University of Southern California Keck School of Medicine, Ophthalmology, Los Angeles, CA.
Asia Pac J Ophthalmol (Phila). 2021;10(1):60-62. doi: 10.1097/APO.0000000000000364.
Large administrative health databases, nationwide surveys, and the widespread adoption of electronic medical records have led to an increasing availability of health-related data on ocular inflammatory disease, allowing us to elucidate the real-world epidemiology of uveitis and examine patient and systems-level risk factors for the incidence of specific etiologies of uveitis and its complications. Despite the many advantages to using big databases, there are also limitations that clinicians must be aware of when making conclusions and extrapolating to the general population, such as the lack of standardization of nomenclature and coding. As the availability of even more robust datasets increases, clinicians and scientists should be prepared to leverage these tools to improve our understanding of disease pathophysiology and our ability to manage patients with ocular inflammatory disease.
大型行政健康数据库、全国性调查以及电子病历的广泛采用，使得与眼部炎症性疾病相关的数据越来越多，这使我们能够阐明葡萄膜炎的真实世界流行病学，并研究患者和系统层面上导致葡萄膜炎特定病因及其并发症发生的风险因素。尽管使用大型数据库有许多优势，但在得出结论和推断到一般人群时，临床医生也必须注意到一些局限性，例如缺乏命名和编码的标准化。随着更强大的数据集的可用性的增加，临床医生和科学家应该准备好利用这些工具来提高我们对疾病病理生理学的理解，并提高我们管理眼部炎症性疾病患者的能力。
