快速睡眠行为障碍诊断码的准确性和在现实世界中设置的影响
Lana M Chahine1, Deena Ratner1, Aaron Palmquist1
1Departments of Neurology (LMC, DR), and Medicine (AP); Graduate School of Public Health (GD, ABN, CR, MB); and Department of Biomedical Informatics (RDB), University of Pittsburgh, PA.
Neurology. Clinical practice
|November 11, 2024
概括
使用ICD代码对孤立的REM睡眠行为障碍 (iRBD) 的查准确度中等. 患有iRBD的患者面临患上神经退行性帕金森症疾病或痴呆症 (NPD) 的风险明显更高.
科学领域:
- 神经学 神经学
- 睡眠医学 睡眠医学
- 数据科学数据科学数据科学
背景情况:
- 孤立的REM睡眠行为障碍 (iRBD) 是神经退行性疾病 (如帕金森症或痴呆症 (NPD)) 的风险因素.
- 准确的社区查iRBD是一个挑战.
- 电子健康记录 (EHR) 为识别iRBD病例提供了一个潜在的资源.
研究的目的:
- 确定国际疾病分类 (ICD) 对REM睡眠行为障碍 (RBD) 代码的积极预测值 (PPV) 在识别实际iRBD病例时.
- 在iRBD患者中评估后续NPD诊断的风险.
- 探索使用EHR数据开发用于预测iRBD的统计模型的可行性.
主要方法:
- 追溯队列研究分析了2011-2021年EHR数据.
- 对RBD ICD代码患者的EHR手动审查,以确认iRBD并排除次要原因.
- 利用最小绝对收缩和选择运算符 (LASSO) 回归来构建和验证使用EHR数据对iRBD的预测模型.
主要成果:
- 实际iRBD的RBD ICD代码的PPV在排除次要原因后为73.25%.
- 患有iRBD的患者在4.7年的中位数随访期间显示了NPD诊断的显著更高风险 (分发危险比率=10.4) .
- 开发的LASSO预测模型表现出强的性能,接收器操作特征曲线下的面积为0.844.
结论:
- 一个RBD ICD代码具有适度的PPV,用于在现实世界的临床环境中识别iRBD.
- 被诊断为iRBD的患者患NPD的风险大幅增加.
- 使用EHR数据的统计模型可以有效预测iRBD,有助于早期识别和管理.
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