The human choroid is a heterogeneous, highly vascular connective tissue that dysfunctions in age-related macular degeneration (AMD). In this study, we performed single-cell RNA sequencing on twenty-one human choroids, eleven of which were derived from donors with early atrophic or neovascular AMD. Using this large donor cohort, we identified new gene expression signatures and immunohistochemically characterized discrete populations of resident macrophages, monocytes/inflammatory macrophages, and dendritic cells. These three immune populations demonstrated unique expression patterns for AMD genetic risk factors, with dendritic cells possessing the highest expression of the neovascular AMD-associated MMP9 gene. Additionally, we performed trajectory analysis to model transcriptomic changes across the choroidal vasculature, and we identified expression signatures for endothelial cells from choroidal arterioles and venules. Finally, we performed differential expression analysis between control, early atrophic AMD, and neovascular AMD samples, and we observed that early atrophic AMD samples had high expression of SPARCL1, a gene that has been shown to increase in response to endothelial damage. Likewise, neovascular endothelial cells harbored gene expression changes consistent with endothelial cell damage and demonstrated increased expression of the sialomucins CD34 and ENCM, which were also observed at the protein level within neovascular membranes. Overall, this study characterizes the molecular features of new populations of choroidal endothelial cells and mononuclear phagocytes in a large cohort of AMD and control human donors.

IMPORTANCE For research involving big data, researchers must accurately identify patients with ocular diseases or phenotypes of interest. Reliance on administrative billing codes alone for this purpose is limiting. OBJECTIVE To develop a method to accurately identify the presence or absence of ocular conditions of interest using electronic health record (EHR) data. DESIGN, SETTING, AND PARTICIPANTS This study is a retrospective analysis of the EHR data of patients (n = 122 339) in the Sight Outcomes Research Collaborative Ophthalmology Data Repository who received eye care at participating academic medical centers between August 1, 2012, and August 31, 2017. An algorithm that searches structured and unstructured (free-text) EHR data for conditions of interest was developed and then tested to determine how well it could detect the presence or absence of exfoliation syndrome (XFS). The algorithm was trained to search for evidence of XFS among a sample of patients with and without XFS (n = 200) by reviewing International Classification of Diseases, Ninth Revision or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-9 or ICD-10) billing codes, the patient's problem list, and text within the ocular examination section and unstructured (free-text) data in the EHR. The likelihood that each patient had XFS was estimated using logistic least absolute shrinkage and selection operator (LASSO) regression. The EHR data of all patients were run through the algorithm to generate an XFS probability score for each patient. The algorithm was validated with review of EHRs by glaucoma specialists. MAIN OUTCOMES AND MEASURES Positive predictive value (PPV) and negative predictive value (NPV) of the algorithm were computed as the proportion of patients correctly classified with XFS or without XFS. RESULTS This study included 122 339 patients, with a mean (SD) age of 52.4 (25.1) years. Of these patients, 69 002 (56.4%) were female and 99 579 (81.4%) were white. The algorithm assigned a less than 10% probability of XFS for 121 085 patients (99.0%) as well as an XFS probability score of more than 75% for 543 patients (0.4%), more than 90% for 353 patients (0.3%), and more than 99% for 83 patients (0.07%). Validated by glaucoma specialists, the algorithm had a PPV of 95.0% (95% CI, 89.5%-97.7%) and an NPV of 100% (95% CI, 91.2%-100%). When there was ICD-9 or ICD-10 billing code documentation of XFS, in 86% or 96% of the records, respectively, evidence of XFS was also recorded elsewhere in the EHR. Conversely, when there was clinical examination or free-text evidence of XFS, it was documented with ICD-9 codes only approximately 40% of the time and even less often with ICD-10 codes. CONCLUSIONS AND RELEVANCE The algorithm developed, tested, and validated in this study appears to be better at identifying the presence or absence of XFS in EHR data than the conventional approach of assessing only billing codes; such an algorithm may enhance the ability of investigators to use EHR data to stud...

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