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PurposeTo investigate sex-based differences in inflammation-related biomarkers on spectral-domain OCT.DesignCross-sectional study.ParticipantsPatients with diabetic macular edema (DME) between February 1, 2019, and March 31, 2023, without intravitreal anti-VEGF injection within the previous 6 months.MethodsWe reviewed each patient's medical record for age, biological sex, race and ethnicity, most recent glycated hemoglobin A1c (HbA1c) level, visual acuity (VA), and central macular thickness (CMT). OCT biomarkers that have been found in literature to be associated with inflammation, including disorganization of retinal inner layers (DRIL), retinal hyperreflective retinal foci (HRFs), hyperreflective choroidal foci (HCFs), subfoveal neuroretinal detachment (SND), and perturbation in retinal nerve fiber layer thickness, ganglion cell layer thickness, and inner nuclear layer (INL) thickness were evaluated by graders masked to the clinical characteristics of the patients. We performed multivariable regression analyses with the OCT biomarkers as the outcome variables and sex, age, HbA1c, and CMT as independent variables.Main outcome measuresOCT inflammation-related biomarkers, as listed above.ResultsFemale patients were, on average, 2 years older than male patients (P = 0.041). There were no significant differences in race and ethnicity, HbA1c, VA, or CMT between male and female patients. After controlling for age, HbA1c, and CMT, we found male sex to be associated with more HRF (incidence rate ratio [IRR] = 1.19; 95% confidence interval [CI] = 1.10-1.29), more HCF (odds ratio = 2.01; 95% CI = 1.12-3.64), and thicker INL (7 μm thicker in males; 95% CI = 2-12). Sex was not a significant predictor for either DRIL or SND in the multivariable regression models. Patients with higher HbA1c were more likely to have more HRF (IRR = 1.02 per 1 point increase; 95% CI = 1.00-1.04) after controlling for other factors.ConclusionsMale sex was correlated with more inflammation-related biomarkers on OCT including more HRF, more HCF, and thicker INL, after accounting for age, glycemic control, and amount of DME. Further studies are needed to evaluate the potential implications of these sex-based differences for individualized treatment.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

PurposeElectronic health records (EHRs) contain a vast amount of clinical data. Improved automated classification approaches have the potential to accurately and efficiently identify patient cohorts for research. We evaluated if a rule-based natural language processing (NLP) algorithm using clinical notes performed better for classifying proliferative diabetic retinopathy (PDR) and nonproliferative diabetic retinopathy (NPDR) severity compared with International Classification of Diseases, ninth edition (ICD-9) or 10th edition (ICD-10) codes.DesignCross-sectional study.SubjectsDeidentified EHR data from an academic medical center identified 2366 patients aged ≥18 years, with diabetes mellitus, diabetic retinopathy (DR), and available clinical notes.MethodsFrom these 2366 patients, 306 random patients (100 training set, 206 test set) underwent chart review by ophthalmologists to establish the gold standard. International Classification of Diseases codes were extracted from the EHR. The notes algorithm identified positive mention of PDR and NPDR severity from clinical notes. Proliferative diabetic retinopathy and NPDR severity classification by ICD codes and the notes algorithm were compared with the gold standard. The entire DR cohort (N = 2366) was then classified as having presence (or absence) of PDR using ICD codes and the notes algorithm.Main outcome measuresSensitivity, specificity, positive predictive value (PPV), negative predictive value, and F1 score for the notes algorithm compared with ICD codes using a gold standard of chart review.ResultsFor PDR classification of the test set patients, the notes algorithm performed better than ICD codes for all metrics. Specifically, the notes algorithm had significantly higher sensitivity (90.5% [95% confidence interval 85.7, 94.9] vs. 68.4% [60.4, 75.3]), but similar PPV (98.0% [95.4-100] vs. 94.7% [90.3, 98.3]) respectively. The F1 score was 0.941 [0.910, 0.966] for the notes algorithm compared with 0.794 [0.734, 0.842] for ICD codes. For PDR classification, ICD-10 codes performed better than ICD-9 codes (F1 score 0.836 [0.771, 0.878] vs. 0.596 [0.222, 0.692]). For NPDR severity classification, the notes algorithm performed similarly to ICD codes, but performance was limited by small sample size.ConclusionsThe notes algorithm outperformed ICD codes for PDR classification. The findings demonstrate the significant potential of applying a rule-based NLP algorithm to clinical notes to increase the efficiency and accuracy of cohort selection for research.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

To investigate the variability in subthreshold laser treatment plans for patients with diabetic macular edema or central serous chorioretinopathy. Diagnostic images from 20 patients were utilized, and 25 retina specialists generated subthreshold treatment plans along with a self-rated experience level. Evaluation comprised of i) Area Variability (AV): quantifies the consensus regarding the covered area and is the difference between the areas planned by 75% and 25% of the participants ii) mean Centroid Distance (CD): level of agreement on the localization of a treatment. Subgroup analysis investigated the impact of participants experience levels, utilizing the Mann-Whitney-Wilcoxon test. The predominant plan style is a targeted treatment approach (92%) and avoidance of subfoveal region (89%). Mean CD is 71.0 ± 37.5 pixels (≈half disc diameter) and mean AV is 9.8%±8.9%. A slight difference is observed between the 50-75% areas, but a notable distinction exists between the 25-50% areas. Subgroup analysis revealed CD and AV value of 75.9 pixels and 24% in the lower experience level group as opposed to 55.9 pixels and 8.6% in the higher experience level group. There is significant variability in treatment planning which reduces with increased experience of retina specialists. While consensus is observed around focal points, differences in the surrounding extents persist.

CLINICAL RELEVANCE: Although diabetes is associated with a classic microvascular disease of the retina, it is also increasingly being recognized as a cause of retinal neuropathy. Preclinical evidence suggests that retinal neuropathy in diabetes manifests in part as photoreceptor dysfunction, preceding the development of vascular features in experimental models. It remains unknown whether such findings are relevant to patients with diabetes. METHODS: Here, we review 4 lines of clinical evidence suggesting that diabetes-associated photoreceptor pathology is linked to the development of retinal microvascular disease. RESULTS: First, a major population-based investigation of susceptibility loci for diabetic retinopathy (DR) implicated a photoreceptor protein product as a protective factor. Next, electroretinography and other studies of visual function collectively show that rod and/or cone-derived abnormalities occur decades before the development of vascular features of DR. Third, protection from DR seemingly develops in patients with coincident retinitis pigmentosa, as suggested by several case series. Finally, based on anatomic features, we propose that the beneficial effect of macular laser in DR occurs via ablation of diseased photoreceptors. CONCLUSIONS: The evidence we present is limited due to the small patient populations used in the studies we cite and due to the lack of methodologies that allow causative relationships to be inferred. Collectively, however, these clinical observations suggest that photoreceptors are involved in early diabetic retinal disease and may in fact give rise to the classic features of DR. FINANCIAL DISCLOSURES: Proprietary or commercial disclosures may be found in the Footnotes and Disclosures at the end of this article.

PurposeUse of the electronic health record (EHR) has motivated the need for data standardization. A gap in knowledge exists regarding variations in existing terminologies for defining diabetic retinopathy (DR) cohorts. This study aimed to review the literature and analyze variations regarding codified definitions of DR.DesignLiterature review and quantitative analysis.SubjectsPublished manuscripts.MethodsFour graders reviewed PubMed and Google Scholar for peer-reviewed studies. Studies were included if they used codified definitions of DR (e.g., billing codes). Data elements such as author names, publication year, purpose, data set type, and DR definitions were manually extracted. Each study was reviewed by ≥ 2 authors to validate inclusion eligibility. Quantitative analyses of the codified definitions were then performed to characterize the variation between DR cohort definitions.Main outcome measuresNumber of studies included and numeric counts of billing codes used to define codified cohorts.ResultsIn total, 43 studies met the inclusion criteria. Half of the included studies used datasets based on structured EHR data (i.e., data registries, institutional EHR review), and half used claims data. All but 1 of the studies used billing codes such as the International Classification of Diseases 9th or 10th edition (ICD-9 or ICD-10), either alone or in addition to another terminology for defining disease. Of the 27 included studies that used ICD-9 and the 20 studies that used ICD-10 codes, the most common codes used pertained to the full spectrum of DR severity. Diabetic retinopathy complications (e.g., vitreous hemorrhage) were also used to define some DR cohorts.ConclusionsSubstantial variations exist among codified definitions for DR cohorts within retrospective studies. Variable definitions may limit generalizability and reproducibility of retrospective studies. More work is needed to standardize disease cohorts.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.