Six-Year Outcomes in Subjects with Polypoidal Choroidal Vasculopathy in the EVEREST II Study.

INTRODUCTION: The EVEREST II study previously reported that intravitreally administered ranibizumab (IVR) combined with photodynamic therapy (PDT) achieved superior visual gain and polypoidal lesion closure compared to IVR alone in patients with polypoidal choroidal vasculopathy (PCV). This follow-up study reports the long-term outcomes 6 years after initiation of the EVEREST II study. METHODS: This is a non-interventional cohort study of 90 patients with PCV from 16 international trial sites who originally completed the EVEREST II study. The long-term outcomes were assessed during a recall visit at about 6 years from commencement of EVEREST II. RESULTS: The monotherapy and combination groups contained 41 and 49 participants, respectively. The change in best-corrected visual acuity (BCVA) from baseline to year 6 was not different between the monotherapy and combination groups; - 7.4 ± 23.0 versus - 6.1 ± 22.4 letters, respectively. The combination group had greater central subfield thickness (CST) reduction compared to the monotherapy group at year 6 (- 179.9 vs - 74.2 µm, p = 0.011). Fewer eyes had subretinal fluid (SRF)/intraretinal fluid (IRF) in the combination versus monotherapy group at year 6 (35.4% vs 57.5%, p = 0.032). Factors associated with BCVA at year 6 include BCVA (year 2), CST (year 2), presence of SRF/IRF at year 2, and number of anti-VEGF treatments (years 2-6). Factors associated with presence of SRF/IRF at year 6 include combination arm (OR 0.45, p = 0.033), BCVA (year 2) (OR 1.53, p = 0.046), and presence of SRF/IRF (year 2) (OR 2.59, p = 0.042). CONCLUSION: At 6 years following the EVEREST II study, one-third of participants still maintained good vision. As most participants continued to require treatment after exiting the initial trial, ongoing monitoring and re-treatment regardless of polypoidal lesion status are necessary in PCV. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT01846273.

Faricimab Treat-and-Extend for Diabetic Macular Edema: Two-Year Results from the Randomized Phase 3 YOSEMITE and RHINE Trials.

PURPOSE: To evaluate the 2-year efficacy, durability, and safety of dual angiopoietin-2 and vascular endothelial growth factor (VEGF) A pathway inhibition with intravitreal faricimab according to a personalized treat-and-extend (T&E)-based regimen with up to every-16-week dosing in the YOSEMITE and RHINE (ClinicalTrials.gov identifiers, NCT03622580 and NCT03622593, respectively) phase 3 trials of diabetic macular edema (DME). DESIGN: Randomized, double-masked, noninferiority phase 3 trials. PARTICIPANTS: Adults with visual acuity loss (best-corrected visual acuity [BCVA] of 25-73 letters) due to center-involving DME. METHODS: Patients were randomized 1:1:1 to faricimab 6.0 mg every 8 weeks, faricimab 6.0 mg T&E (previously referred to as personalized treatment interval), or aflibercept 2.0 mg every 8 weeks. The T&E up to every-16-week dosing regimen was based on central subfield thickness (CST) and BCVA change. MAIN OUTCOME MEASURES: Included changes from baseline in BCVA and CST, number of injections, durability, absence of fluid, and safety through week 100. RESULTS: In YOSEMITE and RHINE (n = 940 and 951, respectively), noninferior year 1 visual acuity gains were maintained through year 2; mean BCVA change from baseline at 2 years (weeks 92, 96, and 100 average) with faricimab every 8 weeks (YOSEMITE and RHINE, +10.7 letters and +10.9 letters, respectively) or T&E (+10.7 letters and +10.1 letters, respectively) were comparable with aflibercept every 8 weeks (+11.4 letters and +9.4 letters, respectively). The median number of study drug injections was lower with faricimab T&E (YOSEMITE and RHINE, 10 and 11 injections, respectively) versus faricimab every 8 weeks (15 injections) and aflibercept every 8 weeks (14 injections) across both trials during the entire study. In the faricimab T&E arms, durability was improved further during year 2, with > 60% of patients receiving every-16-week dosing and approximately 80% receiving every-12-week or longer dosing at week 96. Almost 80% of patients who achieved every-16-week dosing at week 52 maintained every-16-week dosing without an interval reduction through week 96. Mean CST reductions were greater (YOSEMITE/RHINE weeks 92/96/100 average: faricimab every 8 weeks -216.0/-202.6 µm, faricimab T&E -204.5/-197.1 µm, aflibercept every 8 weeks -196.3/-185.6 µm), and more patients achieved absence of DME (CST < 325 µm; YOSEMITE/RHINE weeks 92-100: faricimab every 8 weeks 87%-92%/88%-93%, faricimab T&E 78%-86%/85%-88%, aflibercept every 8 weeks 77%-81%/80%-84%) and absence of intraretinal fluid (YOSEMITE/RHINE weeks 92-100: faricimab every 8 weeks 59%-63%/56%-62%, faricimab T&E 43%-48%/45%-52%, aflibercept every 8 weeks 33%-38%/39%-45%) with faricimab every 8 weeks or T&E versus aflibercept every 8 weeks through year 2. Overall, faricimab was well tolerated, with a safety profile comparable with that of aflibercept. CONCLUSIONS: Clinically meaningful visual acuity gains from baseline, anatomic improvements, and extended durability with intravitreal faricimab up to every 16 weeks were maintained through year 2. Faricimab given as a personalized T&E-based dosing regimen supports the role of dual angiopoietin-2 and VEGF-A inhibition to promote vascular stability and to provide durable efficacy for patients with DME. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Large language models and their impact in ophthalmology.

The advent of generative artificial intelligence and large language models has ushered in transformative applications within medicine. Specifically in ophthalmology, large language models offer unique opportunities to revolutionise digital eye care, address clinical workflow inefficiencies, and enhance patient experiences across diverse global eye care landscapes. Yet alongside these prospects lie tangible and ethical challenges, encompassing data privacy, security, and the intricacies of embedding large language models into clinical routines. This Viewpoint highlights the promising applications of large language models in ophthalmology, while weighing up the practical and ethical barriers towards their real-world implementation. This Viewpoint seeks to stimulate broader discourse on the potential of large language models in ophthalmology and to galvanise both clinicians and researchers into tackling the prevailing challenges and optimising the benefits of large language models while curtailing the associated risks.

Efficacy, Durability, and Safety of Faricimab in Patients From Asian Countries With Diabetic Macular Edema: 1-Year Subgroup Analysis of the Phase III YOSEMITE and RHINE Trials.

PURPOSE: To assess the 1-year efficacy, durability, and safety of faricimab in patients with diabetic macular edema from Asian and non-Asian countries. DESIGN: Global, multicenter, randomized, double-masked, active comparator-controlled, phase III trials. METHODS: Subgroup analysis of patients from Asian (N=144) and non-Asian (N=1747) countries randomized to faricimab 6.0 mg every 8 weeks (Q8W), faricimab per personalized treatment interval (PTI), or aflibercept 2.0 mg Q8W in the YOSEMITE/RHINE (NCT03622580/NCT03622593) trials. Primary endpoint: best-corrected visual acuity (BCVA) changes from baseline at 1 year, averaged over weeks 48, 52, and 56. RESULTS: Mean BCVA change from baseline at 1 year in the Asian country subgroup was similar between arms: faricimab Q8W (n=50), +10.9 (95% CI: 8.6-13.2); faricimab PTI (n=48) +10.0 (7.7-12.4) letters; aflibercept Q8W (n=46) +9.0 (6.6-11.4) letters. BCVA gains in the non-Asian country subgroup (n=582, 584, 581) were +11.3 (10.5-12.1), +11.2 (10.5-12.0), and +10.7 (9.9-11.5) letters, respectively. At 1 year, 49% of Asian country patients in the faricimab PTI arm achieved Q16W dosing (vs. 52% non-Asian) and 78% achieved ≥Q12W dosing (vs. 72% non-Asian). Anatomic improvementswere generally greater with faricimab versus aflibercept and similar between the Asian and non-Asian country subgroups. Faricimab was well tolerated, with no new safety signals. CONCLUSIONS: Vision, durability, anatomic, and safety outcomes were generally similar between the Asian and non-Asian country subgroups, suggesting that global YOSEMITE/RHINE results may be generalized to the Asian population. These data support the benefit-risk profile of faricimab for treating Asian patients with diabetic macular edema.

Patient, family member, and health care provider perspective on barriers and facilitators to diabetic retinopathy screening in Thailand: A qualitative study.

OBJECTIVES: Diabetic retinopathy (DR) can cause significant visual impairment which can be largely avoided by early detection through proper screening and treatment. People with DR face a number of challenges from early detection to treatment. The aim of this study was to investigate factors that influence DR screening in Thailand and to identify barriers to follow-up compliance from patient, family member, and health care provider (HCP) perspectives. METHODS: A total of 15 focus group discussions (FGDs) were held, each with five to twelve participants. There were three distinct stakeholders: diabetic patients (n = 47) presenting to a diabetic retinopathy clinic in Thailand, their family members (n = 41), and health care providers (n = 34). All focus group conversations were transcribed verbatim. Thematic analysis was used to examine textual material. RESULTS: Different themes emerged from the FGD on knowledge about diabetes, self-care behaviors of diabetes mellitus (DM), awareness about DR, barriers to DR screening, and the suggested solutions to address those barriers. Data showed lower knowledge and awareness about diabetes and DR in both patients and family members. Long waiting times, financial issues, and lack of a person to accompany appointments were identified as the major deterrents for attending DR screening. Family support for patients was found to vary widely, with some patients reporting to have received adequate support while others reported having received minimal support. Even though insurance covered the cost of attending diabetes/DR screening program, some patients did not show up for their appointments. CONCLUSION: Patients need to be well-informed about the asymptomatic nature of diabetes and DR. Communication at the patient level and shared decision-making with HCPs are essential. Family members and non-physician clinicians (such as diabetes nurses, diabetes educators, physician assistants) who work in the field of diabetes play a vital role in encouraging patients to attend diabetes and DR follow-ups visits regularly.

Recent evidence of economic evaluation of artificial intelligence in ophthalmology.

PURPOSE OF REVIEW: Health economic evaluation (HEE) is essential for assessing value of health interventions, including artificial intelligence. Recent approaches, current challenges, and future directions of HEE of artificial intelligence in ophthalmology are reviewed. RECENT FINDINGS: Majority of recent HEEs of artificial intelligence in ophthalmology were for diabetic retinopathy screening. Two models, one conducted in the rural USA (5-year period) and another in China (35-year period), found artificial intelligence to be more cost-effective than without screening for diabetic retinopathy. Two additional models, which compared artificial intelligence with human screeners in Brazil and Thailand for the lifetime of patients, found artificial intelligence to be more expensive from a healthcare system perspective. In the Thailand analysis, however, artificial intelligence was less expensive when opportunity loss from blindness was included. An artificial intelligence model for screening retinopathy of prematurity was cost-effective in the USA. A model for screening age-related macular degeneration in Japan and another for primary angle close in China did not find artificial intelligence to be cost-effective, compared with no screening. The costs of artificial intelligence varied widely in these models. SUMMARY: Like other medical fields, there is limited evidence in assessing the value of artificial intelligence in ophthalmology and more appropriate HEE models are needed.

Polypoidal Choroidal Vasculopathy: Updates on Risk Factors, Diagnosis, and Treatments.

There have been recent advances in basic research and clinical studies in polypoidal choroidal vasculopathy (PCV). A recent, large-scale, population-based study found systemic factors, such as male gender and smoking, were associated with PCV, and a recent systematic review reported plasma C-reactive protein, a systemic biomarker, was associated with PCV. Growing evidence points to an association between pachydrusen, recently proposed extracellular deposits associated with the thick choroid, and the risk of development of PCV. Many recent studies on diagnosis of PCV have focused on applying criteria from noninvasive multimodal retinal imaging without requirement of indocyanine green angiography. There have been attempts to develop deep learning models, a recent subset of artificial intelligence, for detecting PCV from different types of retinal imaging modality. Some of these deep learning models were found to have high performance when they were trained and tested on color retinal images with corresponding images from optical coherence tomography. The treatment of PCV is either a combination therapy using verteporfin photodynamic therapy and anti-vascular endothelial growth factor (VEGF), or anti-VEGF monotherapy, often used with a treat-and-extend regimen. New anti-VEGF agents may provide more durable treatment with similar efficacy, compared with existing anti-VEGF agents. It is not known if they can induce greater closure of polypoidal lesions, in which case, combination therapy may still be a mainstay. Recent evidence supports long-term follow-up of patients with PCV after treatment for early detection of recurrence, particularly in patients with incomplete closure of polypoidal lesions.

The Classification of Common Macular Diseases Using Deep Learning on Optical Coherence Tomography Images with and without Prior Automated Segmentation.

We compared the performance of deep learning (DL) in the classification of optical coherence tomography (OCT) images of macular diseases between automated classification alone and in combination with automated segmentation. OCT images were collected from patients with neovascular age-related macular degeneration, polypoidal choroidal vasculopathy, diabetic macular edema, retinal vein occlusion, cystoid macular edema in Irvine-Gass syndrome, and other macular diseases, along with the normal fellow eyes. A total of 14,327 OCT images were used to train DL models. Three experiments were conducted: classification alone (CA), use of automated segmentation of the OCT images by RelayNet, and the graph-cut technique before the classification (combination method 1 (CM1) and 2 (CM2), respectively). For validation of classification of the macular diseases, the sensitivity, specificity, and accuracy of CA were found at 62.55%, 95.16%, and 93.14%, respectively, whereas the sensitivity, specificity, and accuracy of CM1 were found at 72.90%, 96.20%, and 93.92%, respectively, and of CM2 at 71.36%, 96.42%, and 94.80%, respectively. The accuracy of CM2 was statistically higher than that of CA (p = 0.05878). All three methods achieved AUC at 97%. Applying DL for segmentation of OCT images prior to classification of the images by another DL model may improve the performance of the classification.

Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study.

BACKGROUND: Diabetic retinopathy is a leading cause of preventable blindness, especially in low-income and middle-income countries (LMICs). Deep-learning systems have the potential to enhance diabetic retinopathy screenings in these settings, yet prospective studies assessing their usability and performance are scarce. METHODS: We did a prospective interventional cohort study to evaluate the real-world performance and feasibility of deploying a deep-learning system into the health-care system of Thailand. Patients with diabetes and listed on the national diabetes registry, aged 18 years or older, able to have their fundus photograph taken for at least one eye, and due for screening as per the Thai Ministry of Public Health guidelines were eligible for inclusion. Eligible patients were screened with the deep-learning system at nine primary care sites under Thailand's national diabetic retinopathy screening programme. Patients with a previous diagnosis of diabetic macular oedema, severe non-proliferative diabetic retinopathy, or proliferative diabetic retinopathy; previous laser treatment of the retina or retinal surgery; other non-diabetic retinopathy eye disease requiring referral to an ophthalmologist; or inability to have fundus photograph taken of both eyes for any reason were excluded. Deep-learning system-based interpretations of patient fundus images and referral recommendations were provided in real time. As a safety mechanism, regional retina specialists over-read each image. Performance of the deep-learning system (accuracy, sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) were measured against an adjudicated reference standard, provided by fellowship-trained retina specialists. This study is registered with the Thai national clinical trials registry, TCRT20190902002. FINDINGS: Between Dec 12, 2018, and March 29, 2020, 7940 patients were screened for inclusion. 7651 (96·3%) patients were eligible for study analysis, and 2412 (31·5%) patients were referred for diabetic retinopathy, diabetic macular oedema, ungradable images, or low visual acuity. For vision-threatening diabetic retinopathy, the deep-learning system had an accuracy of 94·7% (95% CI 93·0-96·2), sensitivity of 91·4% (87·1-95·0), and specificity of 95·4% (94·1-96·7). The retina specialist over-readers had an accuracy of 93·5 (91·7-95·0; p=0·17), a sensitivity of 84·8% (79·4-90·0; p=0·024), and specificity of 95·5% (94·1-96·7; p=0·98). The PPV for the deep-learning system was 79·2 (95% CI 73·8-84·3) compared with 75·6 (69·8-81·1) for the over-readers. The NPV for the deep-learning system was 95·5 (92·8-97·9) compared with 92·4 (89·3-95·5) for the over-readers. INTERPRETATION: A deep-learning system can deliver real-time diabetic retinopathy detection capability similar to retina specialists in community-based screening settings. Socioenvironmental factors and workflows must be taken into consideration when implementing a deep-learning system within a large-scale screening programme in LMICs. FUNDING: Google and Rajavithi Hospital, Bangkok, Thailand. TRANSLATION: For the Thai translation of the abstract see Supplementary Materials section.

Diagnosis of Polypoidal Choroidal Vasculopathy From Fluorescein Angiography Using Deep Learning.

PURPOSE: To differentiate polypoidal choroidal vasculopathy (PCV) from choroidal neovascularization (CNV) and to determine the extent of PCV from fluorescein angiography (FA) using attention-based deep learning networks. METHODS: We build two deep learning networks for diagnosis of PCV using FA, one for detection and one for segmentation. Attention-gated convolutional neural network (AG-CNN) differentiates PCV from other types of wet age-related macular degeneration. Gradient-weighted class activation map (Grad-CAM) is generated to highlight important regions in the image for making the prediction, which offers explainability of the network. Attention-gated recurrent neural network (AG-PCVNet) for spatiotemporal prediction is applied for segmentation of PCV. RESULTS: AG-CNN is validated with a dataset containing 167 FA sequences of PCV and 70 FA sequences of CNV. AG-CNN achieves a classification accuracy of 82.80% at image-level, and 86.21% at patient-level for PCV. Grad-CAM shows that regions contributing to decision-making have on average 21.91% agreement with pathological regions identified by experts. AG-PCVNet is validated with 56 PCV sequences from the EVEREST-I study and achieves a balanced accuracy of 81.132% and dice score of 0.54. CONCLUSIONS: The developed software provides a means of performing detection and segmentation of PCV on FA images for the first time. This study is a promising step in changing the diagnostic procedure of PCV and therefore improving the detection rate of PCV using FA alone. TRANSLATIONAL RELEVANCE: The developed deep learning system enables early diagnosis of PCV using FA to assist the physician in choosing the best treatment for optimal visual prognosis.

Deep Learning to Detect OCT-derived Diabetic Macular Edema from Color Retinal Photographs: A Multicenter Validation Study.

PURPOSE: To validate the generalizability of a deep learning system (DLS) that detects diabetic macular edema (DME) from 2-dimensional color fundus photographs (CFP), for which the reference standard for retinal thickness and fluid presence is derived from 3-dimensional OCT. DESIGN: Retrospective validation of a DLS across international datasets. PARTICIPANTS: Paired CFP and OCT of patients from diabetic retinopathy (DR) screening programs or retina clinics. The DLS was developed using data sets from Thailand, the United Kingdom, and the United States and validated using 3060 unique eyes from 1582 patients across screening populations in Australia, India, and Thailand. The DLS was separately validated in 698 eyes from 537 screened patients in the United Kingdom with mild DR and suspicion of DME based on CFP. METHODS: The DLS was trained using DME labels from OCT. The presence of DME was based on retinal thickening or intraretinal fluid. The DLS's performance was compared with expert grades of maculopathy and to a previous proof-of-concept version of the DLS. We further simulated the integration of the current DLS into an algorithm trained to detect DR from CFP. MAIN OUTCOME MEASURES: The superiority of specificity and noninferiority of sensitivity of the DLS for the detection of center-involving DME, using device-specific thresholds, compared with experts. RESULTS: The primary analysis in a combined data set spanning Australia, India, and Thailand showed the DLS had 80% specificity and 81% sensitivity, compared with expert graders, who had 59% specificity and 70% sensitivity. Relative to human experts, the DLS had significantly higher specificity (P = 0.008) and noninferior sensitivity (P < 0.001). In the data set from the United Kingdom, the DLS had a specificity of 80% (P < 0.001 for specificity of >50%) and a sensitivity of 100% (P = 0.02 for sensitivity of > 90%). CONCLUSIONS: The DLS can generalize to multiple international populations with an accuracy exceeding that of experts. The clinical value of this DLS to reduce false-positive referrals, thus decreasing the burden on specialist eye care, warrants a prospective evaluation.

Evolving treatment paradigms for PCV.

Polypoidal choroidal vasculopathy (PCV) is a subtype of neovascular AMD (nAMD) that accounts for a significant proportion of nAMD cases worldwide, and particularly in Asia. Contemporary PCV treatment strategies have closely followed those used in typical nAMD, though there are significant gaps in knowledge on PCV management and it remains unclear if these strategies are appropriate. Current clinical trial data suggest intravitreal anti-vascular endothelial growth factor (VEGF) therapy alone or in combination with photodynamic therapy is effective in managing haemorrhage and exudation in PCV, although the optimal treatment interval, including as-needed and treat-and-extend approaches, is unclear. Newer imaging modalities, including OCT angiography and high-resolution spectral domain OCT have enabled characterisation of unique PCV biomarkers that may provide guidance on how and when treatment and re-treatment should be initiated. Treatment burden for PCV is a major focus of future therapeutic research and several newly developed anti-VEGF agents, including brolucizumab, faricimab, and new modes of drug delivery like the port delivery system, offer hope for dramatically reduced treatment burden for PCV patients. Beyond anti-VEGF therapy, recent developments in our understanding of PCV pathophysiology, in particular the role of choroidal anatomy and lipid mediators in PCV pathogenesis, offer new treatment avenues that may become clinically relevant in the future. This article explores the current management of PCV and more recent approaches to PCV treatment based on an improved understanding of this unique disease process.

Optical Coherence Tomography Classification Systems for Diabetic Macular Edema and Their Associations With Visual Outcome and Treatment Responses - An Updated Review.

ABSTRACT: Optical coherence tomography (OCT) is an invaluable imaging tool in detecting and assessing diabetic macular edema (DME). Over the past decade, there have been different proposed OCT-based classification systems for DME. In this review, we present an update of spectral-domain OCT (SDOCT)-based DME classifications over the past 5 years. In addition, we attempt to summarize the proposed OCT qualitative and quantitative parameters from different classification systems in relation to disease severity, risk of progression, and treatment outcome. Although some OCT-based measurements were found to have prognostic value on visual outcome, there has been a lack of consensus or guidelines on which parameters can be reliably used to predict treatment outcomes. We also summarize recent literatures on the prognostic value of these parameters including quantitative measures such as macular thickness or volume, central subfield thickness or foveal thickness, and qualitative features such as the morphology of the vitreoretinal interface, disorganization of retinal inner layers, ellipsoid zone disruption integrity, and hyperreflec-tive foci. In addition, we discuss that a framework to assess the validity of biomarkers for treatment outcome is essentially important in assessing the prognosis before deciding on treatment in DME. Finally, we echo with other experts on the demand for updating the current diabetic retinal disease classification.

Cost-Utility Analysis of Drug Treatments in Patients with Polypoidal Choroidal Vasculopathy in Thailand.

PURPOSE: The aim of this study was to estimate the cost-utility and budget impact of pharmacological treatments for the eye with polypoidal choroidal vasculopathy (PCV) in Thailand. METHODS: A Markov model-based cost-utility analysis (CUA) and budget impact analysis were conducted. The lifetime cost and outcomes of PCV treatments were estimated. We discounted costs and outcomes at 3% per annum. Parameters were estimated using data from published literatures, local cost and utility data, and epidemiology data among Thai patients. Univariate and probabilistic sensitivity analyses were performed to account for parameter uncertainty. RESULTS: Intravitreal bevacizumab (IVB) resulted in the lowest lifetime cost, followed by IVB plus verteporfin photodynamic therapy (IVB+vPDT) and intravitreal aflibercept (IVA). The combination of IVB or intravitreal ranibizumab (IVR) and verteporfin photodynamic therapy (IVB+vPDT or IVR+vPDT) yielded the highest quality-adjusted life-years (QALY). When compared with IVB from a societal perspective, the incremental cost-effectiveness ratio for patients with PCV receiving IVB+vPDT, IVR+vPDT, IVA were 10,304; 54,135; and 82,738 the United States dollar (USD) per QALY gained, respectively. At the Thai societal willingness to pay threshold of 4884 USD, IVB had the highest probability of being cost-effective (99%) followed by IVB+vPDT (1%). IVB+vPDT could be a cost-effective strategy and required a budget of 12.61 million USD over five fiscal years when the price of verteporfin reduced by approximately 45%. CONCLUSION: None of the drug treatments for PCV was cost-effective in the Thai context. The decreased price of verteporfin is required to support the inclusion of IVB+vPDT in the Thai National List of Essential Medicines for the treatment of PCV.

Treat-and-Extend Regimens for the Management of Neovascular Age-related Macular Degeneration and Polypoidal Choroidal Vasculopathy: Consensus and Recommendations From the Asia-Pacific Vitreo-retina Society.

PURPOSE: Review and provide consensus recommendations on use of treat-and-extend (T&E) regimens for neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) management with relevance for clinicians in the Asia-Pacific region. METHODS: A systematic search of MEDLINE, EMBASE, and Cochrane databases, and abstract databases of the Asia-Pacific Vitreo-retina Society, European Society of Retina Specialists, American Academy of Ophthalmology, and Controversies in Ophthalmology: Asia-Australia congresses, was conducted to assess evidence for T&E regimens in nAMD. Only studies with ≥100 study eyes were included. An expert panel reviewed the results and key factors potentially influencing the use of T&E regimens in nAMD and PCV, and subsequently formed consensus recommendations for their application in the Asia-Pacific region. RESULTS: Twenty-seven studies were included. Studies demonstrated that T&E regimens with aflibercept, ranibizumab, or bevacizumab in nAMD, and with aflibercept in PCV, were efficacious and safe. The recommendation for T&E is, after ≥3 consecutive monthly loading doses, treatment intervals can be extended by 2 to 4 weeks up to 12 to 16 weeks. When disease activity recurs, the recommendation is to reinject and shorten intervals by 2 to 4 weeks until fluid resolution, after which treatment intervals can again be extended. Intraretinal fluid should be treated until resolved; however, persistent minimal subretinal fluid after consecutive treatments may be tolerated with treatment intervals maintained or extended if the clinical condition is stable. CONCLUSIONS: T&E regimens are efficacious and safe for nAMD and PCV, can reduce the number of visits, and minimize the overall burden for clinicians and patients.

Capacity building in screening and treatment of diabetic retinopathy in Asia-Pacific region.

The focus of capacity building for screening and treatment of diabetic retinopathy (DR) is on health professionals who are nonophthalmologists. Both physicians and nonphysicians are recruited for screening DR. Although there is no standardization of the course syllabus for the capacity building, it is generally accepted to keep their sensitivity >80%, specificity >95%, and clinical failure rate <5% for the nonophthalmologists, if possible. A systematic literature search was performed using the PubMed database and the following search terms: diabetic retinopathy, diabetic retinopathy screening, Asia, diabetic retinopathy treatment, age-related macular degeneration, capacity building, deep learning, artificial intelligence (AI), nurse-led clinic, and intravitreal injection (IVI). AI may be a tool for improving their capacity. Capacity building on IVIs of antivascular endothelial growth factors for DR is focused on nurses. There is evidence that, after a supervision of an average of 100 initial injections, the trained nurses can do the injections effectively and safely, the rate of endophthalmitis ranges from 0.03 to 0.07%, comparable to ophthalmologists. However, laws and regulations, which are different among countries, are challenges and barriers for nonophthalmologists, particularly for nonphysicians, for both screening and treatment of DR. Even if nonphysicians or physicians who are nonophthalmologists are legally approved for these tasks, sustainability of the capacity is another important challenge, this may be achieved if the capacity building can be part of their career development. Patient acceptability is another important barrier for initiating care provided by nonophthalmologists, particularly in Asia. There are also collaborations between national eye institutes of high-income countries, nongovernment organizations, and local eye institutes to improve both the quality and quantity of ophthalmologists and retinal specialists in low-income countries in Asia. This approach may require more labor, cost, and time consuming than training nonophthalmologists.

Economic Evaluations of Artificial Intelligence in Ophthalmology.

ABSTRACT: Artificial intelligence (AI) is expected to cause significant medical quality enhancements and cost-saving improvements in ophthalmology. Although there has been a rapid growth of studies on AI in the recent years, real-world adoption of AI is still rare. One reason may be because the data derived from economic evaluations of AI in health care, which policy makers used for adopting new technology, have been fragmented and scarce. Most data on economics of AI in ophthalmology are from diabetic retinopathy (DR) screening. Few studies classified costs of AI software, which has been considered as a medical device, into direct medical costs. These costs of AI are composed of initial and maintenance costs. The initial costs may include investment in research and development, and costs for validation of different datasets. Meanwhile, the maintenance costs include costs for algorithms upgrade and hardware maintenance in the long run. The cost of AI should be balanced between manufacturing price and reimbursements since it may pose significant challenges and barriers to providers. Evidence from cost-effectiveness analyses showed that AI, either standalone or used with humans, was more cost-effective than manual DR screening. Notably, economic evaluation of AI for DR screening can be used as a model for AI to other ophthalmic diseases.

Non-ICGA treatment criteria for Suboptimal Anti-VEGF Response for Polypoidal Choroidal Vasculopathy: APOIS PCV Workgroup Report 2.

PURPOSE: To develop and validate OCT and color fundus photography (CFP) criteria in differentiating polypoidal choroidal vasculopathy (PCV) from typical neovascular age-related macular degeneration (nAMD) in eyes with suboptimal response to anti-vascular endothelial growth factor (VEGF) monotherapy and to determine whether OCT alone can be used to guide photodynamic therapy (PDT) treatment. DESIGN: Clinical study evaluating diagnostic accuracy. PARTICIPANTS: Patients with nAMD who received 3-month anti-VEGF monotherapy but had persistent activity defined as subretinal fluid or intraretinal fluid at month 3 assessments. METHODS: In phase 1, international retina experts evaluated OCT and CFP of eyes with nAMD to identify the presence or absence of features due to PCV. The performance of individual and combinations of these features were compared with ICGA. In phase 2, these criteria were applied to an independent image set to assess generalizability. In a separate exercise, retinal experts drew proposed PDT treatment spots using only OCT and near-infrared (NIR) images in eyes with PCV and persistent activity. The location and size of proposed spot were compared with ICGA to determine the extent of coverage of polypoidal lesions (PLs) and branching neovascular network (BNN). MAIN OUTCOME MEASURES: Sensitivity and specificity of CFP and OCT criteria to differentiate PCV from nAMD and accuracy of coverage of OCT-guided PDT compared with ICGA. RESULTS: In eyes with persistent activity, the combination of 3 non-ICGA-based criteria (sharp-peaked pigment epithelial detachment [PED], subretinal pigment epithelium [RPE] ring-like lesion, and orange nodule) to detect PCV showed good agreement compared with ICGA, with an area under the receiver operating characteristic curve of 0.85. Validation using both an independent image set and assessors achieved an accuracy of 0.77. Compared with ICGA, the OCT-guided PDT treatment spot covered 100% of PL and 90% of the BNN. CONCLUSIONS: In nAMD eyes with persistent activity, OCT and CFP can differentiate PCV from typical nAMD, which may allow the option of adjunct PDT treatment. Furthermore, OCT alone can be used to plan adjunct PDT treatment without the need for ICGA, with consistent and complete coverage of PL.