Performance of a Computational Phenotyping Algorithm for Sarcoidosis Using Diagnostic Codes in Electronic Medical Records: Case Validation Study From 2 Veterans Affairs Medical Centers

Utilization of Computable Phenotypes in Electronic Health Record Research: A Review and Case Study in Atopic Dermatitis

Querying electronic health records databases to accurately identify specific cohorts of patients has countless observational and interventional research applications. Computable phenotypes are computationally executable, explicit sets of selection criteria composed of data elements, logical expressions, and a combination of natural language processing and machine learning techniques enabling expedited patient cohort identification. Phenotyping encompasses a range of implementations, each with advantages and use cases. In this paper, the dermatologic computable phenotype literature is reviewed. We identify and evaluate approaches and community supports for computable phenotyping that have been used both generally and within dermatology and, as a case study, focus on studied phenotypes for atopic dermatitis.

Big data in sarcoidosis

PURPOSE OF REVIEW

This review provides an overview of recent advancements in sarcoidosis research, focusing on collaborative networks, phenotype characterization, and molecular studies. It highlights the importance of collaborative efforts, phenotype characterization, and the integration of multilevel molecular data for advancing sarcoidosis research and paving the way toward personalized medicine.

RECENT FINDINGS

Sarcoidosis exhibits heterogeneous clinical manifestations influenced by various factors. Efforts to define sarcoidosis endophenotypes show promise, while technological advancements enable extensive molecular data generation. Collaborative networks and biobanks facilitate large-scale studies, enhancing biomarker discovery and therapeutic protocols.

SUMMARY

Sarcoidosis presents a complex challenge due to its unknown cause and heterogeneous clinical manifestations. Collaborative networks, comprehensive phenotype delineation, and the utilization of cutting-edge technologies are essential for advancing our understanding of sarcoidosis biology and developing personalized medicine approaches. Leveraging large-scale epidemiological resources and biobanks and integrating multilevel molecular data offer promising avenues for unraveling the disease's heterogeneity and improving patient outcomes.

Epidemiology of Sarcoidosis in U.S. Veterans from 2003 to 2019

Rationale: United States veterans represent an important population to study sarcoidosis. Their unique history of environmental exposures, wide geographic distribution, and long-term enrollment in a single integrated healthcare system provides an unparalleled opportunity to understand the incidence, prevalence, and risk factors for sarcoidosis. Objectives: To determine the epidemiology, patient characteristics, geographic distribution, and associated risk factors of sarcoidosis among U.S. veterans. Methods: We used data from the Veterans Health Administration (VHA) electronic health record system between 2003 and 2019 to evaluate the annual incidence, prevalence, and geographic distribution of sarcoidosis (defined using the International Classification of Diseases codes). We used multivariate logistic regression to examine patient characteristics associated with sarcoidosis incidence. Results: Among more than 13 million veterans who received care through or paid for by the VHA, 23,747 (0.20%) incident diagnoses of sarcoidosis were identified. Compared with selected VHA control subjects using propensity score matching, veterans with sarcoidosis were more likely to be female (13.5% vs. 9.0%), of Black race (52.2% vs. 17.0%), and ever-tobacco users (74.2% vs. 64.5%). There was an increase in the annual incidence of sarcoidosis between 2004 and 2019 (from 38 to 52 cases/100,000 person-years) and the annual prevalence between 2003 and 2019 (from 79 to 141 cases/100,000 persons). In a multivariate logistic regression model, Black race (odds ratio [OR], 4.49; 95% confidence interval [CI], 4.33-4.65), female sex (OR, 1.64; 95% CI, 1.56-1.73), living in the Northeast compared with the western region (OR, 1.57; 95% CI, 1.48-1.67), history of tobacco use (OR, 1.36; 95% CI, 1.31-1.41), and serving in the Army, Air Force, or multiple branches compared with the Navy (OR, 1.08; 95% CI, 1.03-1.13; OR, 1.10; 95% CI, 1.04-1.17; OR, 1.27; 95% CI, 1.16-1.39, respectively) were significantly associated with incident sarcoidosis (P < 0.0001). Conclusions: The incidence and prevalence of sarcoidosis are higher among veterans than in the general population. Alongside traditionally recognized risk factors such as Black race and female sex, we found that a history of tobacco use within the Veterans Affairs population and serving in the Army, Air Force, or multiple service branches were associated with increased sarcoidosis risk.