Department of Medicine, Division of Gastroenterology & Hepatology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Pharmacoepidemiol Drug Saf. 2021 Sep;30(9):1168-1174. doi: 10.1002/pds.5291. Epub 2021 May 21.
Population-level studies on the treatment practices and comparative effectiveness of therapies in autoimmune hepatitis (AIH) are lacking due to the absence of validated methods to identify patients with AIH in large databases, such as administrative claims or electronic health records. This study ascertained the performance of International Classification of Diseases (ICD) codes for AIH, and developed and validated a novel algorithm that reliably identifies patients with AIH in health administrative data and claims.
This was a cross-sectional study of patients with ≥1 inpatient or ≥2 outpatient ICD codes for AIH between 2008 and 2019 at a single health system. In a random sample of 250 patients, definite or probable AIH was determined using the Simplified AIH score, Revised AIH score or expert adjudication. The positive predictive value (PPV) was obtained. Variations of this base algorithm were evaluated using additional criteria to increase its performance.
Of the 250 patients, 143 (57.2%) patients had sufficient records available for review. The PPV of the base algorithm was 77.6% (95% CI: 69.9-84.2%). Exclusion of patients with ≥1 ICD code for primary biliary cholangitis or primary sclerosing cholangitis yielded a PPV of 89.7% (95% CI: 82.8-94.6%). Further exclusion of patients with recent immune checkpoint inhibitor therapy increased the PPV to 92.9% (95% CI: 86.5-96.9%).
The use of ICD codes for AIH alone are insufficient to reliably identify patients with AIH in health administrative data and claims. Our proposed algorithm that includes additional diagnostic and medication-related coding criteria demonstrates excellent performance.
由于缺乏验证方法来在大型数据库（如行政索赔或电子健康记录）中识别自身免疫性肝炎（AIH）患者，因此缺乏关于 AIH 治疗实践和治疗效果的人群水平研究。本研究确定了国际疾病分类（ICD）代码用于 AIH 的性能，并开发和验证了一种可靠识别健康行政数据和索赔中 AIH 患者的新算法。
这是一项单中心健康系统 2008 年至 2019 年间至少有 1 次住院或≥2 次门诊 AIH 诊断 ICD 编码的患者的横断面研究。在 250 例患者的随机样本中，使用简化 AIH 评分、修订 AIH 评分或专家裁决确定明确或可能的 AIH。获得阳性预测值（PPV）。使用额外的标准评估该基本算法的变体，以提高其性能。
在 250 例患者中，有 143 例（57.2%）患者有足够的记录可供审查。基本算法的 PPV 为 77.6%（95%CI：69.9-84.2%）。排除至少有 1 个原发性胆汁性胆管炎或原发性硬化性胆管炎 ICD 编码的患者可使 PPV 达到 89.7%（95%CI：82.8-94.6%）。进一步排除近期接受免疫检查点抑制剂治疗的患者可将 PPV 提高至 92.9%（95%CI：86.5-96.9%）。
单独使用 AIH 的 ICD 代码不足以可靠地识别健康行政数据和索赔中的 AIH 患者。我们提出的算法包含了额外的诊断和药物相关编码标准，表现出优异的性能。
