ILD management and monitoring.

Background
Epidemiological studies of interstitial lung disease (ILD) are limited by small numbers and tertiary care bias. Investigators have leveraged the widespread use of electronic health records (EHRs) to overcome these limitations, but struggle to extract patient-level, longitudinal clinical data needed to address many important research questions. We hypothesized that we could automate longitudinal ILD cohort development using the EHR of a large, community-based healthcare system.
Study design and methods
We applied a previously validated algorithm to the EHR of a community-based healthcare system to identify ILD cases between 2012–2020. We then extracted disease-specific characteristics and outcomes using fully automated data-extraction algorithms and natural language processing of selected free-text.
Results
We identified a community cohort of 5,399 ILD patients (prevalence = 118 per 100,000). Pulmonary function tests (71%) and serologies (54%) were commonly used in the diagnostic evaluation, whereas lung biopsy was rare (5%). IPF was the most common ILD diagnosis (n = 972, 18%). Prednisone was the most commonly prescribed medication (911, 17%). Nintedanib and pirfenidone were rarely prescribed (n = 305, 5%). ILD patients were high-utilizers of inpatient (40%/year hospitalized) and outpatient care (80%/year with pulmonary visit), with sustained utilization throughout the post-diagnosis study period.
Discussion
We demonstrated the feasibility of robustly characterizing a variety of patient-level utilization and health services outcomes in a community-based EHR cohort. This represents a substantial methodological improvement by alleviating traditional constraints on the accuracy and clinical resolution of such ILD cohorts; we believe this approach will make community-based ILD research more efficient, effective, and scalable.

Background
间质性肺疾病（interstitial lung disease, ILD）的流行病学研究常受限于样本量偏小与三级诊疗偏倚。研究人员借助电子健康档案（electronic health records, EHRs）的广泛应用以克服上述局限，但在提取用于解答诸多关键研究问题所需的患者级纵向临床数据时仍面临挑战。本研究假设，可依托大型社区医疗系统的电子健康档案，自动化构建间质性肺疾病队列。

Study design and methods
研究设计与方法
本研究将一款经预先验证的算法应用于某社区医疗系统的电子健康档案，以识别2012年至2020年间的间质性肺疾病病例。随后，通过全自动数据提取算法与针对选定自由文本的自然语言处理技术，提取疾病特异性特征与临床转归数据。

Results
研究结果
本研究共纳入5399名间质性肺疾病患者组成社区队列，患病率为每10万人118例。肺功能检查（占比71%）与血清学检测（占比54%）是诊断评估中的常用手段，而肺活检的应用比例极低（仅5%）。特发性肺纤维化（idiopathic pulmonary fibrosis, IPF）是最常见的间质性肺疾病诊断类型（n=972，占比18%）。泼尼松是最常开具的药物（n=911，占比17%）。尼达尼布（nintedanib）与吡非尼酮（pirfenidone）的开具比例极低（n=305，占比5%）。间质性肺疾病患者的医疗资源使用率较高：每年有40%的患者住院，80%的患者接受门诊肺病随访，且在诊断后的整个研究期间持续保持较高的医疗资源使用频率。

Discussion
讨论
本研究证实，依托社区电子健康档案队列，可高效精准地刻画各类患者级医疗资源使用与卫生服务转归特征。该研究方法显著改进了既往研究的局限性，解决了传统间质性肺疾病队列研究在准确性与临床分辨率上的短板；我们认为，此方法可使社区层面的间质性肺疾病研究更高效、更具效力且更具可扩展性。