Development and Validation of a Clinical Prediction Model for Growth Hormone Deficiency in Children with Short Stature: A Retrospective Study in China.
Mali Li, Chao Liu, Yuan Yang, Shuwen Hu, Jia Li, Shichao Qiu, Zhihua Wang
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引用次数: 0
Abstract
Background: A multitude of congenital and acquired conditions can result in short stature, each with distinctive clinical presentations and treatment options. We aimed to develop and validate a prediction model to identify GHD among children with short stature using clinical and laboratory parameters.
Methods: This retrospective observational study included 1120 children with short stature from a hospital in China. The data were randomly split into a derivation set and a validation set. Features were selected based on clinical relevance and statistical significance to construct a multivariate logistic regression model in the derivation set. Discrimination, calibration, and prediction accuracy were evaluated on both sets.
Results: Of the 1120 children, 278 (25%) were diagnosed with GHD, 694 (62%) were male, and the mean age was 6.97 ± 2.97 years. The derivation set comprises 785 (70%) children. The model incorporates four predictors: age (OR=0.761; 95% CI 0.660, 0.873), delayed bone age (OR=1.841; 95% CI 1.365, 2.537), IGF-1 SDS (OR=0.148; 95% CI 0.095, 0.220), and IGF-1/IGFBP-3 ratio (OR=0.901; 95% CI 0.870, 0.930). The model exhibits good discriminative ability, with an AUC of 0.952 (0.937, 0.967) in the derivation set and 0.950 (0.927, 0.973) in the validation set. Furthermore, it shows high accuracy with sensitivity and specificity of 0.895 in the derivation set, which was 0.946 and 0.851 in the validation set. The model also demonstrates reliable calibration.
Conclusion: We have developed a prediction model for accurate screening of GHD in children with short stature.
背景：许多先天性和后天条件可导致身材矮小，每一个都有不同的临床表现和治疗选择。我们的目的是建立并验证一个预测模型，通过临床和实验室参数来识别矮小儿童的GHD。方法：本回顾性观察研究纳入了中国某医院1120名身材矮小的儿童。数据随机分为派生集和验证集。根据临床相关性和统计显著性选择特征，在衍生集中构建多元逻辑回归模型。对两组进行判别、校准和预测精度评估。结果：1120例患儿中，诊断为GHD的278例（25%），男性694例（62%），平均年龄6.97±2.97岁。派生集包括785（70%）个子。该模型包含四个预测因素：年龄（OR=0.761; 95% CI 0.660, 0.873）、延迟骨龄（OR=1.841; 95% CI 1.365, 2.537）、IGF-1 SDS （OR=0.148; 95% CI 0.095, 0.220）和IGF-1/IGFBP-3比值（OR=0.901; 95% CI 0.870, 0.930）。该模型具有良好的判别能力，推导集的AUC为0.952(0.937,0.967)，验证集的AUC为0.950（0.927,0.973）。推导集的灵敏度和特异度分别为0.895，验证集的灵敏度和特异度分别为0.946和0.851。该模型还证明了标定的可靠性。结论：我们建立了一个准确筛查矮小儿童GHD的预测模型。
期刊介绍：
The Journal of Multidisciplinary Healthcare (JMDH) aims to represent and publish research in healthcare areas delivered by practitioners of different disciplines. This includes studies and reviews conducted by multidisciplinary teams as well as research which evaluates or reports the results or conduct of such teams or healthcare processes in general. The journal covers a very wide range of areas and we welcome submissions from practitioners at all levels and from all over the world. Good healthcare is not bounded by person, place or time and the journal aims to reflect this. The JMDH is published as an open-access journal to allow this wide range of practical, patient relevant research to be immediately available to practitioners who can access and use it immediately upon publication.
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