在部署临床预测模型时,应保留持续预测的风险
Robin Blythe1, Rex Parsons2, Marcus Eh Ong3
1Programme in Health Services Research & Population Health, Duke-NUS Medical School, Singapore.
Journal of clinical epidemiology
|October 8, 2025
概括
使用连续的风险评分,而不仅仅是风险组,可以提高患者优先级和医疗保健环境中的经济价值. 根据预测风险对患者进行排名,可以带来显著的好处,尤其是在资源限制的情况下.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健服务研究 医疗服务研究
- 临床决策支持 临床决策支持
背景情况:
- 临床预测模型经常将概率分为风险组,可能会丢失有价值的信息.
- 使用连续风险预测与离散风险组的经济影响尚不清楚.
研究的目的:
- 为了评估通过持续预测风险对患者的排名的影响,与仅使用风险组相比.
- 在不同的条件下评估持续风险预测的经济价值和绩效效益.
主要方法:
- 模拟场景与不同的模型歧视和事件流行率.
- 使用正的预测值,灵敏度和真正的平均等级来评估性能.
- 将发现应用于基于机器学习的顺序分数系统,使用真实紧急部门的数据.
主要成果:
- 根据预测风险对患者进行排名,比单独使用风险组更有显著的绩效益.
- 福利随着更高的模型歧视和结果流行率而增加,并且对糟糕的校准有很强的抵抗力.
- 对新加坡急救部门数据的分析显示,在资源限制较大的情况下,获益最大.
结论:
- 在风险组内使用连续概率优先考虑患者提供了潜在的经济优势.
- 未来的预测模型应该为持续的风险得分提供方程,以便更好地确定患者的优先级.
- 建议在部署的模型中将持续风险得分与临床判断相结合.
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