Development and validation of a novel public prediction platform for deciduous caries in preschool children: an observational study from Northwest China

Abstract Background Early childhood caries (ECC) is a major global public health concern, necessitating its early screening. This study aimed to establish a caries risk assessment (CRA) platform for managing caries in community preschool children in underdeveloped regions of Northwest China. Methods...

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Main Authors: Zhidong Zhang, Zhihao Dou, Xu Zhang, Di Wang, Kamenjiang Abudoureheman, Yimin Yan, Fenyan Kang, Wei Zhang, Wenbo Meng, Kailiang Zhang, Zhige Li, Jie Zhang, Bin Liu, Baoping Zhang
Format: Article
Language:English
Published: BMC 2025-08-01
Series:BMC Pediatrics
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Online Access:https://doi.org/10.1186/s12887-025-05763-w
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author Zhidong Zhang
Zhihao Dou
Xu Zhang
Di Wang
Kamenjiang Abudoureheman
Yimin Yan
Fenyan Kang
Wei Zhang
Wenbo Meng
Kailiang Zhang
Zhige Li
Jie Zhang
Bin Liu
Baoping Zhang
author_facet Zhidong Zhang
Zhihao Dou
Xu Zhang
Di Wang
Kamenjiang Abudoureheman
Yimin Yan
Fenyan Kang
Wei Zhang
Wenbo Meng
Kailiang Zhang
Zhige Li
Jie Zhang
Bin Liu
Baoping Zhang
author_sort Zhidong Zhang
collection DOAJ
description Abstract Background Early childhood caries (ECC) is a major global public health concern, necessitating its early screening. This study aimed to establish a caries risk assessment (CRA) platform for managing caries in community preschool children in underdeveloped regions of Northwest China. Methods We collected clinical examination and questionnaire data of children aged 3 to 5 years in six regions of Gansu Province. Then we selected variables using least absolute shrinkage and selection operator (LASSO) regression and constructed a CRA model utilizing multivariate logistic regression analyses. The predictive performance was assessed by the receiver operating characteristic (ROC), calibration, clinical decision and impact curves. The subgroup application was analyzed on the basis of the residence of children. Results The CRA model included age, residence, feeding pattern within six months of birth, history of toothache, and history of dental visits as predictors. The Hosmer–Lemeshow test showed that the model fitting was acceptable (χ2 = 7.049, P = 0.531). And the model exhibited an excellent discriminatory performance in external cohort, as evidenced by the ROC curve parameters, with an area under the ROC curve (AUC) of 0.804 (95% CI: 0.765–0.844), sensitivity of 0.807, and specificity of 0.660. The calibration curves showed that the model exhibited good predictive accuracy, and the clinical decision and impact curves showed that the model was useful within reasonable threshold probabilities. Finally, we created an online prediction platform to ensure public use of the CRA model. Conclusions The presented CRA model offers a novel public platform with the potential to serve as an effective tool for the screening and management of deciduous caries at the community level in underdeveloped regions of Northwest China.
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spelling doaj-art-ba3b530eacf74ca0af6f061b3b3202d52025-08-20T04:02:50ZengBMCBMC Pediatrics1471-24312025-08-0125111210.1186/s12887-025-05763-wDevelopment and validation of a novel public prediction platform for deciduous caries in preschool children: an observational study from Northwest ChinaZhidong Zhang0Zhihao Dou1Xu Zhang2Di Wang3Kamenjiang Abudoureheman4Yimin Yan5Fenyan Kang6Wei Zhang7Wenbo Meng8Kailiang Zhang9Zhige Li10Jie Zhang11Bin Liu12Baoping Zhang13School (Hospital) of Stomatology, Lanzhou UniversitySchool (Hospital) of Stomatology, Lanzhou UniversityEndoscopic Diagnosis and Treatment Center, Gansu Province HospitalSchool of Stomatology, Xi’an Jiaotong UniversitySchool (Hospital) of Stomatology, Lanzhou UniversitySchool (Hospital) of Stomatology, Lanzhou UniversityGansu Provincial Center for Disease Control and PreventionThe Center of Health Management, The Second Hospital of Lanzhou UniversityThe Department of General Surgery, The First Hospital of Lanzhou UniversitySchool (Hospital) of Stomatology, Lanzhou UniversitySchool (Hospital) of Stomatology, Lanzhou UniversitySchool (Hospital) of Stomatology, Lanzhou UniversitySchool (Hospital) of Stomatology, Lanzhou UniversitySchool (Hospital) of Stomatology, Lanzhou UniversityAbstract Background Early childhood caries (ECC) is a major global public health concern, necessitating its early screening. This study aimed to establish a caries risk assessment (CRA) platform for managing caries in community preschool children in underdeveloped regions of Northwest China. Methods We collected clinical examination and questionnaire data of children aged 3 to 5 years in six regions of Gansu Province. Then we selected variables using least absolute shrinkage and selection operator (LASSO) regression and constructed a CRA model utilizing multivariate logistic regression analyses. The predictive performance was assessed by the receiver operating characteristic (ROC), calibration, clinical decision and impact curves. The subgroup application was analyzed on the basis of the residence of children. Results The CRA model included age, residence, feeding pattern within six months of birth, history of toothache, and history of dental visits as predictors. The Hosmer–Lemeshow test showed that the model fitting was acceptable (χ2 = 7.049, P = 0.531). And the model exhibited an excellent discriminatory performance in external cohort, as evidenced by the ROC curve parameters, with an area under the ROC curve (AUC) of 0.804 (95% CI: 0.765–0.844), sensitivity of 0.807, and specificity of 0.660. The calibration curves showed that the model exhibited good predictive accuracy, and the clinical decision and impact curves showed that the model was useful within reasonable threshold probabilities. Finally, we created an online prediction platform to ensure public use of the CRA model. Conclusions The presented CRA model offers a novel public platform with the potential to serve as an effective tool for the screening and management of deciduous caries at the community level in underdeveloped regions of Northwest China.https://doi.org/10.1186/s12887-025-05763-wCaries risk assessmentClinical prediction modelDeciduous cariesMachine learning
spellingShingle Zhidong Zhang
Zhihao Dou
Xu Zhang
Di Wang
Kamenjiang Abudoureheman
Yimin Yan
Fenyan Kang
Wei Zhang
Wenbo Meng
Kailiang Zhang
Zhige Li
Jie Zhang
Bin Liu
Baoping Zhang
Development and validation of a novel public prediction platform for deciduous caries in preschool children: an observational study from Northwest China
BMC Pediatrics
Caries risk assessment
Clinical prediction model
Deciduous caries
Machine learning
title Development and validation of a novel public prediction platform for deciduous caries in preschool children: an observational study from Northwest China
title_full Development and validation of a novel public prediction platform for deciduous caries in preschool children: an observational study from Northwest China
title_fullStr Development and validation of a novel public prediction platform for deciduous caries in preschool children: an observational study from Northwest China
title_full_unstemmed Development and validation of a novel public prediction platform for deciduous caries in preschool children: an observational study from Northwest China
title_short Development and validation of a novel public prediction platform for deciduous caries in preschool children: an observational study from Northwest China
title_sort development and validation of a novel public prediction platform for deciduous caries in preschool children an observational study from northwest china
topic Caries risk assessment
Clinical prediction model
Deciduous caries
Machine learning
url https://doi.org/10.1186/s12887-025-05763-w
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