Clinical risk score for central precocious puberty among girls with precocious pubertal development: a cross sectional study

Abstract

Background: The gold standard for the diagnosis of central precocious puberty (CPP) is gonadotropin-releasing hormone (GnRH) or GnRH analogs (GnRHa) stimulation test. But the stimulation test is time-consuming and costly. Our objective was to develop a risk score model readily adoptable by clinicians and patients.

Methods: A cross-sectional study based on the electronic medical record system was conducted in the Children's Hospital, Fudan University, Shanghai, China from January 2010 to August 2016. Patients with precocious puberty were randomly split into the training (n = 314) and validation (n = 313) sample. In the training sample, variables associated with CPP (P < 0.2) in univariate analyses were introduced in a multivariable logistic regression model. Prediction model was selected using a forward stepwise analysis. A risk score model was built with the scaled coefficients of the model and tested in the validation sample.

Results: CPP was diagnosed in 54.8% (172/314) and 55.0% (172/313) of patients in the training and validation sample, respectively. The CPP risk score model included age at the onset of puberty, basal luteinizing hormone (LH) concentration, largest ovarian volume, and uterine volume. The C-index was 0.85 (95% CI: 0.81-0.89) and 0.86 (95% CI: 0.82-0.90) in the training and the validation sample, respectively. Two cut-off points were selected to delimitate a low- (< 10 points), median- (10-19 points), and high-risk (≥ 20 points) group.

Conclusions: A risk score model for the risk of CPP had a moderate predictive performance, which offers the advantage of helping evaluate the requirement for further diagnostic tests (GnRH or GnRHa stimulation test).

Keywords: Central precocious puberty; GnRH stimulation test; Risk score model.

Fig. 1

Study subjects flow chart

Fig. 2

Receiver operating characteristic curves of the prediction model and risk score system. a Training sample. b Validation sample