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.

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

BACKGROUND: Electronic medical records (EMRs) offer the promise of computationally identifying sarcoidosis cases. However, the accuracy of identifying these cases in the EMR is unknown. OBJECTIVE: The aim of this study is to determine the statistical performance of using the International Classification of Diseases (ICD) diagnostic codes to identify patients with sarcoidosis in the EMR. METHODS: We used the ICD diagnostic codes to identify sarcoidosis cases by searching the EMRs of the San Francisco and Palo Alto Veterans Affairs medical centers and randomly selecting 200 patients. To improve the diagnostic accuracy of the computational algorithm in cases where histopathological data are unavailable, we developed an index of suspicion to identify cases with a high index of suspicion for sarcoidosis (confirmed and probable) based on clinical and radiographic features alone using the American Thoracic Society practice guideline. Through medical record review, we determined the positive predictive value (PPV) of diagnosing sarcoidosis by two computational methods: using ICD codes alone and using ICD codes plus the high index of suspicion. RESULTS: Among the 200 patients, 158 (79%) had a high index of suspicion for sarcoidosis. Of these 158 patients, 142 (89.9%) had documentation of nonnecrotizing granuloma, confirming biopsy-proven sarcoidosis. The PPV of using ICD codes alone was 79% (95% CI 78.6%-80.5%) for identifying sarcoidosis cases and 71% (95% CI 64.7%-77.3%) for identifying histopathologically confirmed sarcoidosis in the EMRs. The inclusion of the generated high index of suspicion to identify confirmed sarcoidosis cases increased the PPV significantly to 100% (95% CI 96.5%-100%). Histopathology documentation alone was 90% sensitive compared with high index of suspicion. CONCLUSIONS: ICD codes are reasonable classifiers for identifying sarcoidosis cases within EMRs with a PPV of 79%. Using a computational algorithm to capture index of suspicion data elements could significantly improve the case-identification accuracy.