阿尔茨海默病患者的分层使用知识引导的无监督潜伏因子集群与电子健康记录数据
medRxiv : the preprint server for health sciences
|January 7, 2025
概括
预测阿尔茨海默病 (AD) 的结果至关重要. 这项研究将阿尔茨海默病患者分为两组,确定一个具有更高的养老院入院风险但死亡率相似的患者,使得更好的临床管理成为可能.
科学领域:
- 老年学是指老年学的学科.
- 神经学 神经学
- 数据科学数据科学数据科学
背景情况:
- 阿尔茨海默病 (AD) 患者的发展轨迹有很大的不同.
- 预测未来与AD相关的发病率和死亡率对于临床护理至关重要.
- 目前的预测模型在护理点缺乏准确性.
研究的目的:
- 用基线临床资料对阿尔茨海默病 (AD) 患者进行分层.
- 开发一个动态模型,更新诊断后的患者分层.
- 预测未来与AD相关的结果,如养老院入院和死亡率.
主要方法:
- 2011-2022年使用的电子健康记录 (EHR) 数据.
- 应用无监督的表型和潜在因子集群,以知识图嵌入为指导.
- 在诊断时分为两组的16411名AD患者进行了分层分类,并在1年后重新评估.
- 预测的养老院入院和死亡风险,调整为混因素.
主要成果:
- 在AD诊断时确定了两组患者:第一组 (41%) 和第二组 (59%).
- 第1组表现出更高的并发症负担,并接受了更多与AD相关的药物.
- 第二组在基线和1年随访期间,患者入住养老院的风险显著降低.
- 在两个时间点上,两组的死亡风险在两组之间都是可比的.
结论:
- 可用随时可用的临床数据对阿兹海默症患者进行分层.
- 动态模型更新分层1年诊断后有效预测结果.
- 患者分层有助于预测与AD相关的结果,为量身定制的管理和干预提供信息.
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