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

OBJECTIVES

The GnRH stimulation test has been used as the gold standard for the diagnosis of central precocious puberty (CPP), but it has some practical barriers. This study intends to build a diagnostic model of CPP in girls based on the population in northern China.

METHODS

A total of 163 girls with precocious puberty (PP) were included from December 2018 to December 2019. Multifactor logistic regression analysis was conducted. Based on the results of multivariate logistic regression analysis, a nomogram was established for clinical application.

RESULTS

A multi logistic regression model showed that LH (OR=1.238, 95 % CI: 1.067-1.436, p=0.005), inhibin B (OR=1.066, 95 % CI: 1.032-1.100, p<0.001), bone age (OR=1.563, 95 % CI: 1.037-2.358, p=0.033), and uterine length (OR=1.180, 95 % CI: 1.034-1.348, p=0.014) were diagnostic factors for CPP. The prediction model AUC was 0.906 (95 % CI: 0.852-0.959, p<0.001).

CONCLUSIONS

We successfully developed a nomogram model for CPP patients based on clinical data. The diagnostic prediction model included four indicators: basal LH, inhibin B, bone age, and uterine body length.