在基于人群的认知衰老队列中,对基于代码的痴呆症算法的性能差距进行表征
Maria Vassilaki1, Sunyang Fu2, Luke R Christenson1
1Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
Journal of Alzheimer's disease : JAD
|August 28, 2023
概括
这项研究评估了使用电子健康记录的痴呆症识别算法. 老年人和轻度认知障碍的人更有可能被算法错误分类.
科学领域:
- 老年学是指老年学的学科.
- 医疗信息学 医疗信息学
- 神经学 神经学
背景情况:
- 电子健康记录 (EHR) 为痴呆症识别算法提供了有价值的数据.
- 使用计费代码和药物数据的现有算法显示了可变的性能.
- 了解错误分类模式可以指导算法改进.
研究的目的:
- 评估一个基于代码的算法在梅奥诊所老化研究 (MCSA) 中对痴呆症诊断的准确性.
- 通过痴呆症检测算法错误分类的个体的特征.
- 将算法性能与基于人口的参考标准进行比较.
主要方法:
- 利用了5,316名没有基线痴呆症的MCSA参与者的数据.
- 从EHR中提取了与痴呆相关的ICD-9/10代码和药物.
- 使用统计测试来比较正确和不正确分类的个体的特征.
主要成果:
- 该算法以0.70的灵敏度和0.95的特异性显示了中等至高的性能.
- 假阳性患者年龄较大,并发病率较高,患轻度认知障碍 (MCI) 的可能性更高.
- 假阴性是年龄较大,更有可能患有MCI或表现出功能限制.
结论:
- 基于代码的痴呆症诊断方法与强大的参考标准相比,显示了中高的准确性.
- 高龄和MCI的存在是与错误分类相关的关键因素.
- 识别错误分类患者的特征有助于改进痴呆症诊断算法.
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