Search

ImportanceDiagnostic information from administrative claims and electronic health record (EHR) data may serve as an important resource for surveillance of vision and eye health, but the accuracy and validity of these sources are unknown.ObjectiveTo estimate the accuracy of diagnosis codes in administrative claims and EHRs compared to retrospective medical record review.Design, Setting, and ParticipantsThis cross-sectional study compared the presence and prevalence of eye disorders based on diagnostic codes in EHR and claims records vs clinical medical record review at University of Washington–affiliated ophthalmology or optometry clinics from May 2018 to April 2020. Patients 16 years and older with an eye examination in the previous 2 years were included, oversampled for diagnosed major eye diseases and visual acuity loss.ExposuresPatients were assigned to vision and eye health condition categories based on diagnosis codes present in their billing claims history and EHR using the diagnostic case definitions of the US Centers for Disease Control and Prevention Vision and Eye Health Surveillance System (VEHSS) as well as clinical assessment based on retrospective medical record review.Main Outcome and MeasuresAccuracy was measured as area under the receiver operating characteristic curve (AUC) of claims and EHR-based diagnostic coding vs retrospective review of clinical assessments and treatment plans.ResultsAmong 669 participants (mean [range] age, 66.1 [16-99] years; 357 [53.4%] female), identification of diseases in billing claims and EHR data using VEHSS case definitions was accurate for diabetic retinopathy (claims AUC, 0.94; 95% CI, 0.91-0.98; EHR AUC, 0.97; 95% CI, 0.95-0.99), glaucoma (claims AUC, 0.90; 95% CI, 0.88-0.93; EHR AUC, 0.93; 95% CI, 0.90-0.95), age-related macular degeneration (claims AUC, 0.87; 95% CI, 0.83-0.92; EHR AUC, 0.96; 95% CI, 0.94-0.98), and cataracts (claims AUC, 0.82; 95% CI, 0.79-0.86; EHR AUC, 0.91; 95% CI, 0.89-0.93). However, several condition categories showed low validity with AUCs below 0.7, including diagnosed disorders of refraction and accommodation (claims AUC, 0.54; 95% CI, 0.49-0.60; EHR AUC, 0.61; 95% CI, 0.56-0.67), diagnosed blindness and low vision (claims AUC, 0.56; 95% CI, 0.53-0.58; EHR AUC, 0.57; 95% CI, 0.54-0.59), and orbital and external diseases (claims AUC, 0.63; 95% CI, 0.57-0.69; EHR AUC, 0.65; 95% CI, 0.59-0.70).Conclusion and RelevanceIn this cross-sectional study of current and recent ophthalmology patients with high rates of eye disorders and vision loss, identification of major vision-threatening eye disorders based on diagnosis codes in claims and EHR records was accurate. However, vision loss, refractive error, and other broadly defined or lower-risk disorder categories were less accurately identified by diagnosis codes in claims and EHR data.

IMPORTANCE: Approximately 2 million cataract operations are performed annually in the US, and patterns of cataract surgery delivery are changing to meet the increasing demand. Therefore, a comparative analysis of visual acuity outcomes after immediate sequential bilateral cataract surgery (ISBCS) vs delayed sequential bilateral cataract surgery (DSBCS) is important for informing future best practices. OBJECTIVE: To compare refractive outcomes of patients who underwent ISBCS, short-interval (1-14 days between operations) DSBCS (DSBCS-14), and long-interval (15-90 days) DSBCS (DSBCS-90) procedures. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used population-based data from the American Academy of Ophthalmology Intelligent Research in Sight (IRIS) Registry. A total of 1 824 196 IRIS Registry participants with bilateral visual acuity measurements who underwent bilateral cataract surgery were assessed. EXPOSURES: Participants were divided into 3 groups (DSBCS-90, DSBCS-14, and ISBCS groups) based on the timing of the second eye surgery. Univariable and multivariable linear regression models were used to analyze the refractive outcomes of the first and second surgery eye. MAIN OUTCOMES AND MEASURES: Mean postoperative uncorrected visual acuity (UCVA) and best-corrected visual acuity (BCVA) after cataract surgery. RESULTS: This study analyzed data from 1 824 196 patients undergoing bilateral cataract surgery (mean [SD] age for those

IMPORTANCE: Diabetic macular edema (DME) is the predominant cause of visual impairment in patients with type 1 or 2 diabetes. Automated fluid volume measurements using optical coherence tomography (OCT) may improve the diagnostic accuracy of DME screening. OBJECTIVE: To assess the diagnostic accuracy of an automated central macular fluid volume (CMFV) quantification using OCT for DME. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional observational study was conducted at a tertiary academic center among 215 patients with diabetes (1 eye each) enrolled from January 26, 2015, to December 23, 2019. All participants underwent comprehensive examinations, 6 × 6-mm macular structural OCT horizontal raster scans, and 6 × 6-mm macular OCT angiography volumetric scans. From January 1 to March 30, 2020, 2 retinal specialists reviewed the structural OCT scans independently and diagnosed DME if intraretinal or subretinal fluid was present. Diabetic macular edema was considered center involved if fluid was present within the central fovea (central 1-mm circle). A third retinal specialist arbitrated any discrepancy. The mean central subfield thickness (CST) within the central fovea was measured on structural OCT horizontal raster scans. A deep learning algorithm automatically quantified fluid volumes on 6 × 6-mm OCT angiography volumetric scans and within the central foveas (CMFV). MAIN OUTCOMES AND MEASURES: The area under the receiver operating characteristic curve (AUROC) and the sensitivity and specificity of CST and CMFV for DME diagnosis. RESULTS: We enrolled 1 eye each of 215 patients with diabetes (117 women [54.4%]; mean [SD] age, 59.6 [12.4] years). Diabetic macular edema was present in 136 eyes; 93 cases of DME were center involved. The AUROC of CMFV for diagnosis of center-involved DME (0.907 [95% CI, 0.861-0.954]) was greater than the AUROC of CST (0.832 [95% CI, 0.775-0.889]; P = .02). With the specificity set at 95%, the sensitivity of CMFV for detection of center-involved DME (78.5% [95% CI, 68.8%-86.3%]) was higher than that of CST (53.8% [95% CI, 43.1%-64.2%]; P = .002). Center-involved DME cases not detected by CST but detected by CMFV were associated with a thinner CST (290.8 μm [95% CI, 282.3-299.3 μm] vs 369.4 μm [95% CI, 347.1-391.7 μm]; P

IMPORTANCE: Recent studies have demonstrated the successful application of artificial intelligence (AI) for automated retinal disease diagnostics but have not addressed a fundamental challenge for deep learning systems: the current need for large, criterion standard–annotated retinal data sets for training. Low-shot learning algorithms, aiming to learn from a relatively low number of training data, may be beneficial for clinical situations involving rare retinal diseases or when addressing potential bias resulting from data that may not adequately represent certain groups for training, such as individuals older than 85 years. OBJECTIVE: To evaluate whether low-shot deep learning methods are beneficial when using small training data sets for automated retinal diagnostics. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study, conducted from July 1, 2019, to June 21, 2020, compared different diabetic retinopathy classification algorithms, traditional and low-shot, for 2-class designations (diabetic retinopathy warranting referral vs not warranting referral). The public domain EyePACS data set was used, which originally included 88 692 fundi from 44 346 individuals. Statistical analysis was performed from February 1 to June 21, 2020. MAIN OUTCOMES AND MEASURES: The performance (95% CIs) of the various AI algorithms was measured via receiver operating curves and their area under the curve (AUC), precision recall curves, accuracy, and F1 score, evaluated for different training data sizes, ranging from 5120 to 10 samples per class. RESULTS: Deep learning algorithms, when trained with sufficiently large data sets (5120 samples per class), yielded comparable performance, with an AUC of 0.8330 (95% CI, 0.8140-0.8520) for a traditional approach (eg, fined-tuned ResNet), compared with low-shot methods (AUC, 0.8348 [95% CI, 0.8159-0.8537]) (using self-supervised Deep InfoMax [our method denoted as DIM]). However, when far fewer training images were available (n = 160), the traditional deep learning approach had an AUC decreasing to 0.6585 (95% CI, 0.6332-0.6838) and was outperformed by a low-shot method using self-supervision with an AUC of 0.7467 (95% CI, 0.7239-0.7695). At very low shots (n = 10), the traditional approach had performance close to chance, with an AUC of 0.5178 (95% CI, 0.4909-0.5447) compared with the best low-shot method (AUC, 0.5778 [95% CI, 0.5512-0.6044]). CONCLUSIONS AND RELEVANCE: These findings suggest the potential benefits of using low-shot methods for AI retinal diagnostics when a limited number of annotated training retinal images are available (eg, with rare ophthalmic diseases or when addressing potential AI bias).

This cross-sectional study assesses whether a model-to-data deep learning approach (ie, validation of the algorithm without any data transfer) can be applied in ophthalmology., Key Points Question Can a model-to-data approach be applied to deep learning studies in ophthalmology? Findings This cross-sectional study applied a model-to-data approach to deep learning using 400 optical coherence tomography B-scans from patients with active exudative age-related macular degeneration. Without any data transfer between institutions, the model was trained to recognize areas of intraretinal fluid on the scans, and no difference was found in the comparison of the model with manual grading. Meaning Although clinical application is at present limited, these results suggest that model-to-data approaches can obviate many of the traditional hurdles in large-scale deep learning projects and may increase application in future ophthalmology studies., Importance Amid an explosion of interest in deep learning in medicine, including within ophthalmology, concerns regarding data privacy, security, and sharing are of increasing importance. A model-to-data approach, in which the model itself is transferred rather than data, can circumvent many of these challenges but has not been previously demonstrated in ophthalmology. Objective To determine whether a model-to-data deep learning approach (ie, validation of the algorithm without any data transfer) can be applied in ophthalmology. Design, Setting, and Participants This single-center cross-sectional study included patients with active exudative age-related macular degeneration undergoing optical coherence tomography (OCT) at the New England Eye Center from August 1, 2018, to February 28, 2019. Data were primarily analyzed from March 1 to June 20, 2019. Main Outcomes and Measures Training of the deep learning model, using a model-to-data approach, in recognizing intraretinal fluid (IRF) on OCT B-scans. Results The model was trained (learning curve Dice coefficient, >80%) using 400 OCT B-scans from 128 participants (69 female [54%] and 59 male [46%]; mean [SD] age, 77.5 [9.1] years). In comparing the model with manual human grading of IRF pockets, no statistically significant difference in Dice coefficients or intersection over union scores was found (P > .05). Conclusions and Relevance A model-to-data approach to deep learning applied in ophthalmology avoided many of the traditional hurdles in large-scale deep learning, including data sharing, security, and privacy concerns. Although the clinical relevance of these results is limited at this time, this proof-of-concept study suggests that such a paradigm should be further examined in larger-scale, multicenter deep learning studies.

IMPORTANCE: Conventional segmentation of the retinal nerve fiber layer (RNFL) is prone to errors that may affect the accuracy of spectral-domain optical coherence tomography (SD-OCT) scans in detecting glaucomatous damage. OBJECTIVE: To develop a segmentation-free deep learning (DL) algorithm for assessment of glaucomatous damage using the entire circle B-scan image from SD-OCT. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study at a single institution used data from SD-OCT images of eyes with glaucoma (perimetric and preperimetric) and normal eyes. The data set was randomly split at the patient level into a training (50%), validation (20%), and test data set (30%). Data were collected from March 2008 to April 2019, and analysis began April 2018. EXPOSURES: A convolutional neural network was trained to discriminate glaucomatous from normal eyes using the SD-OCT circle B-scan without segmentation lines. MAIN OUTCOMES AND MEASURES: The ability to discriminate glaucoma from healthy eyes was evaluated by comparing the area under the receiver operating characteristic curve and sensitivity at 80% or 95% specificity for the DL algorithm’s predicted probability of glaucoma vs conventional RNFL thickness parameters given by SD-OCT software. The performance was also assessed in preperimetric glaucoma, as well as by visual field severity using Hodapp-Parrish-Anderson criteria. RESULTS: A total of 20 806 SD-OCT images from 1154 eyes of 635 individuals (612 [53%] with glaucoma and 542 normal eyes [47%]) were included. The mean (SD) age at SD-OCT scan was 70.8 (10.4) years in individuals with glaucoma and 55.8 (14.1) years in controls. There were 187 women (53.3%) in the glaucoma group and 165 (59.8%) in the control group. Of 612 eyes with glaucoma, 432 (70.4%) had perimetric and 180 (29.6%) had preperimetric glaucoma. The DL algorithm had a significantly higher area under the receiver operating characteristic curve than global RNFL thickness (0.96 vs 0.87; difference = 0.08 [95% CI, 0.04-0.12]) and each RNFL thickness sector for discriminating between glaucoma and controls (all P

IMPORTANCE: Infectious conjunctivitis is highly transmissible and a public health concern. While mitigation strategies have been successful on a local level, population-wide decreases in spread are rare. OBJECTIVE: To evaluate whether internet search interest and emergency department visits for infectious conjunctivitis were associated with public health interventions adopted during the COVID-19 pandemic. DESIGN, SETTING, AND PARTICIPANTS: Internet search data from the US and emergency department data from a single academic center in the US were used in this study. Publicly available smartphone mobility data were temporally aligned to quantify social distancing. Internet search term trends for nonallergic conjunctivitis, corneal abrasions, and posterior vitreous detachments were obtained. Additionally, all patients who presented to a single emergency department from February 2015 to February 2021 were included in a review. Physician notes for emergency department visits at a single academic center with the same diagnoses were extracted. Causal inference was performed using a bayesian structural time-series model. Data were compared from before and after April 2020, when the US Centers for Disease Control and Prevention recommended members of the public wear masks, stay at least 6 feet from others who did not reside in the same home, avoid crowds, and quarantine if experiencing flulike symptoms or exposure to persons with COVID-19 symptoms. EXPOSURES: Symptoms of or interest in conjunctivitis in the context of the COVID-19 pandemic. MAIN OUTCOME AND MEASURES: The hypothesis was that there would be a decrease in internet search interest and emergency department visits for infectious conjunctivitis after the adaptation of public health measures targeted to curb COVID-19. RESULTS: A total of 1156 emergency department encounters with a diagnosis of conjunctivitis were noted from January 2015 to February 2021. Emergency department encounters for nonallergic conjunctivitis decreased by 37.3% (95% CI, −12.9% to −60.6%; P

Importance Big data studies may allow for the aggregation of patients with rare diseases such as uveitis to answer important clinical questions. Standardization of uveitis-related variables will be necessary, including the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes used to identify patients of interest. There are currently limited data on the uniformity of diagnosis mapping to ICD-10 codes for uveitis diagnoses among different health systems. Objective To assess the degree of uniformity in mapping of uveitis clinical concepts to ICD-10 codes across health care systems using the same electronic health record (EHR) system. Design, setting, and participants This multicenter survey study was conducted between September 14 and October 9, 2020, at 5 academic health care systems that use the Epic EHR. Researchers from the University of Washington, Harvard University, Stanford University, Yale University, and the University of California, San Francisco queried 54 uveitis-related diagnostic terms and recorded the associated ICD-10 codes. Main outcomes and measures The degree of uniformity for uveitis clinical concepts and associated ICD-10 codes. Results Fifty-four uveitis-related diagnostic terms were queried within the Epic EHR at 5 different health care systems. There was perfect agreement among all 5 centers for 52 of the 54 diagnostic terms. Two diagnostic terms had differences in ICD-10 coding: juvenile idiopathic arthritis associated chronic uveitis and intermediate uveitis. Intermediate uveitis was associated with codes H20.1x (ICD-10 description: chronic iridocyclitis) or H20.9 (ICD-10 description: unspecified iridocyclitis) in 3 centers while being associated with code H30.2x (ICD-10 description: posterior cyclitis) at the 2 remaining centers. The discrepancies appear to be related to a recent update in diagnostic mapping in the Epic EHR. Conclusions and relevance This study suggests that ICD-10 code mapping to uveitis diagnostic terminology appears to be highly uniform at different centers with the Epic EHR. However, temporal changes in diagnosis mapping to ICD-10 codes and a lack of 1-to-1 mapping of diagnosis to ICD-10 code add additional sources of complexity to the interpretation of big data studies in uveitis.