开发和验证一个模型,使用卫生行政数据对心血管死亡原因进行分类
Sagar Patel1, Wade Thompson2,3,4,5, Atul Sivaswamy3
1Faculty of Medicine, University of Toronto, Toronto, Canada.
概括
这项研究开发了一种使用卫生行政数据的模型,以确定心血管死亡原因. 该模型显示了在大量人群中对心血管死亡进行分类的潜力.
科学领域:
- 医疗信息学 医疗信息学
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 对心血管 (CV) 死亡原因 (COD) 的准确分类对于公共卫生监测和研究至关重要.
- 常规收集的卫生行政数据为流行病学研究提供了宝贵的资源,但需要强大的方法来对COD进行分类.
研究的目的:
- 开发和评估一个预测模型,使用卫生行政数据来分类心血管 (CV) 死亡原因 (COD).
- 评估模型在区分CV和非CV死亡的表现,使用大量基于人口的队列.
主要方法:
- 使用来自加拿大安大略省的CANHEART队列 (2008-2015) 的卫生行政数据开发了一个后勤回归模型.
- 该模型利用例行收集的医疗管理数据来预测心血管疾病,ICD-10代码作为黄金标准.
- 模型的区分和校准被评估为导出和验证队列,包括40岁以上的死者.
主要成果:
- 心血管疾病的最强预测因素包括中风,心肌梗塞,心力衰竭和最近的心血管住院.
- 在验证队列中,该模型实现了0.80的c统计值,CV COD的灵敏度为0.75和特异性为0.71.
- 在各个子队列中,表现各不相同,在二次预防中敏感度更高,在初级预防组中特异性更高.
结论:
- 使用卫生行政数据的建模方法表明,对对心血管死亡原因进行分类的巨大潜力.
- 在广泛的临床或研究应用之前,需要进一步的研究和验证.
- 这种方法可以加强流行病学监测和研究心血管死亡率.
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