目的 构建并验证乳房早发育女童发生中枢性性早熟的风险列线图模型，旨在建立可量化、可视化的风险动态评估工具，为个体化干预时机选择提供循证支持。 方法 回顾性纳入2023年9月至2024年12月就诊于河北省人民医院儿科的162例乳房早发育女童，根据青春期发育情况及促性腺素释放激素激发试验结果，将研究对象分为单纯乳房早发育组（59例）和中枢性性早熟组（103例）。对两组间临床资料进行分析对比，采用二元logistic回归及Lasso回归（λ最小均方误差准则）对变量进行筛选，构建列线图模型，并绘制受试者工作特征曲线、校准曲线、决策曲线评估效能。 结果 两组女童身高标准差积分、体质量标准差积分、骨龄、骨龄与实际年龄差值、骨龄指数、> 4 mm卵泡数、子宫长径、子宫容积、雌二醇、胰岛素样生长因子1（IGF-1）、促黄体生成素基础值（luteinizing hormone，LH）、促卵泡生成素基础值、LH基础值/FSH基础值、LH峰值、促甲状腺激素（hyroid-stimulating hormone，TSH）峰值、LH峰值/FSH峰值间存在统计学差异（P < 0.05），经单因素logistic回归、Lasso回归及多因素logistic回归最终筛选出LH基础值、身高标准差积分、IGF-1和> 4 mm卵泡数四项独立预测因子，构建列线图模型，显示其AUC达0.932，阈值为0.414，此时敏感度为95.1%、特异度为78.0%，模型校准度良好，临床净收益率54.6%。 结论 整合LH基础值、身高标准差积分、IGF-1及> 4 mm卵泡数的列线图模型能有效预测乳房早发育女童发生中枢性性早熟的风险，为无创筛查提供实用工具，助力临床早期干预。

Objective To construct and validate a nomogram model for predicting the risk of central precocious puberty （CPP） in girls with early breast development， to establish a quantifiable and visual dynamic risk assessment tool and provide evidence-based support for the selecting the timing of individualized intervention. Methods A total of 162 girls with early breast development who visited the Pediatrics Department of Hebei Provincial People's Hospital from September 2023 to December 2024 were retrospectively included. Based on pubertal development status and the results of the gonadotropin-releasing hormone stimulation test， the subjects were divided into an isolated premature thelarche （IPT） group （59 cases） and the CPP group （103 cases）. Clinical data were compared between the two groups. Predictors were screened using binary logistic regression and LASSO regression. A nomogram model was constructed， and its performance was evaluated using the receiver operating characteristic （ROC） curve， calibration curve， and decision curve analysis. Results Statistical analysis revealed significant differences between the two groups in height standard deviation score， weight standard deviation score， bone age， bone age advancement， bone age index， the number of follicles greater than 4 mm， uterine volume， estradiol， insulin-like growth factor 1 （IGF-1）， basal luteinizing hormone （LH） level， basal follicle-stimulating hormone （FSH） level， LH/FSH ratio， LH peak， FSH peak， and LH peak/FSH peak ratio （P < 0.05）. Four independent predictors of basal LH level， height standard deviation score， IGF-1， and the number of follicles greater than 4mm were screened out. The nomogram model showed an AUC of 0.932， at a probability threshold of 0.414，the sensitivity was 95.1%， the specificity was 78.0%， the model demonstrated good calibration and the clinical net benefit rate was 54.6%. Conclusions The nomogram model incorporating basal LH level， height standard deviation score， IGF-1 and the number of follicles greater than 4mm can effectively predict the risk of central precocious puberty in girls with early breast development. It provides a practical tool for non-invasive screening and facilitates early clinical intervention.