文献类型：期刊文献

英文题名：Development and validation of a nomogram to predict poor short-term response to recombinant human growth hormone treatment in children with growth disorders

作者：Feng, Y. D.[1];Wang, J.[2,4];Tao, Z. B.[3];Jiang, H. K.[1]

第一作者：Feng, Y. D.

通信作者：Jiang, HK[1]

机构：[1]China Med Univ, Affiliated Hosp 1, Dept Pediat, 155 Nanjing North St, Shenyang 110000, Liaoning, Peoples R China;[2]Gansu Univ Chinese Med, Lanzhou, Peoples R China;[3]Lanzhou Univ, Hosp 1, Dept Pediat, Lanzhou, Peoples R China;[4]Lanzhou Matern & Child Hlth Care Hosp, Dept Neonatol, Lanzhou, Peoples R China

第一机构：China Med Univ, Affiliated Hosp 1, Dept Pediat, 155 Nanjing North St, Shenyang 110000, Liaoning, Peoples R China

通信机构：[1]corresponding author), China Med Univ, Affiliated Hosp 1, Dept Pediat, 155 Nanjing North St, Shenyang 110000, Liaoning, Peoples R China.

年份：2023

卷号：46

期号：7

起止页码：1343

外文期刊名：JOURNAL OF ENDOCRINOLOGICAL INVESTIGATION

收录：;Scopus(收录号：2-s2.0-85161957250);WOS:【SCI-EXPANDED(收录号:WOS:000895609400001)】；

语种：英文

外文关键词：Growth hormone deficiency; Turner syndrome; Small for gestational age; Idiopathic short stature; LASSO regression; Nomogram

摘要：Purpose The purpose of this study was to develop and validate a clinical predictive model for predicting the likelihood of a poor therapeutic response during the first year of recombinant human growth hormone (rhGH) treatment in children with growth disorders. Methods A total of 627 pediatric patients with growth disorders (GHD, ISS, TS, SGA) from The LG Growth Study cohort were evaluated. Restricted cubic splines (RCS) were utilized to investigate the association between predictors and the risk of poor rhGH response. Variables were selected using LASSO regression, and multivariate logistics regression models were established. Receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC) were used to assess the predictive model's accuracy and clinical value. The predictive accuracy of the model was validated on the testing set. Results Two predictive models containing 8 baseline predictors (diagnosis, age, height SDS, bone age minus chronological age, rhGH dosage, distance from mid-parental height in SDS, weight SDS, IGF-1 SDS) and 1 post-treatment predictor (height SDS gain at 6 months) were constructed by multivariate logistic regression analyses. The nomogram was built based on the multivariate predictive model and showed good discrimination and model fit effects in both the training set and the testing set. DCA and CIC analyses presented good clinical usability. Conclusion The clinical predictive model for predicting the probability of poor short-term response of rhGH treatment in pediatric patients with growth disorders is useful and can assist physicians in making clinical decisions.