儿童和青少年预测模型:系统性审查和元分析
Yuan Luo1,2, Xiaoni Chai3, Yunchen Li4,2
1School of Nursing, Capital Medical University, Beijing, 100069, China.
EClinicalMedicine
|November 10, 2025
概括
儿童的预测模型显示中度准确性,临床特征和EEG优于MRI. 大多数研究具有高度偏见,缺乏验证,突出了需要改进方法和外部验证的需要.
科学领域:
- 儿科神经学 儿科神经学
- 医疗信息学 医疗信息学
背景情况:
- 儿童会对认知发展和生活质量产生负面影响.
- 早期风险识别对于改善儿科的结果至关重要.
- 现有的儿科预测模型显示结果不一致,需要进行全面评估.
研究的目的:
- 系统地审查和整合关于儿童和青少年预测模型准确性和有效性的发现.
- 评估当前预测模型的偏差风险和适用性.
- 引导未来的研究,并为儿科的临床策略提供信息.
主要方法:
- 在多个数据库 (PubMed,Embase,CINAHL,Web of Science等) 中进行全面的搜索. 是进行的.
- 用预测模型风险偏差评估工具来评估研究质量.
- 使用随机效应的元分析,将曲线下的面积 (AUC) 聚合为模型性能.
主要成果:
- 包括27项研究,其中25项被确定为具有偏见高风险的研究.
- 训练模型的AUC是0.794,验证模型的AUC是0.726.
- 结合临床特征和EEG的模型表现优于包括MRI的模型;非机器学习模型的表现优于机器学习模型.
结论:
- 目前针对儿童和青少年的预测模型存在重大局限性,包括偏见的高风险和不充分的验证.
- 使用临床特征和EEG模型的模型需要进一步研究.
- 预测因素选择的标准化方法和强大的外部验证对于提高这些模型的临床实用性至关重要.
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