Diagnostic model based on multiple factors for girls with central precocious puberty

Area of Science:

• Pediatric Endocrinology
• Reproductive Medicine
• Clinical Diagnostics

Background:

• The Gonadotropin-Releasing Hormone (GnRH) stimulation test is the standard for diagnosing central precocious puberty (CPP).
• Practical limitations of the GnRH test necessitate alternative diagnostic approaches.
• Central precocious puberty (CPP) requires accurate and timely diagnosis for effective management.

Purpose of the Study:

• To develop a predictive diagnostic model for central precocious puberty (CPP) in girls from northern China.
• To identify key clinical and biochemical indicators for CPP diagnosis.
• To create a user-friendly nomogram for clinical application in CPP diagnosis.

Main Methods:

• A cohort of 163 girls with precocious puberty (PP) was analyzed between December 2018 and December 2019.
• Multifactor logistic regression was employed to identify significant diagnostic factors.
• A nomogram was constructed based on the multivariate logistic regression model for clinical utility.

Main Results:

• Basal luteinizing hormone (LH), inhibin B, bone age, and uterine length were identified as significant predictors of CPP.
• The logistic regression model demonstrated strong diagnostic capability with an Area Under the Curve (AUC) of 0.906.
• Specific odds ratios (OR) and confidence intervals (CI) were established for each identified diagnostic factor.

Conclusions:

• A novel nomogram-based diagnostic model for CPP in girls was successfully developed using readily available clinical data.
• The model incorporates basal LH, inhibin B, bone age, and uterine body length for accurate CPP prediction.
• This tool offers a practical alternative for diagnosing CPP, especially in regions with limited access to specialized testing.