Division of Liver Diseases, Icahn School of Medicine Mount Sinai, New York, NY.
Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, MI.
Hepatology. 2021 Dec;74(6):2974-2987. doi: 10.1002/hep.32086.
Although chronic HCV infection increases mortality, thousands of patients remain diagnosed-but-untreated (DBU). We aimed to (1) develop a DBU phenotyping algorithm, (2) use it to facilitate case finding and linkage to care, and (3) identify barriers to successful treatment.
We developed a phenotyping algorithm using Java and SQL and applied it to ~2.5 million EPIC electronic medical records (EMRs; data entered January 2003 to December 2017). Approximately 72,000 EMRs contained an HCV International Classification of Diseases code and/or diagnostic test. The algorithm classified 10,614 cases as DBU (HCV-RNA positive and alive). Its positive and negative predictive values were 88% and 97%, respectively, as determined by manual review of 500 EMRs randomly selected from the ~72,000. Navigators reviewed the charts of 6,187 algorithm-defined DBUs and they attempted to contact potential treatment candidates by phone. By June 2020, 30% (n = 1,862) had completed an HCV-related appointment. Outcomes analysis revealed that DBU patients enrolled in our care coordination program were more likely to complete treatment (72% [n = 219] vs. 54% [n = 256]; P < 0.001) and to have a verified sustained virological response (67% vs. 46%; P < 0.001) than other patients. Forty-eight percent (n = 2,992) of DBU patients could not be reached by phone, which was a major barrier to engagement. Nearly half of these patients had Fibrosis-4 scores ≥ 2.67, indicating significant fibrosis. Multivariable logistic regression showed that DBUs who could not be contacted were less likely to have private insurance than those who could (18% vs. 50%; P < 0.001).
The digital DBU case-finding algorithm efficiently identified potential HCV treatment candidates, freeing resources for navigation and coordination. The algorithm is portable and accelerated HCV elimination when incorporated in our comprehensive program.
虽然慢性 HCV 感染会增加死亡率，但仍有数千名患者被诊断但未接受治疗（DBU）。我们的目的是：（1）开发一种 DBU 表型算法，（2）使用它来促进病例发现和联系护理，以及（3）确定成功治疗的障碍。
我们使用 Java 和 SQL 开发了一种表型算法，并将其应用于大约 250 万份 EPIC 电子病历（EMR；数据输入时间为 2003 年 1 月至 2017 年 12 月）。大约有 72000 份 EMR 包含 HCV 国际疾病分类代码和/或诊断测试。该算法将 10614 例归类为 DBU（HCV-RNA 阳性且存活）。通过随机选择 500 份 EMR 进行手动审查，该算法的阳性预测值和阴性预测值分别为 88%和 97%。导航员审查了算法定义的 6187 例 DBU 的图表，并试图通过电话联系潜在的治疗候选人。截至 2020 年 6 月，30%（n=1862）已完成与 HCV 相关的预约。结果分析表明，参加我们护理协调计划的 DBU 患者更有可能完成治疗（72%[n=219]与 54%[n=256]；P<0.001），并且更有可能获得验证的持续病毒学应答（67%与 46%；P<0.001）。48%（n=2992）的 DBU 患者无法通过电话联系，这是参与的主要障碍。这些患者中有近一半的 Fibrosis-4 评分≥2.67，表明存在显著纤维化。多变量逻辑回归显示，无法联系到的 DBU 患者拥有私人保险的可能性低于能够联系到的患者（18%与 50%；P<0.001）。
数字 DBU 病例发现算法有效地确定了潜在的 HCV 治疗候选人，为导航和协调腾出了资源。该算法具有便携性，并且在纳入我们的综合计划时加速了 HCV 的消除。
