在加拿大三个省份验证被动监控中风严重性得分
Amy Y X Yu1,2, Peter C Austin2, Alison L Park2
1Department of Medicine (Neurology), University of Toronto, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
概括
被动监控中风严重性 (PaSSV) 评分在加拿大不同省份和时间段显示出有效性. 这种基于行政数据的措施对于研究中风结果中的风险调整非常有价值.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 神经学 神经学
- 公共卫生 公共卫生
背景情况:
- 对中风结果的研究需要对中风严重程度进行准确的风险调整.
- 源自行政数据的被动监控中风严重性 (PaSSV) 评分提供了一个潜在的解决方案,在严重性测量通常无法实现的情况下.
- 在不同的地理和时间设置中对PaSSV的外部验证至关重要.
研究的目的:
- 评估PaSSV评分的地理和时间外部有效性.
- 评估PaSSV得分在风险调整中风结果研究中的有用性.
主要方法:
- 利用了来自不列颠哥伦比亚省,新斯科舍省和安大略省 (2014-2019) 的成年缺血性中风和脑内出血患者的相关行政数据.
- 计算了PaSSV得分,并使用了Cox比例危险模型来评估与30天死亡率和365天长期护理入院的关联.
- 使用Uno的c统计数据评估模型的区分能力,比较PaSSV和没有PaSSV的模型.
主要成果:
- 包括三个省份的86,142名患者;PaSSV得分是可比的.
- 较高的PaSSV分数 (表明中风严重程度较低) 与所有省份的死亡和长期护理入院风险降低显著相关.
- 将PaSSV纳入多变量模型显然提高了预测准确性 (增加了c统计).
结论:
- 该PaSSV评分表现出强大的地理和时间有效性.
- PaSSV是中风结果研究中的风险调整的一个有价值的工具,支持多管辖区分析.
- 这些发现支持PaSSV在评估中风护理质量和结果方面的更广泛应用.
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