作为一种动态疾病:朝着可操作的,个性化的发作风险预测
Kai Michael Schubert1, Anthony G Marson2,3, Eugen Trinka4,5,6
1Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.
的诊断依赖于长期的风险,但短期的动态工具更好地反映了患者的需求. 这些工具可以改善复发的沟通和治疗决策.
科学领域:
- 神经学 神经学
- 临床流行病学临床流行病学
背景情况:
- 目前的病诊断使用了一个静态的10年风险值 (≥60%) 在一次发作后.
- 这种长期观点与临床决策和患者的时间表相冲突,特别是在获得性中,复发风险在前1-2年是最高的.
研究的目的:
- 评估动态,时间敏感的风险预测工具对于的诊断和管理的有用性.
- 探索这些工具如何更好地与临床实践和患者需求保持一致.
主要方法:
- 审查现有的预后模型 (例如,COSY,SELECT,CAVE,RISE) 以预测发作复发.
- 在获得性中复发的时间模式的分析.
- 讨论将动态风险预测纳入临床指南中的障碍.
主要成果:
- 动态工具提供可重新计算的近期发作风险估计,反映患者状况的变化.
- 这些工具可以增强沟通,告知治疗值 (NNT),并改善临床试验招聘.
- 障碍包括有限的指导方针集成,验证差距和"奥迪普斯效应".
结论:
- 个性化,时间敏感的风险预测可以优化的护理和研究.
- 采用动态模型可以减少过度治疗,更好地满足患者的需求.
- 这些工具的进一步整合和验证对于广泛的临床采用是必要的.
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