Medical, Environmental, and Social Determinants Associated With Periocular Cutaneous Malignancies in the United States Using the All of Us National Database.

OBJECTIVE: To identify common factors associated with periocular cutaneous malignancies using the National Institutes of Health (NIH) All of Us database. METHODOLOGY: In this case-control study, we extracted electronic health records and sociodemographic data for 385 cases of periocular cutaneous malignancies from the All of Us nationwide database. Controls (N = 1540) were matched to the demographic characteristics of the 2020 United States Census. Bivariate analyses and multivariable logistic regression determined variables significantly associated with increased odds of periocular cutaneous malignancies. We analyzed medical, environmental, and social determinants to evaluate which factors were associated with increased odds of periocular cutaneous malignancies. RESULTS: Among the cases, the mean (standard deviation) age was 66.8 (11.2) years at the time of diagnosis. The majority were male (207, 54%) and white (361, 94%). Periocular cutaneous malignancy was significantly more likely among individuals with high sun exposure (odds ratio [OR] 14.79, 95% confidence interval [CI] 3.35-85.73, P = 0.001), those identifying as white race (OR 3.88, 95% CI 1.06-25.33, P = 0.079), and those with higher socioeconomic status, including higher annual income (OR 1.35, 95% CI 1.25-1.46, P < 0.001). CONCLUSIONS: This study demonstrates similar risk factors for periocular cutaneous malignancies, echoing prior research that showed increased associations with lighter-pigmented skin and higher socioeconomic status. It also sheds light on the positive impact of physician surveillance and health utilization factors in the early detection and treatment of these malignancies, aspects less explored in prior analyses.

Association between Glucagon-Like Peptide 1 (GLP-1) Receptor Agonists Exposure and Intraocular Pressure Change: GLP-1 Receptor Agonists and Intraocular Pressure Change.

OBJECTIVE: This study aims to provide data on the effects of glucagon-like peptide 1 receptor (GLP-1R) agonists on intraocular pressure (IOP). DESIGN: Retrospective clinical cohort study. SETTING: Multicenter. STUDY POPULATION: 1247 glaucoma surgery and treatment naïve eyes of 626 patients who were initiated on GLP-1R agonists compared to 1083 glaucoma surgery and treatment naïve eyes of 547 patients who were initiated on other oral antidiabetics. OBSERVATION PROCEDURES: The University of California Health Data Warehouse was queried for patients exposed to GLP-1R agonists or other oral antidiabetics. Index date was defined as the date of first exposure to the medication. Eyes with at least one pre-exposure and one post-exposure tonometry record within 365 days of the index date were included in the analysis. Clinical and laboratory data elements were extracted from the database. Eyes were censored from the analysis upon exposure to glaucoma hypotensive medication or glaucoma surgery. ∆IOP was analyzed using a paired t-test. Regression analysis was conducted using generalized estimating equations (GEE) accounting for inter-eye correlation. Sensitivity analyses were performed to assess the robustness of the findings. MAIN OUTCOME MEASURES: Primary outcome measure was ∆IOP after exposure to the medication. RESULTS: The median age of all included subjects was 66.2 years [IQR=18.3]; 607 (51.7%) were female, and 667 (56.9%) were Caucasian. Median pre-exposure IOP, HbA1c, and BMI were 15.2 mmHg [IQR=3.8], 7.5 [IQR=2.4], and 29.8 [IQR=9.4], respectively. 776 individuals (66.1%) had diabetes, with the median number of active oral antidiabetics being 1.0 [IQR=1.0], and 441 (37.5%) being insulin users. Several pre-exposure characteristics significantly differed between the GLP-1R agonist and the control group. The mean ∆IOP was -0.4±2.8 mmHg (paired t-test p<0.001) and -0.2±3.3 mmHg (paired t-test p = 0.297) in the GLP-1R agonist and other antidiabetics groups, respectively. Pre-exposure IOP was the only independent predictor of ΔIOP in multivariable GEE. Sensitivity analyses yielded similar results. CONCLUSIONS: Although GLP-1R agonists were significantly associated with a decrease in IOP in the paired analysis, they were not associated with ΔIOP in multivariable GEE. Moreover, the difference between the ΔIOP in the two groups was small. Future prospective studies following a standardized dose and delivery method may provide further insights.

Association between Glucagon-Like Peptide 1 (GLP-1) Receptor Agonists Exposure and Intraocular Pressure Change.

Objective: This study aims to provide data on the effects of glucagon-like peptide 1 receptor (GLP-1R) agonists on intraocular pressure (IOP). Design: Retrospective cohort study. Subjects Participants and/or Controls: 1247 glaucoma surgery and treatment naïve eyes of 626 patients who were initiated on GLP-1R agonists compared to 1083 glaucoma surgery and treatment naïve eyes of 547 patients who were initiated on other oral antidiabetics. Methods Intervention or Testing: The University of California Health Data Warehouse was queried for patients exposed to GLP-1R agonists or other oral antidiabetics. Index date was defined as the date of first exposure to the medication. Eyes with at least one pre-exposure and one post-exposure tonometry record within 365 days of the index date were included in the analysis. Clinical and laboratory data elements were extracted from the database. Eyes were censored from the analysis upon exposure to glaucoma hypotensive medication or glaucoma surgery. ΔIOP was analyzed using a paired t-test. Regression analysis was conducted using generalized estimating equations (GEE) accounting for inter-eye correlation. Sensitivity analyses were performed to assess the robustness of the findings. Main Outcome Measures: Primary outcome measure was ΔIOP after exposure to the medication. Results: The median age of all included subjects was 66.2 years [IQR=18.3]; 607 (51.7%) were female, and 667 (56.9%) were Caucasian. Median pre-exposure IOP, HbA1c, and BMI were 15.2 mmHg [IQR=3.8], 7.5 [IQR=2.4], and 29.8 [IQR=9.4], respectively. 776 individuals (66.1%) had diabetes, with the median number of active oral antidiabetics being 1.0 [IQR=1.0], and 441 (37.5%) being insulin users. Several pre-exposure characteristics significantly differed between the GLP-1R agonist and the control group. The mean ΔIOP was -0.4±2.8 mmHg (paired t-test p<0.001) and -0.2±3.3 mmHg (paired t-test p = 0.297) in the GLP-1R agonist and other antidiabetics groups, respectively. Pre-exposure IOP was the only independent predictor of ΔIOP in multivariable GEE. Sensitivity analyses yielded similar results. Conclusions: Although GLP-1R agonists were significantly associated with a decrease in IOP in the paired analysis, they were not associated with ΔIOP in multivariable GEE. Moreover, the difference between the ΔIOP in the two groups was small. Future prospective studies following a standardized dose and delivery method may provide further insights.

Advancing Toward a Common Data Model in Ophthalmology: Gap Analysis of General Eye Examination Concepts to Standard Observational Medical Outcomes Partnership (OMOP) Concepts.

Purpose: Evaluate the degree of concept coverage of the general eye examination in one widely used electronic health record (EHR) system using the Observational Health Data Sciences and Informatics Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Design: Study of data elements. Participants: Not applicable. Methods: Data elements (field names and predefined entry values) from the general eye examination in the Epic foundation system were mapped to OMOP concepts and analyzed. Each mapping was given a Health Level 7 equivalence designation-equal when the OMOP concept had the same meaning as the source EHR concept, wider when it was missing information, narrower when it was overly specific, and unmatched when there was no match. Initial mappings were reviewed by 2 graders. Intergrader agreement for equivalence designation was calculated using Cohen's kappa. Agreement on the mapped OMOP concept was calculated as a percentage of total mappable concepts. Discrepancies were discussed and a final consensus created. Quantitative analysis was performed on wider and unmatched concepts. Main Outcome Measures: Gaps in OMOP concept coverage of EHR elements and intergrader agreement of mapped OMOP concepts. Results: A total of 698 data elements (210 fields, 488 values) from the EHR were analyzed. The intergrader kappa on the equivalence designation was 0.88 (standard error 0.03, P < 0.001). There was a 96% agreement on the mapped OMOP concept. In the final consensus mapping, 25% (1% fields, 31% values) of the EHR to OMOP concept mappings were considered equal, 50% (27% fields, 60% values) wider, 4% (8% fields, 2% values) narrower, and 21% (52% fields, 8% values) unmatched. Of the wider mapped elements, 46% were missing the laterality specification, 24% had other missing attributes, and 30% had both issues. Wider and unmatched EHR elements could be found in all areas of the general eye examination. Conclusions: Most data elements in the general eye examination could not be represented precisely using the OMOP CDM. Our work suggests multiple ways to improve the incorporation of important ophthalmology concepts in OMOP, including adding laterality to existing concepts. There exists a strong need to improve the coverage of ophthalmic concepts in source vocabularies so that the OMOP CDM can better accommodate vision research. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Towards effective data sharing in ophthalmology: data standardization and data privacy.

PURPOSE OF REVIEW: The purpose of this review is to provide an overview of updates in data standardization and data privacy in ophthalmology. These topics represent two key aspects of medical information sharing and are important knowledge areas given trends in data-driven healthcare. RECENT FINDINGS: Standardization and privacy can be seen as complementary aspects that pertain to data sharing. Standardization promotes the ease and efficacy through which data is shared. Privacy considerations ensure that data sharing is appropriate and sufficiently controlled. There is active development in both areas, including government regulations and common data models to advance standardization, and application of technologies such as blockchain and synthetic data to help tackle privacy issues. These advancements have seen use in ophthalmology, but there are areas where further work is required. SUMMARY: Information sharing is fundamental to both research and care delivery, and standardization/privacy are key constituent considerations. Therefore, widespread engagement with, and development of, data standardization and privacy ecosystems stand to offer great benefit to ophthalmology.