行政数据不足以识别近期的危急疾病:基于人口的回顾性队列研究
Allan Garland1, Ruth Ann Marrie1, Hannah Wunsch2
1Department of Medicine, University of Manitoba, Winnipeg, MB, Canada.
Frontiers in epidemiology
|March 8, 2024
概括
使用行政数据预测成年人患重症的结果是不成功的. 需要进行进一步的研究,通过结合额外的数据类型来提高预测准确性,以获得更好的患者结果.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 老年学是一门学科.
背景情况:
- 预测严重疾病可以及时进行干预,以预防或延迟不良事件.
- 在加拿大曼尼托巴省,研究了40-89岁的社区成年人.
- 近期的危急疾病被定义为在30-180天内接受机械通风或非息性死亡的ICU入院.
研究的目的:
- 为了确定在不久的将来患重症的概率大于33%的成年人.
- 用行政数据评估预测模型的有效性.
- 探索社会人口结构,慢性疾病,脆弱性和医疗保健利用对危急疾病风险的影响.
主要方法:
- 使用2013-2015年的数据进行了一项回顾性队列研究.
- 使用分类和回归树 (CART) 分析来识别高风险子组.
- 分析了72个潜在的预测因素,使用后勤回归进行灵敏度分析.
主要成果:
- 大约0.38%的队列在不久的将来经历了严重疾病.
- 卡特模型确定了2,644个子组,社会经济地位,养老院住宿和脆弱是关键预测因素.
- 该模型显示了显著的过度匹配,与训练队列相比,测试队列的表现不佳 (确定子组的结果率为4.7%).
- 后勤回归也未能达到研究的目标.
结论:
- 在当前基于人口的行政数据下,无法实现对社区成年人近期危急疾病的高准确性预测.
- 需要额外的数据源来提高危急疾病预测模型的准确性.
- 进一步的研究对于开发有效的危急疾病预测工具至关重要.
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