What type of study is this?

This is a Cohort Study study (also classified as: Validation Study).

What question did this study set out to answer?

This research aims to develop age-specific models for diagnosing central precocious puberty using clinical biomarkers.

May 6, 2026International Journal of Clinical Practice0 citationsOpen Access

Enhancing the Evaluation of Central Precocious Puberty in Female Patients via Age‐Specific Models Incorporating Clinical Biomarkers

SXShuyu XieNorthern Jiangsu People's Hospital YHYingxuan HuangAffiliated Hospital of Youjiang Medical University for Nationalities CMChao MengChina Academy of Chinese Medical Sciences

Key Points

This research aims to develop age-specific models for diagnosing central precocious puberty using clinical biomarkers.
Retrospective review of clinical records from 529 girls with CPP and 808 with PPP.
Participants were stratified into age groups: under 8 years and 8 years or older.
Logistic regression analysis identified significant predictors for CPP; diagnostic algorithms were created accordingly.
Models were validated with a prospective cohort of 115 CPP and 162 PPP patients in 2021.
Key parameters like height, weight, bone age, and LH levels distinguished CPP from PPP.
Models achieved AUC values of 0.907 for < 8 years and 0.888 for ≥ 8 years, indicating high diagnostic efficacy.
The < 8 years model showed a sensitivity of 75.9% and specificity of 77.3%; the ≥ 8 years model had 77.9% sensitivity and 83.1% specificity.

Abstract

Objective This investigation sought to develop and prospectively validate age‐specific diagnostic models based on routinely accessible clinical biomarkers. These models differentiate central precocious puberty (CPP) from peripheral precocious puberty (PPP) in girls, providing a pragmatic strategy to reduce superfluous gonadotropin‐releasing hormone (GnRH) stimulation testing in practice. Methods A retrospective review was performed on clinical records of 529 girls diagnosed with CPP and 808 with PPP, managed between June 2015 and December 2020. The cohort was stratified into two age groups: under 8 years and 8 years or older. Clinical characteristics were compared, and logistic regression analysis identified significant predictors for CPP. Unique diagnostic algorithms were developed for each stratum. The discriminative capacity of these models was assessed via receiver operating characteristic (ROC) curve analysis. External validation was subsequently conducted using a prospective cohort of 115 CPP and 162 PPP patients recruited in 2021. Results Multiple parameters showed significant differences between the CPP and PPP groups, such as height, weight, ΔZ height, bone age, estradiol concentration, basal luteinizing hormone (LH) and follicle‐stimulating hormone (FSH) levels, the LH/FSH ratio, and uterine and ovarian volumes ( p < 0.05). For patients younger than 8 years, the derived algorithm included ΔZ height, weight, bone age, basal LH, and uterine volume. The model for girls aged ≥ 8 years utilized ΔZ height, bone age, basal LH, and uterine volume. Both tools showed high diagnostic efficacy, with area under the curve (AUC) values of 0.907 and 0.888, respectively, exceeding the discriminatory power of any individual parameter. In the validation cohort, the models demonstrated sensitivities of 75.9% (< 8 years) and 77.9% (≥ 8 years), with specificities of 77.3% and 83.1%. Conclusions The established biomarker‐driven models display strong diagnostic performance. A calculated model value greater than −1.47 in girls below 8 years of age or above 0.3 in those aged 8 years and older, suggests a high likelihood of CPP. Under these circumstances, conducting a confirmatory GnRH stimulation test is clinically justified.

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