临床预测模型的实施和更新:系统审查
Alexander Saelmans1, Tom Seinen1, Victor Pera1
1Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
Mayo Clinic proceedings. Digital health
|July 2, 2025
概括
临床上实施的预测模型显示了成功的患者护理改进,尽管许多人都有很高的偏见和缺乏验证. 影响评估和模型更新对于减少偏差和提高临床效用至关重要.
科学领域:
- 临床信息学 临床信息学
- 医疗保健服务研究 医疗服务研究
- 生物统计学 生物统计学
背景情况:
- 预测二进制预测模型越来越多地在临床环境中实施.
- 有效的实施和更新策略对于最大限度地提高模型效用和确保患者安全至关重要.
研究的目的:
- 总结临床使用的预测模型的实施方法和更新方法.
- 为研究人员提供关于模型实施和更新的指导.
主要方法:
- 在Embase,Medline和Web of Science的系统文献搜索 (2010年1月至2024年1月).
- 使用个人预后或诊断 (TRIPOD) 和预测模型偏差风险评估 (PROBAST) 准则的多变量预测模型的透明报告来提取数据.
- 综合了有关实施,验证和更新实践的调查结果.
主要成果:
- 包括37篇描述56个模型的文章; 86%具有偏差的高风险.
- 只有32%的人在开发/内部验证期间进行了校准评估;27%的人接受了外部验证.
- 模型主要应用于医院信息系统 (63%),网络应用程序 (32%) 或患者决策辅助工具 (5%).
- 只有13%的模型在实施后进行了更新.
结论:
- 尽管存在高偏差和低于最佳的验证,但实施的模型证明了对患者护理的成功影响.
- 影响评估和模型更新对于识别和减轻偏见至关重要.
- 需要遵守预测建模的最佳实践,以提高可靠性和临床实用性.
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