中国循证儿科杂志 ›› 2021, Vol. 16 ›› Issue (2): 109-113.DOI: 10.3969/j.issn.1673-5501.2021.02.006

• 论著 • 上一篇    下一篇

女孩中枢性性早熟诊断预测模型的建立和验证

吴文涌, 陈瑞敏, 袁欣   

  1. 福建医科大学附属福州儿童医院 福州,350000
  • 收稿日期:2020-12-28 修回日期:2021-04-19 出版日期:2021-04-25 发布日期:2021-06-04
  • 通讯作者: 陈瑞敏,email:Chenrm321@sina.com
  • 基金资助:
    1 福州市临床重点专科建设项目:201610191;2 福建省福州儿童医院重点专科专项课题:ZD-2019-02

Development and verification of a diagnostic prediction model for girls with central precocious puberty

WU Wenyong, CHEN Ruimin, YUAN Xin   

  1. Fuzhou Children's Hospital of Fujian Medical University, Fuzhou 350000, China
  • Received:2020-12-28 Revised:2021-04-19 Online:2021-04-25 Published:2021-06-04
  • Contact: CHEN Ruimin,email:Chenrm321@sina.com

摘要: 背景 促性腺激素释放激素(GnRH)激发试验是目前诊断中枢性性早熟(CPP)的金标准,但需多次采血。以黄体生成素(LH)基础值诊断CPP在不同研究中的截断值差异较大。目前已报道的CPP诊断预测模型,或操作不便,或诊断效能不满意。目的 建立便于临床操作且诊断效能较高的预测模型辅助诊断女孩CPP。设计 收集完成GnRH激发试验的性早熟(PP)女孩的临床资料,应用Lasso回归分析筛选CPP的预测因子,Logistic回归建立预测模型。方法 纳入2014年1月至2020年4月在福建医科大学附属福州儿童医院就诊、开始出现乳房发育的年龄≥4岁且<8岁、完成GnRH激发试验的PP女孩,截取临床资料。通过文献复习和咨询内分泌专业临床专家意见,初步筛选出相互独立的预测因子,进行变量转换后,应用Lasso回归分析确定最终预测因子,重新拟合Logistic模型,分析其诊断效能并进行内部验证。主要结局指标 模型对女孩CPP的诊断效能。结果 1 107例PP女孩进入分析,CPP 537例,非CPP 570例。最终纳入5个预测因子:病程、乳房Tanner分期、LH基础值、骨龄(BA)和子宫大小,建立女孩CPP的预测模型:LN[P/(1-P)] =-5.508+1.579×LH基础值(“Middle”)+2.861×LH基础值(“High”)+1.191×子宫大小+0.316×BA+0.371×病程(“Middle”)+0.430×病程(“Long”)+0.285×乳房Tannar分期(“B>2”)。该模型ROC的AUC为0.858,当预测截点为0.476时,约登指数最大,敏感度为72.6%,特异度为86.7%;预测截点为0.75时,特异度为95.1%,敏感度为50.5%;预测截点为0.25时,敏感度为90.9%,特异度为51.9%。结论 女孩CPP诊断预测模型诊断效能较为满意,不同的诊断截点可用于临床的诊断或筛查。

关键词: 女孩, 中枢性性早熟, 预测模型, Lasso回归

Abstract: Background Gonadotropin-releasing hormone (GnRH) stimulation test is the gold standard for the diagnosis of central precocious puberty (CPP) at present, but multiple blood samples are needed. The truncation value of basic luteinizing hormone (LH) in diagnosing CPP varies greatly in different studies. The diagnostic prediction models of CPP that have been reported are either inconvenient to operate or unsatisfactory in their diagnostic performance.Objective To establish a predictive model which is convenient for clinical operation and has high diagnostic efficiency to assist the diagnosis of CPP in girls.DesignClinical data of girls with precocious puberty (PP) who completed the GnRH stimulation test were collected. Lasso regression analysis was used to screen the predictors of CPP, and Logistic regression was used to establish the prediction model.Methods Clinical data were collected from girls admitted to Fuzhou Children's Hospital of Fujian Medical University from January 2014 to April 2020 with PP who had undergone GnRH stimulation test and whose breast developed at the age of ≥4 to < 8. Through literature review and consultation with clinical experts in endocrinology, independent predictors were screened out preliminarily. After variable conversion, Lasso regression analysis was used to determine the final predictors. Logistic model was re-fitted to analyze its diagnostic performance and conduct internal validation.Main outcome measures The diagnostic efficacy of the model for CPP in girls.Results A total of 1,107 PP girls were included into the analysis, including 537 CPP and 570 non-CPP. Finally, five predictors were included-course of PP, breast Tanner stage, basic LH, bone age (BA), and uterine size-to establish a prediction model for CPP in girls: LN[P/(1-P)]=-5.508+1.579×basic LH("Middle")+2.861×basic LH("High")+1.191×uterine size+0.316×BA+0.371×course of PP("Middle")+0.430×course of PP("Long")+0.285×breast Tannar stage("B>2"). The AUC of the ROC of this model was 0.858. When the predicted cut-off point was 0.476, the Youden index was the highest, with sensitivity of 72.6% and specificity of 86.7%. When the prediction cut-off point was 0.75, the specificity and sensitivity were 95.1% and 50.5%. When the prediction cut-off point was 0.25, the sensitivity and specificity were 90.9% and 51.9%.Conclusion The diagnostic performance of the diagnostic prediction model for CPP in girls constructed in this paper is relatively satisfactory, and different diagnostic cut-off points can be used for clinical diagnosis or screening.

Key words: Girls, Central precocious puberty, Prediction model, Lasso regression