Wang Xijia, Zhang Peng, Lu Huifei, Luo Dandan, Yang Dunhui, Li Kang, Qiu Shuqi, Zeng Haotao, Zeng Xianhai
Department of Graduate and Scientific Research, Zunyi Medical University Zhuhai Campus, Zhuhai, Guangdong, People's Republic of China.
Department of Otolaryngology, Longgang E.N.T Hospital & Shenzhen Key Laboratory of E.N.T, Institute of E.N.T Shenzhen, Shenzhen, People's Republic of China.
BMJ Open. 2025 Mar 5;15(3):e088253. doi: 10.1136/bmjopen-2024-088253.
This study aimed to systematically evaluate published predictive models for dental caries in children and adolescents.
A systematic review and meta-analysis of observational studies.
Comprehensive searches were conducted in PubMed, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, Embase, China National Knowledge Infrastructure, Wanfang Database, China Science and Technology Journal Database (VIP) and SinoMed for relevant studies published up to 18 January 2024. The search focused on caries prediction models in children and adolescents.
Eligible studies included observational research (cohort, case-control and cross-sectional designs) that developed risk prediction models for dental caries in children and adolescents aged ≤18 years. Each model was required to include a minimum of two predictors. Studies were excluded if they were not available in English or Chinese, primarily focused on oral microbiome modelling, or lacked essential details regarding study design, model construction or statistical analyses.
A total of 11 studies were included in the review. All models demonstrated a high risk of bias, primarily due to inappropriate statistical methods and unclear applicability resulting from insufficiently detailed presentations of the models. Logistic regression, random forests and support vector machines were the most commonly employed methods. Frequently used predictors included fluoride toothpaste use and brushing frequency. Reported area under the curve (AUC) values ranged from 0.57 to 0.91. A combined predictive model incorporating six caries predictors achieved an AUC of 0.79 (95% CI: 0.73 to 0.84).
Simplified predictive models for childhood caries showed moderate discriminatory performance but exhibited a high risk of bias, as assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Future research should adhere to PROBAST guidelines to minimise bias risk, focus on enhancing model quality, employ rigorous study designs and prioritise external validation to ensure reliable and generalisable clinical predictions.
CRD42024523284.
本研究旨在系统评价已发表的儿童和青少年龋齿预测模型。
对观察性研究进行系统评价和荟萃分析。
在PubMed、科学网、Cochrane图书馆、护理学与健康相关文献累积索引、Embase、中国知网、万方数据库、中国科技期刊数据库（维普）和中国生物医学文献数据库中进行全面检索，以查找截至2024年1月18日发表的相关研究。检索重点为儿童和青少年龋齿预测模型。
符合条件的研究包括针对18岁及以下儿童和青少年龋齿开发风险预测模型的观察性研究（队列研究、病例对照研究和横断面研究设计）。每个模型至少需要包含两个预测因素。如果研究没有英文或中文版本、主要关注口腔微生物组建模、或者缺乏关于研究设计、模型构建或统计分析的基本细节，则将其排除。
本评价共纳入11项研究。所有模型均显示出较高的偏倚风险，主要原因是统计方法不当以及模型呈现不够详细导致适用性不明确。逻辑回归、随机森林和支持向量机是最常用的方法。常用的预测因素包括使用含氟牙膏和刷牙频率。报告的曲线下面积（AUC）值范围为0.57至0.91。一个包含六个龋齿预测因素的联合预测模型的AUC为0.79（95%CI：0.73至0.84）。
使用预测模型偏倚风险评估工具（PROBAST）评估，儿童龋齿简化预测模型显示出中等的区分性能，但存在较高的偏倚风险。未来的研究应遵循PROBAST指南，以尽量减少偏倚风险，专注于提高模型质量，采用严谨的研究设计，并优先进行外部验证，以确保可靠且可推广的临床预测。
PROSPERO注册号：CRD42024523284。
