发育协调障碍早期筛查的生物生态模型。

IF 4.3 2区 医学 Q1 CLINICAL NEUROLOGY
Xiaotian Dai, Tai Ren, Gareth Williams, Gary Jones, Fei Li, Wenchong Du, Jing Hua
{"title":"发育协调障碍早期筛查的生物生态模型。","authors":"Xiaotian Dai, Tai Ren, Gareth Williams, Gary Jones, Fei Li, Wenchong Du, Jing Hua","doi":"10.1111/dmcn.70000","DOIUrl":null,"url":null,"abstract":"<p><strong>Aim: </strong>To develop and externally validate a bio-ecological model for early screening of developmental coordination disorder (DCD) using maternal and environmental risk factors from electronic health records, aimed at improving early detection in children under 5 years.</p><p><strong>Method: </strong>This was a prospective study that examined data from 150 948 preschool children in China. Perinatal and sociodemographic predictors were integrated using logistic regression and random forest algorithms. The model was internally validated on split training and testing subsets and externally validated on an independent clinical sample of 1359 children aged 3 to 10 years, including confirmed diagnoses of DCD. Model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy.</p><p><strong>Results: </strong>In the group aged 3 to 5 years, the model achieved an AUC of 0.70, sensitivity of 71.43%, accuracy of 77.61%, and specificity of 78.00%. In the group aged 6 to 10 years, performance was moderate (AUC = 0.58; sensitivity = 54.88%; accuracy = 61.50%; specificity = 62.28%).</p><p><strong>Interpretation: </strong>This bio-ecological model offers a scalable, cost-effective tool to support the early identification of DCD using electronic health record data. It performs well in early childhood and maintains moderate accuracy in older children, supporting its utility for longer-term risk prediction. The model could enhance existing screening systems by enabling earlier triage and intervention. Further validation across diverse health care settings is warranted.</p>","PeriodicalId":50587,"journal":{"name":"Developmental Medicine and Child Neurology","volume":" ","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A bio-ecological model for early screening of developmental coordination disorder.\",\"authors\":\"Xiaotian Dai, Tai Ren, Gareth Williams, Gary Jones, Fei Li, Wenchong Du, Jing Hua\",\"doi\":\"10.1111/dmcn.70000\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aim: </strong>To develop and externally validate a bio-ecological model for early screening of developmental coordination disorder (DCD) using maternal and environmental risk factors from electronic health records, aimed at improving early detection in children under 5 years.</p><p><strong>Method: </strong>This was a prospective study that examined data from 150 948 preschool children in China. Perinatal and sociodemographic predictors were integrated using logistic regression and random forest algorithms. The model was internally validated on split training and testing subsets and externally validated on an independent clinical sample of 1359 children aged 3 to 10 years, including confirmed diagnoses of DCD. Model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy.</p><p><strong>Results: </strong>In the group aged 3 to 5 years, the model achieved an AUC of 0.70, sensitivity of 71.43%, accuracy of 77.61%, and specificity of 78.00%. In the group aged 6 to 10 years, performance was moderate (AUC = 0.58; sensitivity = 54.88%; accuracy = 61.50%; specificity = 62.28%).</p><p><strong>Interpretation: </strong>This bio-ecological model offers a scalable, cost-effective tool to support the early identification of DCD using electronic health record data. It performs well in early childhood and maintains moderate accuracy in older children, supporting its utility for longer-term risk prediction. The model could enhance existing screening systems by enabling earlier triage and intervention. Further validation across diverse health care settings is warranted.</p>\",\"PeriodicalId\":50587,\"journal\":{\"name\":\"Developmental Medicine and Child Neurology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Developmental Medicine and Child Neurology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/dmcn.70000\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Developmental Medicine and Child Neurology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/dmcn.70000","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0

摘要

目的:利用电子健康记录中的孕产妇和环境风险因素,开发并外部验证发育协调障碍(DCD)早期筛查的生物生态模型,旨在提高5岁以下儿童的早期发现。方法:这是一项前瞻性研究,调查了中国150948名学龄前儿童的数据。使用逻辑回归和随机森林算法整合围产期和社会人口学预测因子。该模型在分裂训练和测试子集上进行了内部验证,并在1359名3至10岁儿童的独立临床样本上进行了外部验证,其中包括确诊的DCD。使用曲线下面积(AUC)、敏感性、特异性和准确性来评估模型的性能。结果:在3 ~ 5岁年龄组中,模型的AUC为0.70,灵敏度为71.43%,准确率为77.61%,特异性为78.00%。6 ~ 10岁组表现中等(AUC = 0.58,灵敏度= 54.88%,准确度= 61.50%,特异性= 62.28%)。解释:这种生物生态模型提供了一种可扩展的、具有成本效益的工具,可以使用电子健康记录数据支持DCD的早期识别。它在儿童早期表现良好,在较大的儿童中保持适度的准确性,支持其用于长期风险预测。该模型可以通过早期分类和干预来加强现有的筛查系统。有必要在不同的卫生保健环境中进一步验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A bio-ecological model for early screening of developmental coordination disorder.

Aim: To develop and externally validate a bio-ecological model for early screening of developmental coordination disorder (DCD) using maternal and environmental risk factors from electronic health records, aimed at improving early detection in children under 5 years.

Method: This was a prospective study that examined data from 150 948 preschool children in China. Perinatal and sociodemographic predictors were integrated using logistic regression and random forest algorithms. The model was internally validated on split training and testing subsets and externally validated on an independent clinical sample of 1359 children aged 3 to 10 years, including confirmed diagnoses of DCD. Model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy.

Results: In the group aged 3 to 5 years, the model achieved an AUC of 0.70, sensitivity of 71.43%, accuracy of 77.61%, and specificity of 78.00%. In the group aged 6 to 10 years, performance was moderate (AUC = 0.58; sensitivity = 54.88%; accuracy = 61.50%; specificity = 62.28%).

Interpretation: This bio-ecological model offers a scalable, cost-effective tool to support the early identification of DCD using electronic health record data. It performs well in early childhood and maintains moderate accuracy in older children, supporting its utility for longer-term risk prediction. The model could enhance existing screening systems by enabling earlier triage and intervention. Further validation across diverse health care settings is warranted.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
7.80
自引率
13.20%
发文量
338
审稿时长
3-6 weeks
期刊介绍: Wiley-Blackwell is pleased to publish Developmental Medicine & Child Neurology (DMCN), a Mac Keith Press publication and official journal of the American Academy for Cerebral Palsy and Developmental Medicine (AACPDM) and the British Paediatric Neurology Association (BPNA). For over 50 years, DMCN has defined the field of paediatric neurology and neurodisability and is one of the world’s leading journals in the whole field of paediatrics. DMCN disseminates a range of information worldwide to improve the lives of disabled children and their families. The high quality of published articles is maintained by expert review, including independent statistical assessment, before acceptance.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信