在医疗保险索赔和最低数据集中比较痴呆症诊断和认知功能测量
Joshua D Niznik1,2,3,4, Jennifer L Lund5, Laura C Hanson1,2
1Division of Geriatric Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Journal of the American Geriatrics Society
|May 30, 2024
概括
在养老院居民中识别痴呆症是具有挑战性的. 这项研究比较了三种方法:索赔算法,诊断指标和认知功能尺度,发现有限的重叠,并且没有单一的痴呆症识别黄金标准.
科学领域:
- 老年学是一门学科.
- 医疗保健信息学 医疗保健信息学
- 神经学 神经学
背景情况:
- 黄金标准痴呆症评估在大型现实世界数据集中很少.
- 研究人员经常使用不完美的方法来识别养老院 (NH) 居民的痴呆症.
- 现有的方法包括基于索赔的算法,诊断指标和最小数据集 (MDS) 认知功能测量.
研究的目的:
- 用三种不同的方法评估NH居民中可能患有痴呆症的比例.
- 为了比较基于索赔的算法,MDS诊断指标和MDS认知功能尺度 (CFS) 的一致性和准确性.
主要方法:
- 采用了2018-2019年医疗保险受益者20%的样本,用于NH招生.
- 使用慢性病仓库 (CCW) 索赔算法,MDS活跃诊断指标和MDS CFS识别痴呆症.
- 计算了CCW算法和MDS指标对CFS的敏感性,特异性和一致性.
主要成果:
- 通过至少一种方法,超过57%的NH入院符合可能痴呆症的标准.
- 在MDS CFS中,可能患有痴呆症的居民比例最大 (44.7%).
- CCW索赔算法显示了63.7%的灵敏度和78.1%的特异性;MDS指标对CFS损伤具有47.0%的灵敏度和91.0%的特异性.
结论:
- 在NH居民中识别痴呆症的基于索赔和MDS的方法具有有限的重叠.
- 两种方法都不能成为确定该群体痴呆症识别的最终黄金标准.
- 需要进一步的研究,以使用额外的MDS数据或处方信息来加强痴呆症的识别.
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