Niznik Joshua D, Lund Jennifer L, Hanson Laura C, Colón-Emeric Cathleen, Kelley Casey J, Gilliam Meredith, Thorpe Carolyn T
Division of Geriatric Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Center for Aging and Health, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
J Am Geriatr Soc. 2024 Aug;72(8):2381-2390. doi: 10.1111/jgs.19019. Epub 2024 May 30.
Gold standard dementia assessments are rarely available in large real-world datasets, leaving researchers to choose among methods with imperfect but acceptable accuracy to identify nursing home (NH) residents with dementia. In healthcare claims, options include claims-based diagnosis algorithms, diagnosis indicators, and cognitive function measures in the Minimum Data Set (MDS), but few studies have compared these. We evaluated the proportion of NH residents identified with possible dementia and concordance of these three.
Using a 20% random sample of 2018-2019 Medicare beneficiaries, we identified MDS admission assessments for non-skilled NH stays among individuals with continuous enrollment in Medicare Parts A, B, and D. Dementia was identified using: (1) Chronic Conditions Warehouse (CCW) claims-based algorithm for Alzheimer's disease and non-Alzheimer's dementia; (2) MDS active diagnosis indicators for Alzheimer's disease and non-Alzheimer's dementias; and (3) the MDS Cognitive Function Scale (CFS) (at least mild cognitive impairment). We compared the proportion of admissions with evidence of possible dementia using each criterion and calculated the sensitivity, specificity, and agreement of the CCW claims definition and MDS indicators for identifying any impairment on the CFS.
Among 346,013 non-SNF NH admissions between 2018 and 2019, 57.2% met criteria for at least one definition (44.7% CFS, 40.7% CCW algorithm, 26.0% MDS indicators). The MDS CFS uniquely identified the greatest proportion with evidence of dementia. The CCW claims algorithm had 63.7% sensitivity and 78.1% specificity for identifying any cognitive impairment on the CFS. Active diagnosis indicators from the MDS had lower sensitivity (47.0%), but higher specificity (91.0%).
Claims- and MDS-based methods for identifying NH residents with possible dementia have only partial overlap in the cohorts they identify, and neither is an obvious gold standard. Future studies should seek to determine whether additional functional assessments from the MDS or prescriptions can improve identification of possible dementia in this population.
在大型真实世界数据集中,很少能获得金标准痴呆评估,这使得研究人员只能在准确性虽不完美但可接受的方法中进行选择,以识别患有痴呆症的养老院(NH)居民。在医疗保健理赔中,可选择的方法包括基于理赔的诊断算法、诊断指标以及最低数据集(MDS)中的认知功能测量,但很少有研究对这些方法进行比较。我们评估了被确定为可能患有痴呆症的NH居民的比例以及这三种方法的一致性。
我们使用2018 - 2019年医疗保险受益人的20%随机样本,确定了在医疗保险A、B和D部分持续参保的个人中,非熟练NH住院的MDS入院评估。使用以下方法确定痴呆症:(1)基于慢性病仓库(CCW)理赔的阿尔茨海默病和非阿尔茨海默病痴呆算法;(2)MDS中阿尔茨海默病和非阿尔茨海默病痴呆的现行诊断指标;(3)MDS认知功能量表(CFS)(至少为轻度认知障碍)。我们比较了使用每种标准有可能患痴呆症证据的入院比例,并计算了CCW理赔定义和MDS指标在识别CFS上任何损伤方面的敏感性、特异性和一致性。
在2018年至2019年期间的346,013例非熟练护理机构NH入院病例中,57.2%符合至少一种定义的标准(44.7%为CFS,40.7%为CCW算法,26.0%为MDS指标)。MDS CFS独特地识别出有痴呆症证据的比例最高。CCW理赔算法在识别CFS上的任何认知损伤方面,敏感性为63.7%,特异性为78.1%。MDS的现行诊断指标敏感性较低(47.0%),但特异性较高(91.0%)。
基于理赔和MDS的识别可能患有痴呆症的NH居民的方法,在它们所识别的队列中只有部分重叠,且两者都不是明显的金标准。未来的研究应设法确定MDS中的额外功能评估或处方是否能改善对该人群中可能患痴呆症的识别。