开发和验证风险评分系统,以在电子健康记录数据中识别狼性炎患者的风险
Zara Izadi1, Milena Gianfrancesco2, Christine Anastasiou2
1University of California San Francisco, San Francisco, California, USA zaraizadiucsf@gmail.com.
Lupus science & medicine
|May 20, 2024
概括
精确的狼性炎 (LN) 鉴定至关重要. 新的评分系统改善了电子健康记录 (EHR) 中的LN检测,即使没有特定的诊断代码,也有助于患者管理和研究.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 类风湿病学 类风湿病学
- 医疗信息学 医疗信息学
背景情况:
- 精确识别狼性炎 (LN) 病例对于患者护理,研究和公共卫生至关重要.
- 电子健康记录 (EHR) 往往没有充分利用国际疾病分类 (ICD) 的LN代码,阻碍了有效的病例识别.
- 现有的诊断编码实践对全面确定LN病例提出了挑战.
研究的目的:
- 评估ICD-9/10代码在EHR中识别流行的狼性炎 (LN) 病例方面的表现.
- 开发和验证用于增强LN识别的新型评分系统,适应具有或没有LN特定ICD代码的设置.
主要方法:
- 利用来自两个大型卫生系统的EHR数据,包括结构化字段和风湿病学家手动图表审查,以确定LN病例.
- 开发了两个基于后勤回归的评分系统:"LN-Code" (包含LN ICD代码) 和"LN-No Code" (不包括LN ICD代码).
- 采用渐变增强模型进行预测器选择和使用标准性能指标验证的评分系统,包括跨不同人口的外部验证和校准.
主要成果:
- 在LN诊断码中,灵敏度低 (43-73%),但特异性高 (92-97%).
- "LN-Code"系统实现了包括LN定义的AUC0.93和0.88的灵敏度.
- "LN-No Code"系统表现出强的性能,AUC敏感度为0.91和0.95 (严格定义为0.97),表现出良好的外部有效性和跨种族和民族群体的校准.
结论:
- 量化了LN诊断代码在EHR中的不足使用情况,突出了病例识别中的重大差距.
- 引入了两个可适应的评分系统 ("LN-Code"和"LN-No Code") 以大幅提高电子健康记录中的LN识别准确性.
- 强调需要在各种医疗保健环境中进一步验证这些评分系统,以确保其广泛适用性和影响.
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