Departments of Ophthalmology.
Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Tokyo, Japan.
J Glaucoma. 2023 Apr 1;32(4):307-312. doi: 10.1097/IJG.0000000000002152. Epub 2022 Nov 28.
Diagnostic or antiglaucoma drug records in the Japanese claims data showed a high validity in identifying glaucoma patients. Specific subtypes were identified with high specificity and negative predictive values but low sensitivity and positive predictive values.
Despite the widespread use of administrative claims data in epidemiological research on glaucoma, only a few studies have investigated the validity of the methods in defining patients with glaucoma using diagnoses and drug records. We aimed to evaluate the validity of these algorithms in identifying patients with glaucoma using the Japanese claims data.
Two ophthalmologists independently reviewed the medical charts and administrative claims data of 500 randomly selected patients who visited the Department of Ophthalmology of an academic hospital in 2019. We constructed 12 algorithms to identify patients with any type and specific subtypes of glaucoma using the claims records of diagnosis, antiglaucoma drugs, and visual field tests. We regarded the diagnosis of glaucoma based on the medical charts as the reference standard and calculated the sensitivity, specificity, and positive and negative predictive values of each algorithm based on the claims data.
The algorithms of ≥1 diagnostic record per year and ≥1 antiglaucoma drug record per year exhibited sensitivities of 94.6% and 89.2%, respectively, and specificities of 88.9% and 98.3%, respectively. An increase in the frequency of records resulted in a decreased sensitivity and slightly increased specificity. The addition of visual field tests did not improve the validity. The algorithms for specific subtypes of glaucoma exhibited high specificity and relatively low sensitivity.
Diagnostic or antiglaucoma drug records in the Japanese claims data were useful for identifying patients with glaucoma. Researchers should select identification algorithms based on the study design.
日本索赔数据中的诊断或抗青光眼药物记录在识别青光眼患者方面具有较高的有效性。特定的亚型具有较高的特异性和阴性预测值，但敏感性和阳性预测值较低。
尽管在青光眼的流行病学研究中广泛使用了行政索赔数据，但只有少数研究调查了使用诊断和药物记录定义青光眼患者的方法的有效性。我们旨在使用日本索赔数据评估这些算法在识别青光眼患者中的有效性。
两名眼科医生独立审查了 2019 年随机选择的 500 名就诊于学术医院眼科的患者的病历和行政索赔数据。我们使用索赔记录中的诊断、抗青光眼药物和视野检查构建了 12 种算法，以识别任何类型和特定亚型的青光眼患者。我们将病历中基于的青光眼诊断作为参考标准，并根据索赔数据计算每种算法的敏感性、特异性和阳性及阴性预测值。
每年≥1 次诊断记录和每年≥1 次抗青光眼药物记录的算法的敏感性分别为 94.6%和 89.2%，特异性分别为 88.9%和 98.3%。记录频率的增加导致敏感性降低，特异性略有增加。添加视野检查并不能提高有效性。特定类型青光眼的算法特异性高，敏感性相对较低。
日本索赔数据中的诊断或抗青光眼药物记录可用于识别青光眼患者。研究人员应根据研究设计选择识别算法。
