丹麦国家登记处的药物耐药的算法
Eva Bølling-Ladegaard1, Julie W Dreier2, Jakob Christensen1,2,3
1Department of Clinical Medicine, Neurology, Aarhus University, 8200 Aarhus, Denmark.
Brain : a journal of neurology
|September 10, 2024
概括
我们开发了一种可靠的算法来识别丹麦卫生登记处的耐药性 (DRE),从而实现大规模研究. 这种算法有助于识别DRE的个体,这些个体面临着严重的健康风险和生活质量下降.
科学领域:
- 神经学 神经学
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
背景情况:
- 耐药性 (DRE) 显著影响患者的生活质量,增加死亡,受伤和心理社会功能障碍的风险.
- 在行政数据中准确识别DRE对于大规模的流行病学研究和公共卫生倡议至关重要.
研究的目的:
- 开发和验证一个强大的算法,用于在丹麦国家卫生登记处识别DRE病例.
- 为了实现对DRE流行,风险因素和结果的高效和大规模研究.
主要方法:
- 用525名经过验证的DRE状态的患者的黄金标准队列来开发和测试基于注册表的算法.
- 基于抗发作药物 (ASM) 处方和医院访问的处方数据,表现最好的算法应用于一个大型的国家队列.
- 验证涉及将算法识别的DRE病例与医疗记录验证的数据进行比较,使用灵敏度,特异性和F1得分等指标.
主要成果:
- 最佳算法实现了0.59的灵敏度,0.93的特异性和0.595.5的F1得分.
- 该算法应用于83,682名患有发作的个人,识别了8650例 (10.3%) 的DRE.
- 早发性,焦点性或泛性,体性并发症和药物滥用被确定为DRE的独立风险因素.
结论:
- 通过使用丹麦国家卫生登记册成功开发了一种有效的DRE识别算法.
- 该工具促进了对DRE的大规模研究,有助于了解其流行病学和相关风险因素.
- 确定的风险因素,如早期发病,类型,并发症和药物滥用等,可以为有针对性的干预和研究提供信息.
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