PDF(Pubmed)
Abstract：
Lyme disease surveillance based on provider and laboratory reports underestimates incidence. We developed an algorithm for automating surveillance using electronic health record data. We identified potential Lyme disease markers in electronic health record data (laboratory tests, diagnosis codes, prescriptions) from January 2017-December 2018 in 2 large practice groups in Massachusetts, USA. We calculated their sensitivities and positive predictive values (PPV), alone and in combination, relative to medical record review. Sensitivities ranged from 57% (95% CI 47%-69%) for immunoassays to 87% (95% CI 70%-100%) for diagnosis codes. PPVs ranged from 53% (95% CI 43%-61%) for diagnosis codes to 58% (95% CI 50%-66%) for immunoassays. The combination of a diagnosis code and antibiotics within 14 days or a positive Western blot had a sensitivity of 100% (95% CI 86%-100%) and PPV of 82% (95% CI 75%-89%). This algorithm could make Lyme disease surveillance more efficient and consistent.
摘要：
基于提供者和实验室报告的莱姆病监测低估了发病率。我们开发了一种使用电子健康记录数据自动监控的算法。我们在电子健康记录数据中确定了潜在的莱姆病标志物(实验室测试，诊断代码,处方）从2017年1月至2018年12月在马萨诸塞州的2个大型实践小组中，美国。我们计算了它们的灵敏度和阳性预测值(PPV)，单独和组合，相对于病历审查。敏感性范围从免疫测定的57％（95％CI47％-69％）到诊断代码的87％（95％CI70％-100％）。诊断代码的PPV范围从53％（95％CI43％-61％）到免疫测定的58％（95％CI50％-66％）。诊断代码和抗生素在14天内的组合或阳性Westernblot的敏感性为100％（95％CI86％-100％），PPV为82％（95％CI75％-89％）。该算法可以使莱姆病监测更加有效和一致。
