Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study.

IF 2.8 3区 医学 Q3 ENDOCRINOLOGY & METABOLISM
Jiani Liu, Xin Zhang, Wei Li, Francis Manyori Bigambo, Dandan Wang, Xu Wang, Beibei Teng
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引用次数: 0

Abstract

Background: Short stature is a prevalent pediatric endocrine disorder for which early detection and prediction are pivotal for improving treatment outcomes. However, existing diagnostic criteria often lack the necessary sensitivity and specificity because of the complex etiology of the disorder. Hence, this study aims to employ machine learning techniques to develop an interpretable predictive model for normal-variant short stature and to explore how growth environments influence its development.

Methods: We conducted a case‒control study including 100 patients with normal-variant short stature who were age-matched with 200 normal controls from the Endocrinology Department of Nanjing Children's Hospital from April to September 2021. Parental surveys were conducted to gather information on the children involved. We assessed 33 readily accessible medical characteristics and utilized conditional logistic regression to explore how growth environments influence the onset of normal-variant short stature. Additionally, we evaluated the performance of the nine machine learning algorithms to determine the optimal model. The Shapley additive explanation (SHAP) method was subsequently employed to prioritize factor importance and refine the final model.

Results: In the multivariate logistic regression analysis, children's weight (OR = 0.92, 95% CI: 0.86, 0.99), maternal height (OR = 0.79, 95% CI: 0.72, 0.87), paternal height (OR = 0.83, 95% CI: 0.75, 0.91), sufficient nighttime sleep duration (OR = 0.48, 95% CI: 0.26, 0.89), and outdoor activity time exceeding three hours (OR = 0.02, 95% CI: 0.00, 0.66) were identified as protective factors for normal-variant short stature. This study revealed that parental height, caregiver education, and children's weight significantly influenced the prediction of normal-variant short stature risk, and both the random forest model and gradient boosting machine model exhibited the best discriminatory ability among the 9 machine learning models.

Conclusions: This study revealed a close correlation between environmental growth factors and the occurrence of normal-variant short stature, particularly anthropometric characteristics. The random forest model and gradient boosting machine model performed exceptionally well, demonstrating their potential for clinical applications. These findings provide theoretical support for clinical identification and preventive measures for short stature.

矮小的可解释预测模型及相关环境生长因子的探讨:一项病例对照研究。
背景:身材矮小是一种常见的儿科内分泌疾病,早期发现和预测是提高治疗效果的关键。然而,由于该病的病因复杂,现有的诊断标准往往缺乏必要的敏感性和特异性。因此,本研究旨在利用机器学习技术开发正常变异矮小的可解释预测模型,并探索生长环境如何影响其发展。方法:我们于2021年4月至9月在南京儿童医院内分泌科进行了一项病例对照研究,纳入了100例正常变异矮小患者,年龄与200例正常对照者相匹配。进行了家长调查,以收集有关儿童的信息。我们评估了33个容易获得的医学特征,并利用条件逻辑回归来探索生长环境如何影响正常变异矮小的发病。此外,我们评估了九种机器学习算法的性能,以确定最优模型。随后采用Shapley加性解释(SHAP)方法对因子重要性进行排序,并完善最终模型。结果:在多因素logistic回归分析中,儿童体重(OR = 0.92, 95% CI: 0.86, 0.99)、母亲身高(OR = 0.79, 95% CI: 0.72, 0.87)、父亲身高(OR = 0.83, 95% CI: 0.75, 0.91)、充足的夜间睡眠时间(OR = 0.48, 95% CI: 0.26, 0.89)和户外活动时间超过3小时(OR = 0.02, 95% CI: 0.00, 0.66)被确定为正常变异矮个子的保护因素。本研究发现,父母身高、照顾者教育程度和儿童体重显著影响正常变异身高风险的预测,随机森林模型和梯度增强机器模型在9种机器学习模型中表现出最好的区分能力。结论:本研究揭示了环境生长因素与正常变异矮小的发生密切相关,特别是人体测量特征。随机森林模型和梯度增强机模型表现得非常好,证明了它们在临床应用中的潜力。研究结果为临床诊断和预防矮个子提供了理论依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Endocrine Disorders
BMC Endocrine Disorders ENDOCRINOLOGY & METABOLISM-
CiteScore
4.40
自引率
0.00%
发文量
280
审稿时长
>12 weeks
期刊介绍: BMC Endocrine Disorders is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of endocrine disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
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