完善慢性疼痛表型:使用电子健康记录对社会人口统计学和疾病相关决定因素进行比较分析
Tahmina Begum1, Bhagyavalli Veeranki2, Ogenna Joy Chike3
1University of North Carolina, Charlotte, NC, USA.
The journal of pain
|January 5, 2025
概括
慢性疼痛表型的电子健康记录 (EHR) 算法显示出不同的准确性. 需要改进,以改善跨不同患者群体的识别.
科学领域:
- 类风湿病学 类风湿病学
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 电子健康记录 (EHR) 越来越多地用于慢性疼痛表型.
- 电子病历中的结构化数据字段使慢性疼痛表型的标准化捕获成为可能.
- 现有的表型化算法需要验证准确性和通用性.
研究的目的:
- 为了确定慢性疼痛病例定义的准确性,从EHR结构化数据中获得.
- 评估验证指标如何因社会人口统计学和疾病相关因素而有所不同.
主要方法:
- 利用了802名患有自身免疫性类风湿性疾病的成年人的EHR数据.
- 使用结构化数据元素 (疼痛评分,诊断代码,处方,干预) 来导出多个表型算法.
- 通过手动图表审查和评估算法性能 (灵敏度,特异性,PPV) 验证了慢性疼痛病例定义.
主要成果:
- 仅使用ICD代码观察到的最高灵敏度 (67%);使用四模算法观察到的最高特异性 (96%).
- 在男性,年轻患者 (18-40岁),亚洲/太平洋岛民和私人保险人群中,特异性更高.
- 阳性预测值 (PPV) 在女性,年轻人和私人保险患者中最高;在男性,亚洲/太平洋岛民和老年患者中最低.
结论:
- 现型化算法性能的变化凸显了EHR慢性疼痛识别中需要改进的需要.
- 当前的算法提供了洞察力,但需要加强可靠的慢性疼痛识别在不同的人口.
- 性能差异需要为公平的慢性疼痛管理量身定制的算法开发和验证.
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