Evaluation of Depression and Anxiety in a Diverse Population With Thyroid Eye Disease Using the Nationwide NIH All of Us Database.

PURPOSE: To evaluate the prevalence of depression and anxiety among individuals with thyroid eye disease (TED) and identify sociodemographic risk factors using the NIH All of Us database. METHODS: Three hundred ninety eight cases with TED were compared with 1,592 controls with demographics matching the 2020 US Census. Primary outcomes were diagnosed depression or anxiety; Patient Health Questionnaire-9 (PHQ-9) scores and General Anxiety Disorder-7 (GAD-7) scores were included as secondary outcomes. We performed multivariable logistic regression to generate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between TED and depression and anxiety. RESULTS: Patients with TED were more likely to have depression (OR 2.72, 95% CI 2.08-3.56, p < 0.001) and anxiety (OR 2.82, 95% CI 2.16-3.70, p < 0.001) than controls. In patients with TED, female gender was an independent risk factor for both depression (OR 1.72; 95% CI 1.00-5.07, p = 0.05) and anxiety (OR 2.17, 95% CI 1.25-3.85, p = 0.006). Unemployment (OR 1.72, 95% CI 1.03-2.94, p = 0.04) and lower income (OR 0.88 for income as a continuous variable, 95% CI 0.79-0.99, p = 0.03) were risk factors for anxiety. Risk factors for more severe depression as assessed by PHQ-9 included lower income (OR 0.70, 95% CI 0.57-0.85, p < 0.001), and protective factors included Black race (OR 0.12, 95% CI 0.02-0.45, p = 0.002). Lower income was associated with more severe anxiety as assessed by GAD-7 (OR 0.77, 95% CI 0.64-0.94, p = 0.009). CONCLUSIONS: Patients with TED were more likely to develop depression and anxiety compared with controls. This study highlights the psychosocial impact of TED and associated socioeconomic risk factors.

Social Determinants of Health Data Availability for Patients with Eye Conditions.

Purpose: To quantify and characterize social determinants of health (SDoH) data coverage using single-center electronic health records (EHRs) and the National Institutes of Health All of Us research program. Design: Retrospective cohort study from June 2014 through June 2021. Participants: Adults 18 years of age or older with a diagnosis of diabetic retinopathy, glaucoma, cataracts, or age-related macular degeneration. Methods: For All of Us, research participants completed online survey forms as part of a nationwide prospective cohort study. In local EHRs, patients were selected based on diagnosis codes. Main Outcome Measures: Social determinants of health data coverage, characterized by the proportion of each disease cohort with available data regarding demographics and socioeconomic factors. Results: In All of Us, we identified 23 806 unique adult patients, of whom 2246 had a diagnosis of diabetic retinopathy, 13 448 had a diagnosis of glaucoma, 6634 had a diagnosis of cataracts, and 1478 had a diagnosis of age-related macular degeneration. Survey completion rates were high (99.5%-100%) across all cohorts for demographic information, overall health, income, education, and lifestyle. However, health care access (12.7%-29.4%), housing (0.7%-1.1%), social isolation (0.2%-0.3%), and food security (0-0.1%) showed significantly lower response rates. In local EHRs, we identified 80 548 adult patients, of whom 6616 had a diagnosis of diabetic retinopathy, 26 793 had a diagnosis of glaucoma, 40 427 had a diagnosis of cataracts, and 6712 had a diagnosis of age-related macular degeneration. High data coverage was found across all cohorts for variables related to tobacco use (82.84%-89.07%), alcohol use (77.45%-83.66%), and intravenous drug use (84.76%-93.14%). However, low data coverage (< 50% completion) was found for all other variables, including education, finances, social isolation, stress, physical activity, food insecurity, and transportation. We used chi-square testing to assess whether the data coverage varied across different disease cohorts and found that all fields varied significantly (P < 0.001). Conclusions: The limited and highly variable data coverage in both local EHRs and All of Us highlights the need for researchers and providers to develop SDoH data collection strategies and to assemble complete datasets.

Text Processing for Detection of Fungal Ocular Involvement in Critical Care Patients: Cross-Sectional Study.

BACKGROUND: Fungal ocular involvement can develop in patients with fungal bloodstream infections and can be vision-threatening. Ocular involvement has become less common in the current era of improved antifungal therapies. Retrospectively determining the prevalence of fungal ocular involvement is important for informing clinical guidelines, such as the need for routine ophthalmologic consultations. However, manual retrospective record review to detect cases is time-consuming. OBJECTIVE: This study aimed to determine the prevalence of fungal ocular involvement in a critical care database using both structured and unstructured electronic health record (EHR) data. METHODS: We queried microbiology data from 46,467 critical care patients over 12 years (2000-2012) from the Medical Information Mart for Intensive Care III (MIMIC-III) to identify 265 patients with culture-proven fungemia. For each fungemic patient, demographic data, fungal species present in blood culture, and risk factors for fungemia (eg, presence of indwelling catheters, recent major surgery, diabetes, immunosuppressed status) were ascertained. All structured diagnosis codes and free-text narrative notes associated with each patient's hospitalization were also extracted. Screening for fungal endophthalmitis was performed using two approaches: (1) by querying a wide array of eye- and vision-related diagnosis codes, and (2) by utilizing a custom regular expression pipeline to identify and collate relevant text matches pertaining to fungal ocular involvement. Both approaches were validated using manual record review. The main outcome measure was the documentation of any fungal ocular involvement. RESULTS: In total, 265 patients had culture-proven fungemia, with Candida albicans (n=114, 43%) and Candida glabrata (n=74, 28%) being the most common fungal species in blood culture. The in-hospital mortality rate was 121 (46%). In total, 7 patients were identified as having eye- or vision-related diagnosis codes, none of whom had fungal endophthalmitis based on record review. There were 26,830 free-text narrative notes associated with these 265 patients. A regular expression pipeline based on relevant terms yielded possible matches in 683 notes from 108 patients. Subsequent manual record review again demonstrated that no patients had fungal ocular involvement. Therefore, the prevalence of fungal ocular involvement in this cohort was 0%. CONCLUSIONS: MIMIC-III contained no cases of ocular involvement among fungemic patients, consistent with prior studies reporting low rates of ocular involvement in fungemia. This study demonstrates an application of natural language processing to expedite the review of narrative notes. This approach is highly relevant for ophthalmology, where diagnoses are often based on physical examination findings that are documented within clinical notes.