Huang Shaojun, Chen Zhiqi, Chen Rongping, Zhang Zhen, Sun Jia, Chen Hong
Department of Endocrinology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Front Pediatr. 2022 Dec 6;10:1006011. doi: 10.3389/fped.2022.1006011. eCollection 2022.
Short stature in children is an important global health issue. This study aimed to analyze the risk factors associated with short stature and to construct a clinical prediction model and risk classification system for short stature.
This cross-sectional study included 12,504 children aged 6-14 years of age from 13 primary and secondary schools in Pingshan District, Shenzhen. A physical examination was performed to measure the height and weight of the children. Questionnaires were used to obtain information about children and their parents, including sex, age, family environment, social environment, maternal conditions during pregnancy, birth and feeding, and lifestyle. The age confounding variable was adjusted through a 1 : 1 propensity score matching (PSM) analysis and 1,076 children were selected for risk factor analysis.
The prevalence of short stature in children aged 6-14 years was 4.3% in the Pingshan District, Shenzhen. The multivariate logistic regression model showed that the influencing factors for short stature were father's height, mother's height, annual family income, father's level of education and parents' concern for their children's height in the future ( < 0.05). Based on the short stature multivariate logistic regression model, a short stature nomogram prediction model was constructed. The area under the ROC curve (AUC) was 0.748, indicating a good degree of discrimination of the nomogram. According to the calibration curve, the Hosmer-Lemesio test value was 0.917, and the model was considered to be accurate. Based on a risk classification system derived from the nomogram prediction model, the total score of the nomogram was 127.5, which is considered the cutoff point to divides all children into low-risk and high-risk groups.
This study analyzed the risk factors for short stature in children and constructed a nomogram prediction model and a risk classification system based on these risk factors, as well as providing short stature screening and assessment individually.
儿童身材矮小是一个重要的全球健康问题。本研究旨在分析与身材矮小相关的危险因素,并构建身材矮小的临床预测模型和风险分类系统。
这项横断面研究纳入了来自深圳坪山区13所中小学的12504名6至14岁的儿童。进行体格检查以测量儿童的身高和体重。通过问卷获取有关儿童及其父母的信息,包括性别、年龄、家庭环境、社会环境、孕期母亲状况、出生和喂养情况以及生活方式。通过1:1倾向评分匹配(PSM)分析调整年龄混杂变量,选择1076名儿童进行危险因素分析。
深圳坪山区6至14岁儿童身材矮小的患病率为4.3%。多因素logistic回归模型显示,身材矮小的影响因素为父亲身高、母亲身高、家庭年收入、父亲教育程度以及父母对孩子未来身高的关注程度(<0.05)。基于身材矮小多因素logistic回归模型,构建了身材矮小列线图预测模型。ROC曲线下面积(AUC)为0.748,表明列线图具有良好的区分度。根据校准曲线,Hosmer-Lemesio检验值为0.917,模型被认为是准确的。基于从列线图预测模型得出的风险分类系统,列线图总分127.5,以此作为将所有儿童分为低风险和高风险组的切点。
本研究分析了儿童身材矮小的危险因素,构建了基于这些危险因素的列线图预测模型和风险分类系统,并为个体提供身材矮小筛查和评估。