Development of a disease diagnostic model to predict the occurrence of central precocious puberty of female.

Objectives: To develop a clinical model for predicting the occurrence of Central Precocious Puberty based on the breast development outcomes in chinese girls.

Methods: This is a retrospective study, which included a total of 1,001 girls aged 6-9 years old who visited the outpatient clinic of Beijing Children's Hospital from January 2017 to October 2022 for "breast development". Participants were categorized into pubertal development (PD) cohort and simple premature breast development (PT) according to the criteria, and information was collected and tested for relevant indicators. After dealing with missing data, logistic regression, LASSO regression and random forest were used to screen the variables, and support vector machine models were built with SMOTE oversampling and ten-fold cross-validation to assess the effectiveness of the models in the training and validation sets.

Results: A total of 1,001 girls were included in the analysis, of whom 369 (36.9 %) were diagnosed with PD and 632 (63.1 %) with PT. Body mass index (BMI), bone age (BA), luteinizing hormone (LH), follicle stimulating hormone (FSH), estradiol (E2), uterine diameter, and ovary volume were identified as the final predictor variables by three variable screening methods. The AUC of the constructed disease diagnostic model was 0.9457 in the developmental cohort and 0.8357 in the external validation group, and sensitivity analyses revealed that the performance of the constructed models with different variable selection strategies was similar.

Conclusions: A disease diagnostic model was developed that may help predict a girl's risk of diagnosing central precocious puberty.