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. 2022 Dec 6:10:1006011.
doi: 10.3389/fped.2022.1006011. eCollection 2022.

Analysis of risk factors and construction of a prediction model for short stature in children

Shaojun Huang  1 , Zhiqi Chen  1 , Rongping Chen  1 , Zhen Zhang  1 , Jia Sun  1 , Hong Chen  1
Affiliations

Analysis of risk factors and construction of a prediction model for short stature in children

Shaojun Huang et al. Front Pediatr. .

Abstract

Background: 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.

Methods: 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.

Results: 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 (P < 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.

Conclusion: 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.

Keywords: children; nomogram prediction model; risk classification system; risk factors; short stature.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart of data screening.
Figure 2
Figure 2
Incidence of short stature among children in Pingshan district, Shenzhen in 2020.
Figure 3
Figure 3
Multivariate logistic regression forest plot of short stature in children.
Figure 4
Figure 4
The prediction nomogram models and risk classification system for short stature in children.
Figure 5
Figure 5
Evaluation of nomogram prediction models. (A) Area under the ROC of the nomogram model; (B) the calibration curve of the nomogram model. Dashed line represents ideal prediction, and solid line represents observed nomogram.
See this image and copyright information in PMC

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