在退伍军人卫生管理局优化药物耐药性的识别
Zulfi Haneef1, Stephan Eisenschenk2, Maria R Lopez3
1Epilepsy Centers of Excellence. Veterans Administration Central Office, Washington, DC, United States; Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States; Baylor College of Medicine, Houston, TX 77030, United States.
Epilepsy research
|April 29, 2025
概括
准确识别耐药性 (DRE) 是至关重要的. 使用抗发作药物 (ASM) 和ICD代码的数量和持续时间的算法在VHA数据中实现了高精度.
科学领域:
- 神经学 神经学
- 医疗信息学 医疗信息学
- 临床研究 临床研究
背景情况:
- 准确识别耐药性 (DRE) 对于患者护理和研究至关重要.
- 以往使用2010年国际联盟 (ILAE) 标准在行政数据中定义DRE的方法很复杂.
- 开发可靠的DRE识别方法对于改善患者的治疗结果至关重要.
研究的目的:
- 开发和验证用于在行政数据中识别DRE的算法.
- 在大型患者队列中提高DRE测量的准确性.
- 完善管理的临床干预措施.
主要方法:
- 退伍军人卫生管理局 (VHA) 国家行政数据的回顾性分析.
- 病学家使用2010年ILAE标准进行图表审查,以确认DRE.
- 后勤回归分析用于开发和优化DRE识别算法.
主要成果:
- 一个最佳的算法根据抗药物 (ASM) 的数量和持续时间以及难治的ICD代码来确定DRE.
- 最好的算法实现了高精度 (F1得分=0.726),灵敏度 (0.74),特异性 (0.81),以及AUC (0.78).
- 与DRE相关的因素包括年龄,ASM使用,EEG/MRI程序和难治的代码.
结论:
- 开发的算法有效地识别行政数据中的DRE,类似于之前的工作,但具有独特的因素组合.
- 针对特定医疗保健环境的微调算法对于最佳性能至关重要.
- 为了评估算法的更广泛的适用性,需要在不同的队列中进行进一步的验证.
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