and birth-related predictors.
To compare performance of prediction models based on predictors available at birth, with and without information on infancy weight gain during the first year when predicting childhood obesity risk.
A Danish register-based cohort study including 55.041 term children born between January 2004 and July 2011 with birthweight >2500 g registered in The Children's Database was used to compare model discrimination, reclassification, sensitivity and specificity of two models predicting risk of childhood obesity at school age. Each model consisted of eight predictors available at birth, one additionally including information on weight gain during the first 12 months of life.
The area under the receiving operating characteristic curve increased from 0.785 (95% confidence interval (CI) [0.773-0.798]) to 0.812 (95% CI [0.801-0.824]) after adding weight gain information when predicting childhood obesity. Adding this information correctly classified 30% more children without obesity and 21% with obesity and improved sensitivity from 0.42 to 0.48. Specificity remained unchanged at 0.91.
Adding infancy weight gain information improves discrimination, reclassification and sensitivity of a comprehensive prediction model based on predictors available at birth.
产后体重增加的信息对于预测后期超重和肥胖很重要，但目前尚不清楚在基于产前和与出生相关的预测因素的综合模型中纳入该产后预测因素是否会提高预测性能。
比较基于出生时可用预测因素的预测模型，以及在预测儿童肥胖风险时是否包含婴儿期第一年体重增加信息的表现。
一项基于丹麦登记的队列研究，包括 2004 年 1 月至 2011 年 7 月期间出生的 55041 名足月儿童，出生体重>2500g，在儿童数据库中登记，用于比较两种预测儿童肥胖风险的模型的判别能力、重新分类、敏感性和特异性。每个模型都由出生时的八个预测因素组成，另一个模型则包括婴儿期前 12 个月体重增加的信息。
当预测儿童肥胖时，加入体重增加信息后，接收者操作特征曲线下面积从 0.785（95%置信区间[0.773-0.798]）增加到 0.812（95%置信区间[0.801-0.824]）。加入该信息正确分类了 30%的无肥胖儿童和 21%的肥胖儿童，且敏感性从 0.42提高到 0.48。特异性仍保持在 0.91。
在基于出生时可用预测因素的综合预测模型中加入婴儿期体重增加信息可提高判别、重新分类和敏感性。
