CLINICAL CHARACTERIZATION OF AQUEOUS AND VITREOUS RETINOL-BINDING PROTEIN 3 CONCENTRATIONS IN RELATION TO DIABETIC RETINOPATHY SEVERITY, RETINAL STRUCTURES, AND SYSTEMIC COMPLICATIONS.

PURPOSE: To evaluate Retinol-Binding Protein 3 (RBP3) from photoreceptors in aqueous and its association with vitreous concentrations, diabetic retinopathy (DR) severity, retinal layer thickness, and clinical characteristics in people with diabetes. METHODS: RBP3 concentration was measured by custom-developed enzyme-linked immunosorbent assay in aqueous and correlated with vitreous concentrations in patients from the 50-Year Medalist study and Beetham Eye Institute at Joslin Diabetes Center. RESULTS: Aqueous RBP3 concentration (N = 131) was elevated in eyes with no to mild DR (mean ± SD 0.7 nM ± 0.2) and decreased in eyes with moderate to severe DR (0.65 nM ± 0.3) and proliferative DR (0.5 nM ± 0.2, P < 0.001) compared to eyes without diabetes. Aqueous and vitreous RBP3 concentrations correlated with each other (r = 0.34, P = 0.001) and between fellow eyes (P < 0.0001). History of retinal surgery did not affect aqueous RBP3 concentrations, but cataract surgery affected both vitreous and aqueous levels. Elevated aqueous RBP3 concentration associated with increased thickness of the outer nuclear layer (P = 0.004) and correlated with hemoglobin A1c, whereas vitreous RBP3 concentrations correlated with diabetic systemic complications. CONCLUSION: These findings suggest that aqueous RBP3 concentration may be an important endogenous clinical retinal protective factor, a biomarker for DR severity, and a promising VEGF-independent clinical intervention target in DR.

Intensive Glycemic Management Is Associated With Reduced Retinal Structure Abnormalities on Ocular Coherence Tomography in the DCCT/EDIC Study.

OBJECTIVE: To investigate quantitative and qualitative changes in retinal structure using optical coherence tomography (OCT) and their associations with systemic or other risk factors in individuals with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: In the Epidemiology of Diabetes Interventions and Complications (EDIC) study, OCT images were obtained during study years 25-28 (2019-2022) in 937 participants; 54% and 46% were from the original intensive (INT) and conventional (CONV) glycemic management treatment groups, respectively. RESULTS: Average age for participants was 61 years old, diabetes duration 39 years, and HbA1c 7.6%. Participants originally in the CONV group were more likely to have disorganization of retinal inner layers (DRIL) (CONV 27.3% vs. INT 18.7%; P = 0.0003), intraretinal fluid (CONV 24.4% vs. INT 19.2%; P = 0.0222), and intraretinal cysts (CONV 20.8% vs. INT 16.6%; P = 0.0471). In multivariable models, sex, age, smoking, mean updated systolic blood pressure, and history of "clinically significant" macular edema (CSME) and of anti-VEGF treatment were independently associated with changes in central subfield thickness, while HbA1c, BMI, and history of CSME and of ocular surgery were associated with DRIL. Visual acuity (VA) decline was associated with significant thinning of all retinal subfields except for the central and inner nasal subfields. CONCLUSIONS: Early intensive glycemic management in T1D is associated with a decreased risk of DRIL. This important morphological abnormality was associated with a history of macular edema, a history of ocular surgery, and worse VA. This study reveals benefits of intensive glycemic management on the retina beyond features detected by fundus photographs and ophthalmoscopy.

Disparities Between Teleretinal Imaging Findings and Patient-Reported Diabetic Retinopathy Status and Follow-up Eye Care Interval: A 10-Year Prospective Study.

OBJECTIVE: To assess self-reported awareness of diabetic retinopathy (DR) and concordance of eye examination follow-up compared with findings from concurrent retinal images. RESEARCH DESIGN AND METHODS: We conducted a prospective observational 10-year study of 26,876 consecutive patients with diabetes who underwent retinal imaging during an endocrinology visit. Awareness and concordance were evaluated using questionnaires and retinal imaging. RESULTS: Awareness information and gradable images were available in 25,360 patients (94.3%). Severity of DR by imaging was as follows: no DR (n = 14,317; 56.5%), mild DR (n = 6,805; 26.8%), or vision-threatening DR (vtDR; n = 4,238; 16.7%). In the no, mild, and vtDR groups, 96.7%, 88.5%, and 54.9% of patients, respectively, reported being unaware of any prior DR. When DR was present, reporting no prior DR was associated with shorter diabetes duration, milder DR, last eye examination >1 year before, no dilation, no scheduled appointment, and less specialized provider (all P < 0.001). Among patients with vtDR, 41.2%, 58.1%, and 64.2% did not report being aware of any DR and follow-up was concordant with current DR severity in 66.7%, 41.3%, and 25.4% (P < 0.001) of patients when prior examination was performed by a retinal specialist, nonretinal ophthalmologist, or optometrist (P < 0.001), respectively. CONCLUSIONS: Substantial discrepancies exist between DR presence, patient awareness, and concordance of follow-up across all DR severity levels. These discrepancies are present across all eye care provider types, with the magnitude influenced by provider type. Therefore, patient self-report should not be relied upon to reflect DR status. Modification of medical care and education models may be necessary to enhance retention of ophthalmic knowledge in patients with diabetes and ensure accurate communication between all health care providers.

Risk Factors for Meeting Criteria for Switching from Bevacizumab to Aflibercept When Treating Eyes with Diabetic Macular Edema and Visual Acuity of < 20/40.

PURPOSE: To identify factors for meeting prespecified criteria for switching from bevacizumab to aflibercept in eyes with center-involved diabetic macular edema (CI-DME) and moderate vision loss initially treated with bevacizumab in DRCR Retina Network protocol AC. DESIGN: Post hoc analysis of data from a randomized clinical trial. PARTICIPANTS: Two hundred seventy participants with one or both eyes harboring CI-DME with visual acuity (VA) letter score of 69 to 24 (Snellen equivalent, 20/50-20/320). METHODS: Eligible eyes were assigned to receive intravitreal aflibercept monotherapy (n = 158) or bevacizumab followed by aflibercept if prespecified criteria for switching were met between 12 weeks and 2 years (n = 154). MAIN OUTCOME MEASURES: Meeting switching criteria: (1) at any time, (2) at 12 weeks, and (3) after 12 weeks. Associations between meeting the criteria for switching and factors measured at baseline and 12 weeks were evaluated in univariable analyses. Stepwise procedures were used to select variables for multivariable models. RESULTS: In the group receiving bevacizumab first, older participants showed a higher risk of meeting the switching criteria at any time, with a hazard ratio (HR) for a 10-year increase in age of 1.32 (95% confidence interval [CI], 1.11-1.58). Male participants or eyes with worse baseline VA were more likely to switch at 12 weeks (for male vs. female: odds ratio [OR], 4.84 [95% CI, 1.32-17.81]; 5-letter lower baseline VA: OR, 1.30 [95% CI, 1.03-1.63]). Worse 12-week central subfield thickness (CST; 10-µm greater: HR, 1.06 [95% CI, 1.04-1.07]) was associated with increased risk of switching after 12 weeks. The mean ± standard deviation improvement in visual acuity after completing the switch to aflibercept was 3.7 ± 4.9 letters compared with the day of switching. CONCLUSIONS: The identified factors can be used to refine expectations regarding the likelihood that an eye will meet protocol criteria to switch to aflibercept when treatment is initiated with bevacizumab. Older patients are more likely to be switched. At 12 weeks, thicker CST was predictive of eyes most likely to be switched in the future. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Automated Machine Learning for Predicting Diabetic Retinopathy Progression From Ultra-Widefield Retinal Images.

Importance: Machine learning (ML) algorithms have the potential to identify eyes with early diabetic retinopathy (DR) at increased risk for disease progression. Objective: To create and validate automated ML models (autoML) for DR progression from ultra-widefield (UWF) retinal images. Design, Setting and Participants: Deidentified UWF images with mild or moderate nonproliferative DR (NPDR) with 3 years of longitudinal follow-up retinal imaging or evidence of progression within 3 years were used to develop automated ML models for predicting DR progression in UWF images. All images were collected from a tertiary diabetes-specific medical center retinal image dataset. Data were collected from July to September 2022. Exposure: Automated ML models were generated from baseline on-axis 200° UWF retinal images. Baseline retinal images were labeled for progression based on centralized reading center evaluation of baseline and follow-up images according to the clinical Early Treatment Diabetic Retinopathy Study severity scale. Images for model development were split 8-1-1 for training, optimization, and testing to detect 1 or more steps of DR progression. Validation was performed using a 328-image set from the same patient population not used in model development. Main Outcomes and Measures: Area under the precision-recall curve (AUPRC), sensitivity, specificity, and accuracy. Results: A total of 1179 deidentified UWF images with mild (380 [32.2%]) or moderate (799 [67.8%]) NPDR were included. DR progression was present in half of the training set (590 of 1179 [50.0%]). The model's AUPRC was 0.717 for baseline mild NPDR and 0.863 for moderate NPDR. On the validation set for eyes with mild NPDR, sensitivity was 0.72 (95% CI, 0.57-0.83), specificity was 0.63 (95% CI, 0.57-0.69), prevalence was 0.15 (95% CI, 0.12-0.20), and accuracy was 64.3%; for eyes with moderate NPDR, sensitivity was 0.80 (95% CI, 0.70-0.87), specificity was 0.72 (95% CI, 0.66-0.76), prevalence was 0.22 (95% CI, 0.19-0.27), and accuracy was 73.8%. In the validation set, 6 of 9 eyes (75%) with mild NPDR and 35 of 41 eyes (85%) with moderate NPDR progressed 2 steps or more were identified. All 4 eyes with mild NPDR that progressed within 6 months and 1 year were identified, and 8 of 9 (89%) and 17 of 20 (85%) with moderate NPDR that progressed within 6 months and 1 year, respectively, were identified. Conclusions and Relevance: This study demonstrates the accuracy and feasibility of automated ML models for identifying DR progression developed using UWF images, especially for prediction of 2-step or greater DR progression within 1 year. Potentially, the use of ML algorithms may refine the risk of disease progression and identify those at highest short-term risk, thus reducing costs and improving vision-related outcomes.

Retinal artery to vein ratio is associated with cerebral microbleeds in individuals with type 1 diabetes.

OBJECTIVES: A third of asymptomatic individuals with type 1 diabetes (T1D) show signs of cerebrovascular disease in brain MRI. These signs associate with advanced stages of diabetic retinal disease, but not in mild or moderate retinopathy. We aimed to evaluate a wider spectrum of retinal changes by exploring the relationship between quantitative measures of retinal vessel parameters (RVP) and cerebrovascular changes in T1D. METHODS: We included 146 neurologically asymptomatic individuals with T1D [51% women, median age 40 (33.0-45.1) years] and 24 healthy, sex-matched and age-matched controls. All individuals underwent a clinical and biochemical work-up and brain MRI, which was evaluated for cerebral microbleeds (CMBs), white matter hyperintensities, and lacunar infarcts. RVPs, including central retinal arteriole (CRAE) and central retinal vein (CRVE) equivalents and the ratio of the two variables (arteriovenous ratio, AVR) were assessed quantitatively by a computer-assisted method (IVAN software, version 3.2.6) from fundus images. RESULTS: Among T1D participants, those with CMBs had a lower arteriovenous ratio (AVR) compared with those without CMBs ( P  = 0.023). AVR was inversely associated with the amount of CMBs ( r  = -0.063, P  = 0.035). CMB prevalence was higher in those with AVR below the median (31%) compared with above the median (16%, P  < 0.001), and this difference was significant also after individuals with only no-to-mild retinopathy were included (28 vs. 16%, P  = 0.005). A correlation between blood pressure and CRAE ( r  = -0.19, P  = 0.025) appeared among those with T1D. CONCLUSION: Regardless of the severity of diabetic retinopathy, AVR is associated with the existence of CMBs in T1D.

Imaging Modalities for Assessing the Vascular Component of Diabetic Retinal Disease: Review and Consensus for an Updated Staging System.

Purpose: To review the evidence for imaging modalities in assessing the vascular component of diabetic retinal disease (DRD), to inform updates to the DRD staging system. Design: Standardized narrative review of the literature by an international expert workgroup, as part of the DRD Staging System Update Effort, a project of the Mary Tyler Moore Vision Initiative. Overall, there were 6 workgroups: Vascular Retina, Neural Retina, Systemic Health, Basic and Cellular Mechanisms, Visual Function, and Quality of Life. Participants: The Vascular Retina workgroup, including 16 participants from 4 countries. Methods: Literature review was conducted using standardized evidence grids for 5 modalities: standard color fundus photography (CFP), widefield color photography (WFCP), standard fluorescein angiography (FA), widefield FA (WFFA), and OCT angiography (OCTA). Summary levels of evidence were determined on a validated scale from I (highest) to V (lowest). Five virtual workshops were held for discussion and consensus. Main Outcome Measures: Level of evidence for each modality. Results: Levels of evidence for standard CFP, WFCP, standard FA, WFFA, and OCTA were I, II, I, I, and II respectively. Traditional vascular lesions on standard CFP should continue to be included in an updated staging system, but more studies are required before they can be used in posttreatment eyes. Widefield color photographs can be used for severity grading within the area covered by standard CFPs, although these gradings may not be directly interchangeable with each other. Evaluation of the peripheral retina on WFCP can be considered, but the method of grading needs to be clarified and validated. Standard FA and WFFA provide independent prognostic value, but the need for dye administration should be considered. OCT angiography has significant potential for inclusion in the DRD staging system, but various barriers need to be addressed first. Conclusions: This study provides evidence-based recommendations on the utility of various imaging modalities for assessment of the vascular component of DRD, which can inform future updates to the DRD staging system. Although new imaging modalities offer a wealth of information, there are still major gaps and unmet research needs that need to be addressed before this potential can be realized. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis.

Color fundus photography (CFP) and Optical coherence tomography (OCT) images are two of the most widely used modalities in the clinical diagnosis and management of retinal diseases. Despite the widespread use of multimodal imaging in clinical practice, few methods for automated diagnosis of eye diseases utilize correlated and complementary information from multiple modalities effectively. This paper explores how to leverage the information from CFP and OCT images to improve the automated diagnosis of retinal diseases. We propose a novel multimodal learning method, named geometric correspondence-based multimodal learning network (GeCoM-Net), to achieve the fusion of CFP and OCT images. Specifically, inspired by clinical observations, we consider the geometric correspondence between the OCT slice and the CFP region to learn the correlated features of the two modalities for robust fusion. Furthermore, we design a new feature selection strategy to extract discriminative OCT representations by automatically selecting the important feature maps from OCT slices. Unlike the existing multimodal learning methods, GeCoM-Net is the first method that formulates the geometric relationships between the OCT slice and the corresponding region of the CFP image explicitly for CFP and OCT fusion. Experiments have been conducted on a large-scale private dataset and a publicly available dataset to evaluate the effectiveness of GeCoM-Net for diagnosing diabetic macular edema (DME), impaired visual acuity (VA) and glaucoma. The empirical results show that our method outperforms the current state-of-the-art multimodal learning methods by improving the AUROC score 0.4%, 1.9% and 2.9% for DME, VA and glaucoma detection, respectively.

Motion Contrast, Phase Gradient, and Simultaneous OCT Images Assist in the Interpretation of Dark-Field Images in Eyes with Retinal Pathology.

The cellular-level visualization of retinal microstructures such as blood vessel wall components, not available with other imaging modalities, is provided with unprecedented details by dark-field imaging configurations; however, the interpretation of such images alone is sometimes difficult since multiple structural disturbances may be present in the same time. Particularly in eyes with retinal pathology, microstructures may appear in high-resolution retinal images with a wide range of sizes, sharpnesses, and brightnesses. In this paper we show that motion contrast and phase gradient imaging modalities, as well as the simultaneous acquisition of depth-resolved optical coherence tomography (OCT) images, provide additional insight to help understand the retinal neural and vascular structures seen in dark-field images and may enable improved diagnostic and treatment plans.

Optical coherence tomography in the management of diabetic macular oedema.

Diabetic macular oedema (DMO) is the major cause of visual impairment in people with diabetes. Optical coherence tomography (OCT) is now the most widely used modality to assess presence and severity of DMO. DMO is currently broadly classified based on the involvement to the central 1 mm of the macula into non-centre or centre involved DMO (CI-DMO) and DMO can occur with or without visual acuity (VA) loss. This classification forms the basis of management strategies of DMO. Despite years of research on quantitative and qualitative DMO related features assessed by OCT, these do not fully inform physicians of the prognosis and severity of DMO relative to visual function. Having said that, recent research on novel OCT biomarkers development and re-defined classification of DMO show better correlation with visual function and treatment response. This review summarises the current evidence of the association of OCT biomarkers in DMO management and its potential clinical importance in predicting VA and anatomical treatment response. The review also discusses some future directions in this field, such as the use of artificial intelligence to quantify and monitor OCT biomarkers and retinal fluid and identify phenotypes of DMO, and the need for standardisation and classification of OCT biomarkers to use in future clinical trials and clinical practice settings as prognostic markers and secondary treatment outcome measures in the management of DMO.

Predictors of New Onset Atrial Fibrillation Burden in the Critically Ill.

INTRODUCTION: Atrial fibrillation (AF) is common in the intensive care unit (ICU) setting and has been associated with adverse outcomes. In this context, there is increasing research interest in AF burden as a predictor of subsequent adverse events. However, the pathophysiology and drivers of AF burden in the ICU are poorly understood. This study sought to evaluate the predictors of AF burden in critical illness-associated new-onset AF (CI-NOAF). METHODS: Out of 7,030 admissions in a tertiary general ICU between December 2015 and September 2018, 309 patients developed CI-NOAF. AF burden was defined as the percentage of monitored time in AF, as extracted from hourly interpretations of continuous ECG monitoring. Low and high AF burden groups were defined relative to the median AF burden. Clinical, laboratory, and echocardiographic parameters were extracted, and multivariable modelling with binary logistic regression was performed to evaluate for independent associations with AF burden. RESULTS: The median AF burden was 7.0%. Factors associated with increased AF burden were age, dyslipidaemia, chronic kidney disease, increased creatinine, CHA2DS2-VASc score, ICU admission diagnosis category, amiodarone administration, and left atrial area (LAA). Factors associated with lower AF burden were previous alcohol excess, burden of ventilation, the use of inotropes/vasopressors, and beta blockers. On multivariate analysis, increased LAA, chronic kidney disease, and amiodarone use were independently associated with increased AF burden, whereas beta blocker use was associated with lower AF burden. CONCLUSION: Left atrial size and chronic cardiovascular comorbidities appear to be the primary drivers of CI-NOAF burden, whereas factors related to acute illness and critical care intervention paradoxically did not appear to be a substantial driver of arrhythmia burden. Further research is needed regarding drivers of AF and the efficacy of rhythm control intervention in this unique setting.

Next-generation forward genetic screens: uniting high-throughput perturbations with single-cell analysis.

Programmable genome-engineering technologies, such as CRISPR (clustered regularly interspaced short palindromic repeats) nucleases and massively parallel CRISPR screens that capitalize on this programmability, have transformed biomedical science. These screens connect genes and noncoding genome elements to disease-relevant phenotypes, but until recently have been limited to individual phenotypes such as growth or fluorescent reporters of gene expression. By pairing massively parallel screens with high-dimensional profiling of single-cell types/states, we can now measure how individual genetic perturbations or combinations of perturbations impact the cellular transcriptome, proteome, and epigenome. We review technologies that pair CRISPR screens with single-cell multiomics and the unique opportunities afforded by extending pooled screens using deep multimodal phenotyping.

Home OCT Imaging for Newly Diagnosed Neovascular Age-Related Macular Degeneration: A Feasibility Study.

OBJECTIVE: To assess the feasibility of daily Home OCT imaging among patients with neovascular age-related macular degeneration (nAMD). DESIGN: Prospective observational study. PARTICIPANTS: Participants with ≥ 1 eye with previously untreated nAMD and visual acuity 20/20 to 20/320. METHODS: Participants meeting the ocular eligibility criteria were considered for enrollment; those who provided consent received a Notal Vision Home OCT device. Participants were instructed to scan both eyes daily. Retina specialists managed treatment according to their standard practice, without access to the Home OCT data. The presence of fluid detected by a reading center (RC) from in-office OCT scans was compared with fluid volumes measured by the Notal OCT Analyzer (NOA) on Home OCT images. MAIN OUTCOME MEASURES: Proportion of participants meeting ocular eligibility criteria who participated in daily scanning, frequency and duration of scanning, proportion of scans eligible for fluid quantification, participant experience with the device, agreement between the RC and NOA fluid determinations, and characteristics of fluid dynamics. RESULTS: Among 40 participants meeting ocular eligibility criteria, 14 (35%) initiated self-scanning. Planned travel (n = 7, 17.5%) and patient-reported inadequate cell reception for the upload of images (n = 5, 12.5%) were the most frequent reasons for not participating. Considering scans of the study eye only, the mean (standard deviation) was 6.3 (0.6) for weekly scanning frequency and 47 (17) seconds for scan duration per eye. Among 2304 scans, 86.5% were eligible for fluid quantification. All participants agreed that scanning became easier over time, and only 1 did not want to continue daily scanning. For 35 scan pairs judged as having fluid by in-office OCT, the NOA detected fluid on 31 scans (89%). For 14 scan pairs judged as having no fluid on in-office OCT, the NOA did not detect fluid on 10 scans (71%). Daily fluid patterns after treatment initiation varied considerably between patients. CONCLUSIONS: For patients with nAMD who initiated home scanning, frequency and quality of scanning and accuracy of fluid detection were sufficient to assess the monitoring of fluid at home. Accommodations for travel and Wi-Fi connectivity could improve uptake of the Home OCT device. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Report From the 2022 Mary Tyler Moore Vision Initiative Diabetic Retinal Disease Clinical Endpoints Workshop.

The Mary Tyler Moore Vision Initiative Diabetic Retinal Disease (DRD) Clinical Endpoints Workshop was held on October 22, 2022 to accelerate progress toward establishment of useful clinical and research endpoints and development of new therapeutics that have important relevance across the full spectrum of DRD pathology. More than 90 patient representatives, clinicians, scientists, funding and regulatory agencies, diagnostic, therapeutic and biotech industry representatives discussed the needs for new diagnostic and therapeutic approaches to prevent and restore retinal neurovascular unit integrity. Phase I of the MTM Vision Initiative plans, notably updating the DRD staging system and severity scale, establishing a human ocular biorepository and resource, and clinical endpoints and biomarker development and validation, was emphasized.

Cellular-Level Analysis of Retinal Blood Vessel Walls Based on Phase Gradient Images.

Diseases such as diabetes affect the retinal vasculature and the health of the neural retina, leading to vision problems. We describe here an imaging method and analysis procedure that enables characterization of the retinal vessel walls with cellular-level resolution, potentially providing markers for eye diseases. Adaptive optics scanning laser ophthalmoscopy is used with a modified detection scheme to include four simultaneous offset aperture channels. The magnitude of the phase gradient derived from these offset images is used to visualize the structural characteristics of the vessels. The average standard deviation image provides motion contrast and enables segmentation of the vessel lumen. Segmentation of blood vessel walls provides quantitative measures of geometrical characteristics of the vessel walls, including vessel and lumen diameters, wall thickness, and wall-to-lumen ratio. Retinal diseases may affect the structural integrity of the vessel walls, their elasticity, their permeability, and their geometrical characteristics. The ability to measure these changes is valuable for understanding the vascular effects of retinal diseases, monitoring disease progression, and drug testing. In addition, loss of structural integrity of the blood vessel wall may result in microaneurysms, a hallmark lesion of diabetic retinopathy, which may rupture or leak and further create vision impairment. Early identification of such structural abnormalities may open new treatment avenues for disease management and vision preservation. Functional testing of retinal circuitry through high-resolution measurement of vasodilation as a response to controlled light stimulation of the retina (neurovascular coupling) is another application of our method and can provide an unbiased evaluation of one's vision and enable early detection of retinal diseases and monitoring treatment results.

INTEGRATING MACULAR OPTICAL COHERENCE TOMOGRAPHY WITH ULTRAWIDE-FIELD IMAGING IN A DIABETIC RETINOPATHY TELEMEDICINE PROGRAM USING A SINGLE DEVICE.

PURPOSE: To determine the effect of combined macular spectral-domain optical coherence tomography (SD-OCT) and ultrawide field retinal imaging (UWFI) within a telemedicine program. METHODS: Comparative cohort study of consecutive patients with both UWFI and SD-OCT. Ultrawide field retinal imaging and SD-OOCT were independently evaluated for diabetic macular edema (DME) and nondiabetic macular abnormality. Sensitivity and specificity were calculated with SD-OCT as the gold standard. RESULTS: Four hundred twenty-two eyes from 211 diabetic patients were evaluated. Diabetic macular edema severity by UWFI was as follows: no DME 93.4%, noncenter involved DME (nonciDME) 5.1%, ciDME 0.7%, ungradable DME 0.7%. SD-OCT was ungradable in 0.5%. Macular abnormality was identified in 34 (8.1%) eyes by UWFI and in 44 (10.4%) eyes by SD-OCT. Diabetic macular edema represented only 38.6% of referable macular abnormality identified by SD-OCT imaging. Sensitivity/specificity of UWFI compared with SD-OCT was 59%/96% for DME and 33%/99% for ciDME. Sensitivity/specificity of UWFI compared with SDOCT was 3%/98% for epiretinal membrane. CONCLUSION: Addition of SD-OCT increased the identification of macular abnormality by 29.4%. More than 58.3% of the eyes believed to have any DME on UWF imaging alone were false-positives by SD-OCT. The integration of SD-OCT with UWFI markedly increased detection and reduced false-positive assessments of DME and macular abnormality in a teleophthalmology program.

Axicabtagene Ciloleucel in Combination with the 4-1BB Agonist Utomilumab in Patients with Relapsed/Refractory Large B-Cell Lymphoma: Phase 1 Results from ZUMA-11.

PURPOSE: Chimeric antigen receptor (CAR) T-cell therapies have shown clinical benefit for patients with relapsed/refractory (R/R) large B-cell lymphoma (LBCL), yet approximately 60% of patients do not respond or eventually relapse. We investigated the safety and feasibility of the CD19-directed CAR T-cell therapy axicabtagene ciloleucel (axi-cel) in combination with the 4-1BB agonist antibody utomilumab as an approach to improve efficacy of CAR T-cell therapy. PATIENTS AND METHODS: In phase 1 of the single-arm ZUMA-11 trial, patients with R/R LBCL received a single axi-cel infusion (target dose, 2 × 106 cells/kg) plus utomilumab 10 to 200 mg intravenously every 4 weeks for up to 6 months in a dose-escalation design. The primary endpoint was incidence of dose-limiting toxicities (DLT) with utomilumab. Key secondary endpoints were safety, antitumor activity, pharmacokinetics, and pharmacodynamics. RESULTS: No DLTs were observed among patients treated with axi-cel and utomilumab (n = 12). Grade ≥3 adverse events occurred in 10 patients (83%); none were Grade ≥3 cytokine release syndrome or neurologic events. The objective response rate was 75% and seven patients (58%) had a complete response. Peak CAR T-cell levels increased in a utomilumab dose-dependent manner up to 100 mg. Patients who received utomilumab 100 mg had persistently increased CAR T cells on days 57 to 168 compared with other dose levels. Utomilumab was associated with dose-dependent increases in IL2, IFNγ, and IL10. CONCLUSIONS: Utomilumab-mediated 4-1BB agonism combined with axi-cel therapy had a manageable safety profile. Dual 4-1BB and CD28 costimulation is a feasible therapeutic approach that may enhance CAR T-cell expansion in patients with LBCL.

Performance of Automated Machine Learning for Diabetic Retinopathy Image Classification from Multi-field Handheld Retinal Images.

PURPOSE: To create and validate code-free automated deep learning models (AutoML) for diabetic retinopathy (DR) classification from handheld retinal images. DESIGN: Prospective development and validation of AutoML models for DR image classification. PARTICIPANTS: A total of 17 829 deidentified retinal images from 3566 eyes with diabetes, acquired using handheld retinal cameras in a community-based DR screening program. METHODS: AutoML models were generated based on previously acquired 5-field (macula-centered, disc-centered, superior, inferior, and temporal macula) handheld retinal images. Each individual image was labeled using the International DR and diabetic macular edema (DME) Classification Scale by 4 certified graders at a centralized reading center under oversight by a senior retina specialist. Images for model development were split 8-1-1 for training, optimization, and testing to detect referable DR ([refDR], defined as moderate nonproliferative DR or worse or any level of DME). Internal validation was performed using a published image set from the same patient population (N = 450 images from 225 eyes). External validation was performed using a publicly available retinal imaging data set from the Asia Pacific Tele-Ophthalmology Society (N = 3662 images). MAIN OUTCOME MEASURES: Area under the precision-recall curve (AUPRC), sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), accuracy, and F1 scores. RESULTS: Referable DR was present in 17.3%, 39.1%, and 48.0% of the training set, internal validation, and external validation sets, respectively. The model's AUPRC was 0.995 with a precision and recall of 97% using a score threshold of 0.5. Internal validation showed that SN, SP, PPV, NPV, accuracy, and F1 scores were 0.96 (95% confidence interval [CI], 0.884-0.99), 0.98 (95% CI, 0.937-0.995), 0.96 (95% CI, 0.884-0.99), 0.98 (95% CI, 0.937-0.995), 0.97, and 0.96, respectively. External validation showed that SN, SP, PPV, NPV, accuracy, and F1 scores were 0.94 (95% CI, 0.929-0.951), 0.97 (95% CI, 0.957-0.974), 0.96 (95% CI, 0.952-0.971), 0.95 (95% CI, 0.935-0.956), 0.97, and 0.96, respectively. CONCLUSIONS: This study demonstrates the accuracy and feasibility of code-free AutoML models for identifying refDR developed using handheld retinal imaging in a community-based screening program. Potentially, the use of AutoML may increase access to machine learning models that may be adapted for specific programs that are guided by the clinical need to rapidly address disparities in health care delivery. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Open-source tools for behavioral video analysis: Setup, methods, and best practices.

Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These tools overcome long-standing limitations of manual scoring of video frames and traditional 'center of mass' tracking algorithms to enable video analysis at scale. The expansion of open-source tools for video acquisition and analysis has led to new experimental approaches to understand behavior. Here, we review currently available open-source tools for video analysis and discuss how to set up these methods for labs new to video recording. We also discuss best practices for developing and using video analysis methods, including community-wide standards and critical needs for the open sharing of datasets and code, more widespread comparisons of video analysis methods, and better documentation for these methods especially for new users. We encourage broader adoption and continued development of these tools, which have tremendous potential for accelerating scientific progress in understanding the brain and behavior.