Analysis of risk factors and construction of a prediction model for short stature in children - Huang, Shaojun; Chen, Zhiqi; Chen, Rongping; Zhang, Zhen; Sun, Jia; Chen, Hong (Frontiers in Pediatrics, 2022)
Background: Short stature in children has been identified as an important issue of global health. The study aims to analyze the risk factors of short stature and construct a clinical prediction model and risk stratification system for short stature. Methods: This study was a cross-sectional study which included 12504 children aged 6-14 from 13 primary and secondary schools in Pingshan district, Shenzhen. Physical examination was used to measure the height and weight of children. Questionnaires were used to obtain information about children and their parents, including gender, age, family environment, social environment, maternal conditions during pregnancy, born and fed, and life style. Adjust age confounding variable via 1:1 Propensity Score Matching (PSM) analysis and 1076 children were selected in the risk factors analysis. Results: The prevalence of short stature in children from 6 to 14 years old is 4.3% in Pingshan district, Shenzhen. Multivariate logistic regression model showed that the influencing factors of short stature are: father's height, mother's height, annual family income, father's education level, and concern about their children's height in future by parents (P<0.05). Based on the multivariate logistic regression model of short stature, a nomogram prediction model of short stature is constructed. The area under the ROC curve (AUC) value is 0.748 which presents a good degree of discrimination of the nomogram. According to the calibration curve, the Hosmer-Lemesio test value is 0.917 and the model is accurate. Based on a risk classification system which stemmed from nomogram prediction model, the total score of the nomogram is 127.5 and regards as the cut-off point, which divides all children into low-risk group and high-risk group. Conclusion: This research analyses the risk factors of short stature in children and constructs a nomogram prediction model and risk classification system based on the risk factors, as well as providing screening and assessment of short stature individually.