女童中枢性性早熟诊断模型构建、评分卡设计及外部验证.

邱世超; 汪治华; 宋娜; 赵婷; 廉怡华; 余佳; 李玛丽; 刘超

Objective To establish an efficient and clinically applicable predictive model and scoring system for central precocious puberty (CPP) in girls, and to develop a diagnostic prediction application. Methods A total of 342 girls aged 4 to 9 years with precocious puberty were included, comprising 216 cases of CPP and 126 cases of isolated premature thelarche. Lasso regression was used to screen for predictive factors, and logistic regression was employed to establish the predictive model. Additionally, a scoring system was constructed using the evidence weight binning method. Data from 129 girls aged 4 to 9 years with precocious puberty were collected for external validation of the scoring system. Results The logistic regression model incorporated five predictive factors: age, insulin-like growth factor-1 (IGF-1), serum follicle-stimulating hormone (FSH), the luteinizing hormone (LH)/FSH baseline ratio, and uterine thickness. The calculation formula was: ln(P/1-P)=-8.439 + 0.216 × age (years) + 0.008 × IGF-1 (ng/mL) + 0.159 × FSH (mIU/mL) + 9.779 × LH/FSH baseline ratio + 0.284 × uterine thickness (mm). This model demonstrated good discriminative ability (area under the curve=0.892) and calibration (Hosmer-Lemeshow test P>0.05). The scoring system based on this logistic regression model showed good discrimination in both the prediction model and external validation datasets, with areas under the curve of 0.895 and 0.805, respectively. Based on scoring system scores, the population was stratified into three risk levels: high, medium, and low. In the high-risk group, the prevalence of CPP exceeded 90%, while the proportion was lower in the medium and low-risk groups. Conclusions The CPP diagnostic predictive model established for girls aged 4 to 9 years exhibits good diagnostic performance. The scoring system can effectively and rapidly stratify the risk of CPP, providing valuable reference for clinical decision-making.

PRECOCIOUS puberty; LOGISTIC regression analysis; FOLLICLE-stimulating hormone; PREDICTION models; REGRESSION analysis

Chinese Journal of Contemporary Pediatrics, 2024, Vol 26, Issue 12, p1267

1008-8830

Academic Journal

10.7499/j.issn.1008-8830.2405079