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}
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.
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.