阿尔茨海默病及相关痴呆症理赔算法验证。
Validation of Claims Algorithms to Identify Alzheimer's Disease and Related Dementias.
机构信息
Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA.
Division of Gerontology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
出版信息
J Gerontol A Biol Sci Med Sci. 2022 Jun 1;77(6):1261-1271. doi: 10.1093/gerona/glab373.
BACKGROUND
Using billing data generated through health care delivery to identify individuals with dementia has become important in research. To inform tradeoffs between approaches, we tested the validity of different Medicare claims-based algorithms.
METHODS
We included 5 784 Medicare-enrolled, Health and Retirement Study participants aged older than 65 years in 2012 clinically assessed for cognitive status over multiple waves and determined performance characteristics of different claims-based algorithms.
RESULTS
Positive predictive value (PPV) of claims ranged from 53.8% to 70.3% and was highest using a revised algorithm and 1 year of observation. The tradeoff of greater PPV was lower sensitivity; sensitivity could be maximized using 3 years of observation. All algorithms had low sensitivity (31.3%-56.8%) and high specificity (92.3%-98.0%). Algorithm test performance varied by participant characteristics, including age and race.
CONCLUSION
Revised algorithms for dementia diagnosis using Medicare administrative data have reasonable accuracy for research purposes, but investigators should be cognizant of the tradeoffs in accuracy among the approaches they consider.
背景
利用医疗保健提供过程中产生的计费数据来识别痴呆症患者在研究中变得越来越重要。为了了解不同方法之间的权衡取舍，我们测试了不同基于医疗保险索赔的算法的有效性。
方法
我们纳入了 5784 名年龄在 65 岁以上的 Medicare 参保者，他们在 2012 年接受了多次认知状态评估，并使用不同的基于医疗保险索赔的算法来确定其性能特征。
结果
索赔的阳性预测值（PPV）范围为 53.8%至 70.3%，使用修订后的算法和 1 年的观察期时最高。更高的 PPV 意味着敏感性降低；使用 3 年的观察期可以使敏感性最大化。所有算法的敏感性（31.3%-56.8%）均较低，特异性（92.3%-98.0%）均较高。算法的测试性能因参与者的特征（包括年龄和种族）而异。
结论
使用医疗保险管理数据进行痴呆症诊断的修订算法在研究目的上具有合理的准确性，但研究人员应意识到他们所考虑的方法在准确性方面存在权衡取舍。
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