Eye

BackgroundDiabetes mellitus (DM) remains a major global health challenge and is associated with vision-threatening complications, including diabetic macular edema (DME), a leading cause of visual impairment. Dyslipidemia has been implicated in the development of macular edema through mechanisms involving vascular permeability, endothelial dysfunction, and chronic inflammation. However, evidence regarding the relationship between lipid abnormalities and macular edema remains inconsistent across studies. AimThis study aimed to evaluate the association between abnormal lipid profiles and diabetic macular edema among patients with type 2 diabetes mellitus attending Kilimanjaro Christian Medical Centre (KCMC). MethodsA hospital-based analytical cross-sectional study was conducted among 296 diabetic outpatients at KCMC. Participants underwent comprehensive ophthalmic evaluation including fundoscopy and imaging with optical coherence tomography (OCT) for assessment of macular edema. Blood samples were collected for biochemical lipid analysis. Data were cleaned and analyzed using STATA version 17. ResultsDiabetic macular edema was identified in 56.4% (167/296) of participants. Abnormal lipid parameters were common, with elevated total cholesterol observed in 48.6%, triglycerides in 43.6%, low-density lipoprotein (LDL) in 36.1%, and reduced high-density lipoprotein (HDL) in 38.9% of patients. Elevated total cholesterol, triglycerides, and LDL levels showed significant associations with macular edema (p < 0.05). After multivariable adjustment, serum triglycerides remained independently associated with macular edema (p = 0.002). ConclusionDyslipidemia demonstrated a significant association with diabetic macular edema, with serum triglycerides emerging as an independent predictor. These findings highlight the importance of lipid monitoring, lifestyle modification, and strengthened screening strategies in reducing the burden of vision-threatening diabetic complications.

IntroductionAnti-vascular endothelial growth factor (anti-VEGF) therapies are standards of care for vision-threatening retinal diseases. This retrospective observational study describes demographics, utilization, best recorded visual acuity (BRVA), and safety among eyes with neovascular age-related macular degeneration (nAMD), diabetic retinopathy (DR), diabetic macular edema (DME), or retinal vein occlusion (RVO) treated with the biosimilar aflibercept-ayyh (PAVBLU(R)) in routine clinical practice. MethodsElectronic medical records from the Retina Consultants of America database of patients receiving aflibercept-ayyh (12/1/2024-10/31/2025) were analyzed, focusing on eyes with [&ge;]84 days of follow-up. The index date was the first documented aflibercept-ayyh injection. Postindex data were used to assess treatment patterns, BRVA (Wilcoxon signed rank test), and adverse events of special interest (AESIs). ResultsA total of 1,000 consecutive eyes from 989 patients received 3,730 injections of aflibercept-ayyh; most (91%) switched from prior anti-VEGF therapy and 9% were anti-VEGF treatment-naive. Disease distribution was 58% nAMD, 19% RVO, 16% DME, and 7% DR. Among switchers, median (IQR) number of prior injections was 21 (8-46). Median (IQR) follow-up was 6.0 months (4.6-7.1). Median (IQR) number of aflibercept-ayyh injections per eye was 4 (3-5). Among eyes with [&ge;]84 days of follow-up (n=889), mean BRVA expressed as logarithm of minimum angle of resolution (logMAR) remained stable for switchers (0.4 to 0.4; P=0.96) and improved from baseline in anti-VEGF-naive eyes (0.5 to 0.4; P<0.01). Confirmed AESIs included iritis (n=2; 0.05% of injections), with no events of vitreous cells, endophthalmitis, retinal detachment, retinal vasculitis, or vitreous hemorrhage. ConclusionIn this descriptive real-world analysis, aflibercept-ayyh was associated with stable visual acuity in previously treated eyes and vision improvement in treatment-naive eyes, with no new or unexpected safety findings, consistent with expectations for aflibercept. These findings add real-world experience to preexisting evidence demonstrating no clinically meaningful differences between aflibercept-ayyh (PAVBLU(R)) and reference aflibercept (EYLEA(R)). KEY SUMMARY POINTSO_ST_ABSWhy carry out this study?C_ST_ABSO_LIThe anti-vascular endothelial growth factor (VEGF) drug aflibercept, approved in 2011 and marketed in the United States as EYLEA(R),* has demonstrated efficacy in treating retinal diseases such as neovascular age-related macular degeneration (nAMD), diabetic retinopathy (DR), diabetic macular edema (DME), or retinal vein occlusion (RVO) and is a standard of care for these disorders. C_LIO_LIAflibercept-ayyh is a biosimilar to aflibercept that has demonstrated comparable efficacy and safety in the treatment of nAMD in a randomized controlled clinical trial. C_LIO_LIThis study describes the real-world use patterns, vision outcomes, and safety of aflibercept-ayyh in clinical settings in the United States for the treatment of nAMD, DR, DME, and RVO. C_LI What was learned from the study?O_LIIn this real-world study of 1,000 consecutive eyes treated with the biosimilar aflibercept-ayyh in patients with retinal diseases, we observed no new safety concerns and that aflibercept-ayyh maintained visual acuity in eyes switching anti-VEGF agents and improved vision in anti-VEGF-naive eyes, consistent with expected responses to aflibercept. C_LIO_LIThese findings support aflibercept-ayyh as a suitable treatment option when anti-VEGF therapy is indicated. *EYLEA(R) is a registered trademark of Regeneron Pharmaceuticals, Inc. PAVBLU(R) is a registered trademark of Amgen Inc. C_LI

PurposeTimely detection of disease activity in chronic retinal diseases improves visual outcomes but is limited by the lack of validated systems for continuous monitoring and care management. We evaluated the real-world performance of an integrated remote physiologic monitoring and principal care management program (RemoniHealth(R)) using a self-administered multimodal retinal function test (Macustat(R)) for home monitoring. MethodsThis single-arm real-world intervention study was conducted across 33 retina practices. A total of 2,216 adults with chronic retinal diseases performed weekly home retinal function testing with integrated care management support. Primary endpoints included the annualized rate of disease progression detection, time to intervention after first flag, true positive rate, and patient adherence. Descriptive statistics and data analyses were analyzed using chi-square tests and Clopper-Pearson confidence intervals. ResultsParticipants contributed 82,644 encounters and 16,805 patient-months of monitoring. The program generated 241 alerts, including 101 Macustat flags and 135 care management prompts. Among 73 adjudicated flags, 56 were true positives and 17 false positives (PPV 76.7%). The annualized detection rate was 4 per 100 patient-years. Of confirmed events, 93% led to intravitreal injection or other major management change. Mean adherence was 72.1%, and patients with [&ge;]80% adherence had higher odds of true positivity. DiscussionThis RPM-PCM model achieved high engagement and meaningful detection of asymptomatic progression between visits, supporting the value of home monitoring for timely intervention. Translational RelevanceThese findings support scalable integration of home vision testing and care management into routine retinal practice to enable earlier intervention and improved continuity of care.

PurposeQuantitative metrics obtained from retinal fundus images (such as vessel length, tortuosity and other scale-dependent measures) are increasingly used as potential biomarkers for systemic diseases, including cardio- and neurovascular conditions. However, with the increasing prevalence of myopia and related axial growth, this study aims to evaluate if axial length scaling significantly alters the overall distributions of the inferred biomarkers when compared to biomarker data obtained without axial length scaling and if these effects can be corrected. Methods2,309 clinic visits from patients aged [&le;]21 years were analysed and extracted for axial-length scaling analysis (range) 20 to 28 mm). The retinal fundus photographs were automatically segmented using Automorph to extract biometric data, including vascular metrics. The parameters were further corrected for axial length using correction factors based on the Bennett-Littmann formula and true axial length. ResultsAxial length significantly influenced biometric parameters (vessel metrics) derived from fundus photography. The magnitude of error in diameter and length of blood vessels was approximately 4-5% for each 1 mm deviation from the reference axial length of 24 mm, whereas the error in vessel area was approximately 9-10% per 1 mm, consistent with the geometric expectation that area scales with the square of linear dimensions. The scaling corrections for different axial lengths are presented. ConclusionsAxial-length-related magnification introduces systematic bias into retinal vascular metrics from fundus photographs. Bennett-Littmann correction using true axial length reduces these errors and should be adopted in quantitative fundus imaging and Al biomarker development.

BackgroundThe visual field (VF) test results of many eyes with glaucoma progress despite treatment. This suggests that some eyes are either untreated or that the management of intraocular pressure (IOP) does not influence the outcome. In this work, we explore whether future VF parameters can be predicted from a baseline optical coherence retinal nerve fibre layer (OCT-RNFL) scan using a deep learning model. MethodsThe model was developed using 1792 eyes from 1610 patients, and externally validated on 151 eyes from a second centre using the same Zeiss Cirrus machine and 281 eyes from a third centre using scans obtained from a different (Heidelberg Spectralis) machine. The Vision Transformers (ViT)-based regression model was trained on baseline OCT-RNFL scans to predict three key VF indices (follow-up interval: 4.74 {+/-} 2.59 years). Model performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), with 95% confidence intervals (CI). ResultsThe model achieved an overall MAE of 2.07 (95% CI: 1.91-2.22) and RMSE of 2.87 (95% CI: 2.60-3.14) on the internal validation set. On external validation, the model showed comparable performance with an MAE of 2.07 (95% CI: 1.8-2.35) for the external validation (Zeiss OCT) cohort and 2.11 (95% CI: 1.93-2.31) for the external validation (Heidelberg OCT) cohort. Saliency maps revealed that the inner and outer RNFL layers were key structures in driving the models predictions. ConclusionsOur ViT-based regression model effectively predicts key VF indices objectively from a single OCT-RNFL scan, with strong performance across two OCT devices, offering a novel tool for predicting glaucoma progression.

PurposeTo investigate the effects of morning and evening narrowband blue light exposure on axial length, and to examine the short-term effect of morning blue light combined with myopic defocus on axial length. MethodsFor objective 1, 18 individuals underwent 60 minutes of narrowband blue light exposure (460nm) in the morning (9:00-11:00AM) and evening (5:00-7:00PM) of the same day. The axial length values were normalized to the average of the morning and evening axial length values. For objective 2, 27 young adults were exposed to 60 minutes of narrowband blue light and broadband white light while wearing a +3.00 D lens over the right eye. Axial length was measured using Lenstar LS900. ResultsA significant reduction in axial length was observed after exposure to morning blue light compared to evening blue light (-10.0{+/-}3.96{micro}m vs.-0.67{+/-}3.30{micro}m; p=0.02), whereas no such effect was observed with broadband white light exposure (0.0{+/-}3.53 {micro}m vs. -2.50{+/-}4.23{micro}m, p=0.70). While the broadband white light exposure did not alter the normal diurnal variation in axial length (+2.35{+/-}1.82{micro}m vs.-6.25{+/-}2.21{micro}m, p=0.04), blue light diminished such a pattern (-4.12{+/-}1.72{micro}m vs. - 2.00{+/-}2.00{micro}m, p=0.48). The myopic defocus did not influence axial length under either narrowband blue or broadband white light conditions. ConclusionThe short-term narrowband blue light exposure led to a significant decrease in axial length in the morning than evening exposure, with a likely influence on the diurnal rhythm of axial length. Morning blue light exposure with lens-induced myopic defocus did not provide additional short-term modulation of axial length.

PurposeTo systematically evaluate ocular biometric and systemic laboratory factors associated with cataract in highly myopic eyes and to characterize potential nonlinear associations using an interpretable machine learning approach, thereby providing deeper mechanistic insights into the pathogenesis of highly myopic cataract. DesignA cross-sectional study encompassed 770 eyes of 594 patients with high myopia from Eye & ENT Hospital of Fudan University. SubjectsThe non-cataract control group included 458 eyes while the cataract group contained 312 eyes. MethodsDemographic traits, ocular biometric and systemic laboratory factors were gathered while features with over 30% of missing data were excluded. Composite indices were obtained through calculation. Multiple machine learning models were compared to investigate the association between features and highly myopic cataract, and the random forest (RF) model was chosen and fine-tuned. Feature selection was carried out by means of Shapley additive explanations (SHAP) and non-linear relationships were probed using SHAP dependence diagrams and confirmed with partial dependence plots. Main Outcome Measures(1) The Area Under the Curve (AUC) and other metrics of multiple machine learning models; (2) Top feature importance of the final simplified RF model; (3) Overall trends between features and highly myopic cataract; (4) Potential inflection points of top continuous features. ResultsA simplified fine-tuned RF model with 17 features reached stable discriminative performance, with a mean AUC of 0.762 (95%CI: [0.731, 0.794]) among 10 independent testing sets. Age and axial length (AL) turned out to be the most influential features which had non-linear relationships highly myopic cataract, with an inflection point seen around 65.75 (95%CI: [63.72, 67.79]) years for age and 30.55 (95% CI: [29.22, 31.88]) mm for axial length respectively, while the ratio of anterior chamber depth to axial length (ACD/AL) was associated with highly-myopic cataract in a U-shape. Ocular biometric factors were more strongly related to highly myopic cataract than systemic laboratory factors. ConclusionsOcular biometric factors, especially age, AL, and composite indices like ACD/AL, have strong and non-linear connections with highly myopic cataract. These results emphasize the significance of ocular structural arrangement in cataract within highly myopic eyes and indicate that interpretable data-driven methods could offer clinically relevant understandings regarding its phenotypic description.

Primary open-angle glaucoma (POAG) disproportionately affects individuals of African ancestry, yet rare coding variation in this population remains understudied. To address this gap, we performed a multi-cohort exome-wide meta-analysis across POAAGG, PMBB, All of Us, and UK Biobank, including 4,815 POAG cases and 22,922 controls of genetically inferred African ancestry. Although no gene reached exome-wide significance, we identified several suggestive gene-level associations driven by rare variants (minor allele frequency [&le;]0.1% or singletons),including signals in SRF, BLTP3A, METTL2A, and KRT10. Among these, SRF demonstrated the strongest association and was driven by rare missense variants with moderate effect sizes. Given its role in cytoskeletal organization and actin dynamics; processes central to trabecular meshwork function and intraocular pressure regulation SRF represents a biologically plausible candidate gene. Notably, these genes have not been previously highlighted in predominantly European ancestry POAG association studies, suggesting potential ancestry-specific rare variant contributions. Overall, our findings highlight the critical importance of investigating rare coding variation in POAG, in disproportionately affected populations to deepen understanding of POAG etiology and genetic risk.

Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss in ageing populations, with oxidative stress recognised as a key pathogenic driver. The dietary antioxidant and cytoprotectant, L-ergothioneine (ET), is avidly accumulated in many tissues, especially the eye. However its relationship to AMD has not been investigated. Here, we examined ETs distribution in ocular tissue and assessed circulating and intraocular ET levels in patients with neovascular AMD. Compared with ocularly-normal age-matched individuals, AMD patients exhibited significantly lower serum ET; elevated levels of ET metabolites, hercynine and ETSO, which may be generated by oxidative stress; and elevated levels of serum allantoin, a product of oxidative damage to urate in humans. Levels of ET in aqueous humour in AMD patients were marginally lower than cataractous patients who are already known to have significantly lower ET levels than healthy eyes. High ET levels were seen in human ocular tissues concentrating in regions vulnerable to oxidative injury, including the lens, retina, retinal pigment epithelium, and choroid, supporting a physiological protective role of ET in the eye. These findings identify the strong association between low ET levels and AMD, warranting further studies to determine whether ET supplementation can modify AMD risk or progression.

ImportanceGenome-wide association studies have identified hundreds of common single nucleotide polymorphisms (SNPs) and small insertions/deletions (indels) associated with primary open-angle glaucoma (POAG) risk, though these variants have modest effect sizes and individually may have minor contributions to disease development. As whole-genome sequencing data is becoming more readily available, structural variants and other complex genomic features can be interrogated for contribution to disease risk. ObjectiveTest the association of structural variants in known glaucoma loci with disease risk. DesignCross-sectional study. SettingA multicenter cohort of individuals from the United States who contributed genomic and electronic health record data to the All of Us Research Program. ParticipantsPOAG case/control cohorts were generated in the All of Us Researcher Workbench using age (>40 for cases, >65 for controls) and ICD 9/10 diagnosis codes. Main Outcomes and MeasuresLogistic regression analyses adjusted for age, sex, and the top 10 principal components of ancestry were used to test association of structural variants within 500 kilobases of 309 known open-angle glaucoma risk loci. The significance threshold after Bonferroni correction was set at p<1.6x10-4. Results516 POAG cases and 18,716 controls of European ancestry from the All of Us v8 data release were included in the analysis. Mean age was 77.0 years among cases and 74.7 years among controls. Females comprised 45.7% of cases and 56.5% of controls. An 8,732 base pair deletion upstream of PITX2 (chr4:110680827-110689558) was associated with 7.3-fold higher odds of POAG (95% confidence interval: 2.9-18.5, p= 2.4x10-5, variant carrier frequency= 1.6% in cases and 0.25% in controls). Functional annotation identified multiple enhancers overlapping the deletion, suggesting that this structural variant likely impacts gene regulation and expression. Conclusion and RelevanceWhole genome sequencing data captures rare structural variants with large effect sizes that are missed by conventional SNP and indel genotyping approaches, enabling improved POAG risk stratification. These data also expand the phenotypic spectrum of structural variation in the PITX2 locus from childhood glaucoma to adult-onset disease, where age at diagnosis and clinical severity may be influenced by the extent of disrupted regulatory elements.

PurposeThere is a need for novel therapies for diabetic retinopathy (DR) because existing therapies treat only certain features of DR and do not work optimally for all patients. While proteomic studies provide insight into disease pathobiology, they are often limited to small sample sizes due to high costs, limiting their generalizability and reproducibility. Moreover, they often yield lists of tens to hundreds of proteins with differential expression, making it difficult to prioritize the most biologically relevant biomarkers beyond using arbitrary fold-change and false-detection rate cutoffs. Here, we applied a two-stage multimodal AI approach: first, we integrated EHR and proteomics data to rationally prioritize candidate protein biomarkers and, next, validated these biomarkers in an independent cohort. These protein biomarkers of DR are rooted in the EHR data and thereby more likely to be biological drivers of disease. MethodsWe obtained EHR data from a large number of patients with and without DR (N=319,997) from the STARR-OMOP database and obtained aqueous humor liquid biopsies from a subset of these patients (N=101) for high-resolution proteomic profiling. We developed Clinical and Omics Multi-Modal Analysis Enhanced with Transfer Learning (COMET) to perform integrated analysis of proteomics and all available EHR data to identify protein biomarkers of DR. The model was trained in two phases: first, it was pretrained using patients with EHR data alone (N=319,896), and then, it was fine tuned using patients with both EHR and proteomics data (N=101), allowing it to learn both clinical and molecular features associated with DR. Findings from COMET were then validated with liquid biopsies from an independent, validation cohort (N=164). Resultst-distributed stochastic neighbor embedding (t-SNE) analysis of EHR and proteomics data identified proteins clustering with related EHR features. Levels of STX3 and NOTCH2, proteins involved in retinal function, were correlated with a diagnosis of macular edema, a record of a visual field exam, and a prescription for latanoprost, highlighting protein-EHR alignment. The pretrained, multimodal COMET model was superior (AUROC=0.98, AUPRC=0.91) compared to models generated using either EHR or proteomics data alone or without pretraining (AUROC: 0.76 to 0.92; AUPRC: 0.47 to 0.74). The proteins SERPINE1, QPCT, AKR1C2, IL2RB, and SRSF6 were prioritized by the COMET model compared to the models without pretraining, supporting their potential role in DR pathobiology, and were subsequently validated in an independent cohort. ConclusionWe used multimodal AI to prioritize protein biomarkers of DR that are most strongly linked to EHR elements, as well as identifying other protein biomarkers associated with disease features like diabetic macular edema. These findings serve as a foundation for future mechanistic studies and highlight the synergistic value of using multimodal AI to fuse EHR and proteomics data for enhanced proteomics analysis.

Regular optical coherence tomography (OCT) monitoring is essential for early detection of retinal disease and timely intervention, but frequent clinicbased imaging burdens patients and healthcare systems. Home-based OCT enables continuous monitoring and reduces clinic visits; however, compact optics and patient-operated acquisition introduce noise, reduced resolution, motion blur, and artifacts that limit clinical reliability and diagnostic confidence. To model home-based OCT acquisition, we employ simulated data reflecting images from Siloton, a compact home-based OCT device. Clinically realistic noise and acquisition artifacts were applied to high-quality OCT images using Silotons simulation software, generating near-real patient-operated scans. Building on this dataset, we propose HAGAN, a Hybrid Attention Generative Adversarial Network developed through a progressive strategy, evolving from a baseline U-Net to an adversarial framework with hybrid attention. The best-performing U-Net architecture, EfficientNet-B1, identified through evaluation and ablation studies, is adopted as the generator. The generator incorporates attention gates at its skip connections and self-attention modules within the decoder, and is paired with a VGG19-based discriminator to form the HAGAN architecture. The model is trained using a multiobjective loss combining pixel-wise, structural, perceptual, edge-preserving, and adversarial components. Experiments on simulated home-based OCT data demonstrate that HAGAN consistently outperforms baseline and state-of-the-art models across standard enhancement metrics and a clinically relevant retinal layer segmentation downstream task, improving visual quality and preservation of diagnostically meaningful anatomical structures. These findings support the potential of HAGAN for reliable enhancement in future home-based OCT platforms, enabling remote retinal monitoring and reducing reliance on in-clinic imaging and routine hospital visits. HighlightsO_LIEnhancing the quality of home-based OCT images to support remote retinal monitoring and reduce the need for frequent referrals to clinical imaging centers C_LIO_LIProposing HAGAN, a hybrid attention generative adversarial network for enhancing OCT images acquired using the Siloton home-based OCT device C_LIO_LIHybrid attention design combining attention gates and self-attention to preserve fine retinal details and global anatomical consistency C_LIO_LIAdversarial learning framework improving perceptual realism and preservation of diagnostically relevant retinal structures in low-quality homeacquired OCT images C_LIO_LIProgressive model development from baseline U-Net to hybrid attention GAN, demonstrating systematic and measurable performance improvements C_LIO_LIClinical relevance validated through downstream retinal layer segmentation, confirming preservation of diagnostically important structures C_LI

Early screening for glaucoma and diabetic retinopathy (DR) is critical to prevent irreversible vision loss, yet remains inaccessible to many underserved populations. However, AI models trained on hospital-grade fundus images often generalize poorly to low-cost images acquired with portable devices such as smartphones. We proposed CausalFund, a causality-inspired learning framework for training AI models that enable reliable low-resource screening from easily acquired non-clinical images. CausalFund disentangles disease-relevant retinal features from spurious image factors to achieve domain-generalizable screening across clinical and non-clinical settings. We integrated CausalFund with seven deep learning backbones for glaucoma and DR screening from portable-device fundus images, including lightweight architectures suitable for on-device deployment. Across diverse experimental settings and image quality conditions, CausalFund consistently improved AUC and achieved a more favorable sensitivity-specificity trade-off than conventional deep learning baselines. As a model-agnostic framework, CausalFund could be extended to other diseases and low-resourced scenarios characterized by degraded or non-standard imaging.

Staphylococcus aureus plays a central role in the exacerbation of atopic dermatitis (AD), but the population structure and pathogenic determinants of strains colonizing AD patients remain poorly understood. It is unclear whether these strains mirror those circulating in the general community or whether specific clonal lineages are selectively adapted to the AD skin microenvironment. Data addressing this question are scarce, particularly in Portugal. In this study, we investigated the molecular epidemiology and pathogenic traits of S. aureus colonizing skin lesions in adult patients with AD in Portugal. We found that lesion-associated isolates belonged predominantly to the methicillin-susceptible S. aureus MSSA-ST398 clonal type, a lineage that is widely circulating in the Portuguese community, particularly among vulnerable populations, and that has also been implicated in severe human infections. Notably, isolates from this clonal type in AD harboured specific pathogenicity traits associated with skin barrier disruption, including hemolysin and urease production, which may contribute to their success as colonizers in AD. Our findings highlight that S. aureus colonization in AD arises from a dynamic interplay between community-level molecular epidemiology and disease-specific selective pressures. While circulating lineages provide the genetic background diversity, the AD skin microenvironment appears to shape which clones ultimately become dominant. Such an integrated perspective may help to inform future geographically tailored strategies aimed at limiting bacterial burden and preventing disease exacerbation in AD.

ObjectivesDigital biomarkers offer scalable screening for type 2 diabetes, yet adoption is stalled by uncertainty regarding economic viability. This study evaluates the cost-effectiveness and budget impact of digital screening compared to opportunistic screening from a Swiss payer perspective. MethodsA probabilistic Markov cohort model was developed to simulate at-risk Swiss adults (age [&ge;]45, BMI [&ge;]25 kg/m{superscript 2}) over a 40-year horizon. The model incorporates a digital attrition parameter, inputs derived from Swiss-specific sources (e.g., the CoLaus study and FSO life tables), and statutory tariffs. Costs and outcomes were discounted at 3.0%. ResultsIn the deterministic base-case, digital screening yielded an incremental cost-effectiveness ratio of CHF 2,912 per quality-adjusted life-year gained. Probabilistic sensitivity analysis indicated a 93.2% probability of cost-effectiveness at the CHF 50,000 threshold. The budget impact analysis estimated a Year 1 gross investment budget of CHF 27 million to identify prevalent cases, followed by long-term savings from averted complications. ConclusionsDigital screening can be highly cost-effective in Switzerland. While the required Year 1 gross investment poses a liquidity challenge, reimbursement via pathway-oriented models under the Swiss tariff could align incentives with long-term complication avoidance.

BackgroundQuadruple therapy, comprising an angiotensin receptor-neprilysin inhibitor (ARNI), {beta}-blocker, mineralocorticoid receptor antagonist (MRA), and sodium-glucose cotransporter 2 inhibitor (SGLT2i), is guideline-recommended for heart failure with reduced ejection fraction (HFrEF). However, uptake in Singapore remains low. This study evaluated the cost-effectiveness of scaling up quadruple therapy from the current 30% uptake to realistic (80%) and stretch (100%) targets. MethodsWe developed a decision-analytic model combining a decision tree and Markov structure to simulate clinical and economic outcomes over a 10-year horizon from the Singapore healthcare system perspective. Transition probabilities were estimated using local real-world data for current regimens, and published literature for quadruple therapy. Costs were derived from hospital billing data and drug utilisation patterns. A probabilistic sensitivity analysis (1,000 simulations) assessed uncertainty. The willingness-to-pay (WTP) threshold was S$45,000 per quality-adjusted life year (QALY) gained. ResultsBoth scale-up scenarios were cost-effective. Compared to current practice, the 80% uptake scenario resulted in an incremental cost of S$2.57M and 110 additional QALYs (ICER: S$23,392/QALY) for 1000 patients over 10 years, while the 100% uptake scenario yielded 137 QALYs at an incremental cost of S$2.88M (ICER: S$21,117/QALY). Under conservative assumptions, both scenarios remained cost-effective. The probability of being cost-effective was 92% (80% uptake) and 96% (100% uptake). InterpretationScaling up quadruple therapy for HFrEF in Singapore is highly cost-effective. Implementation strategies to close the treatment gap should be prioritised to improve outcomes and maximise value in heart failure care.

BackgroundThe Most Favored Nation (MFN) policy is a mechanism that incorporates foreign prices to determine the maximum allowable net price for any branded drug within US government-funded healthcare. Two proposed rules, the Global Benchmark for Efficient Drug Pricing ("GLOBE") (90 Fed. Reg. 60,244) for Medicare Part B and the Guarding US Medicare Against Rising Drug Costs ("GUARD") (90 Fed. Reg. 60,338) for Medicare Part D, invoke the Center for Medicare and Medicaid Innovation Centers payment and service model demonstration and waiver authority, under Section 1115A of the Social Security Act (42 U.S.C. [&#167;] 1315a), to calculate the US MFN price which is the lowest average price within a basket of specified foreign countries. Unlike voluntary manufacturer agreements, GLOBE and GUARD would mandate participation from all applicable manufacturers. MethodsWe derive MFNs potential impact on Medicare pricing from a proprietary dataset provided by IQVIA which contained net prices for the top 37 oncology products by total US sales from January 1, 2019 through June 30, 2025 ranked by total US sales in the following countries: Australia, Belgium, France, Germany, Ireland, Italy, South Africa, Spain, Switzerland, the UK, and the US. For each drug, we select the lowest GDP-adjusted international price from a basket of those countries within 60% of the US GDP per capita, adjusted for purchasing power parity, and calculate the reduction in US price required to match its MFN price, and hence the corresponding reduction in revenues under MFN. A retrospective Net Present Value (NPV) analysis is then used to address the counterfactual question of whether each drug would have been developed had MFN pricing been in place at the time of its FDA approval. ResultsUnder MFN, the average reduction in US prices across our drug cohort was 67%. Eighty-four percent of the 37 cancer drugs in our cohort evidenced a negative NPV if MFN had been in place at the time of their FDA approval and the commercial market is impacted. When the analysis is restricted to MFNs impact on Medicare, the indications for these lost drugs have a total US population of 2.4 million patients. When the analysis is combined across the Medicare and commercial markets, the loss of lead indications impacts over 15 million US patients. ConclusionsMandatory MFN policies reduce the financial incentives required to develop cancer medicines; our projections show a substantial decline in new cancer drug launches and will likely lead companies to pursue indications for populations outside Medicares authority. If so, MFN will reduce the number of new therapies for the very population the Executive Orders are allegedly designed to aid: the Medicare-aged population who require effective new therapies in areas of high unmet medical need, such as late-stage cancers. This creates the perverse outcome of a policy nominally designed to help Medicare beneficiaries by instead redirecting innovation away from their most urgent therapeutic needs.

The risk of esophageal adenocarcinoma (EAC) in Barretts esophagus (BE) varies substantially by segment length and dysplasia grade. This study evaluated the cost-effectiveness and health impacts of dysplasia-stratified EAC surveillance strategies for the Japanese BE population. A state-transition model was developed comparing endoscopy, sponge test, breath test, and miRNA test with no surveillance from a healthcare payer perspective over a lifetime. Non-invasive strategies were assessed as primary surveillance tools, with positive results triggering confirmatory endoscopy, and a scenario analysis evaluated AI-assisted endoscopy. Five BE populations of 50-year-old individuals were modeled: ultra-short segment BE (USSBE), short-segment BE (SSBE), long-segment nondysplastic BE (LSBE-NDBE), LSBE with low-grade dysplasia (LSBE-LGD), and LSBE with high-grade dysplasia (LSBE-HGD). Each modality was evaluated at surveillance intervals of 1, 2, 3, 4, 5, or 10 years. Primary outcomes included net monetary benefits, costs, quality-adjusted life-years, incremental cost-effectiveness ratios, and EAC deaths, with sensitivity analyses assessing parameter uncertainty. Surveillance was not cost-effective for USSBE, SSBE, or LSBE-NDBE. For LSBE-LGD, annual endoscopy was most cost-effective, averting 83 EAC deaths per 10,000 individuals, while for LSBE-HGD, annual breath testing was most cost-effective, averting 295 deaths. These findings support dysplasia-specific surveillance in LSBE with implications for global surveillance practice.

BackgroundThe United Nations General Assembly High-level Meeting on Antimicrobial Resistance (UNGA HLM-AMR) committed to a target that 70% of global human antibiotic use (ABU) should be from the Access group of the WHO AWaRe system. MethodsWe used 2019 IQVIA MIDAS(R) global ABU Quarterly value sales, volumes (kg/SU) and average ex-manufacturer prices to evaluate price per daily defined dose (DDD) by AWaRe group across countries. IQVIA MIDAS volumes/value data reflect public, private, or mixed sectors. We estimated potential national pharmaceutical expenditure savings if i) the UNGA 70% Access target was met, and ii) national ABU aligned with the WHO Model List of Essential Medicines (EML). We evaluated 7-day treatment prices for common oral and parenteral antibiotics across AWaRe groups. We measured affordability in middle-income countries (MICs) by income group, as the percentage of the population at risk of falling below national poverty lines if paying out-of-pocket, using income distributions and generalised beta distributions of the second kind. Prices were reported in 2019 international dollars (I$). ResultsVolume-weighted ex-manufacturer prices per DDD were lower for Access (I$1{middle dot}2, IQR I$0{middle dot}7) than Watch (I$2{middle dot}6, IQR I$2{middle dot}1) and highest (I$83{middle dot}8, IQR I$80{middle dot}9) for Reserve antibiotics. Lower prices were seen in high-income countries for Access antibiotics. Meeting the 70% Access target could save countries I$0{middle dot}1 million-I$4{middle dot}9 billion annually. Global savings could reach I$10{middle dot}4 billion if only WHO EML-listed antibiotics were used. Seven-day parenteral meropenem could put 7% (IQR 9%) of the population in MICs at risk of impoverishment. ConclusionAntibiotic policies focused on achieving the UNGA-AMR 70% Access target could generate significant potential national and global expenditure savings. FundingThis work was supported by the Wellcome Trust (304681/Z/23/Z) as part of the Antibiotic Data to Inform Local Action (ADILA) project and the Global Antibiotic Policy initiative (GAPi) project (RES 2024-495).

ObjectivesTo examine associations between cardiometabolic conditions and health-related quality of life (HRQoL) and to evaluate whether condition-associated HRQoL changed from 2001 to 2022. MethodsWe analyzed nationally representative data from U.S. adults aged [&ge;]18 years in the Medical Expenditure Panel Survey, 2001-2022. Survey years without BMI data (2017, 2019, 2021) were excluded. EQ-5D utilities were mapped from SF-12 scores using a validated algorithm. For each survey year, survey-weighted multivariable regression models estimated associations of sociodemographic characteristics, BMI, and cardiometabolic conditions (diabetes, heart disease, high blood pressure, high cholesterol, obesity, stroke) with HRQoL measured by EQ-5D. Temporal changes in condition-associated HRQoL decrements were assessed using meta-regression across years. Associations in recent survey years were summarized using pooled estimates from 2015, 2016, 2018, and 2022. ResultsOverall HRQoL improved from 2001 to 2022 across age groups, with the largest improvement among older adults. In pooled analyses, stroke was associated with the largest adjusted HRQoL decrement (-0.0714), followed by heart disease (-0.0503), diabetes (-0.0427), high blood pressure (-0.0328), obesity (-0.0305), and high cholesterol (-0.0236). Additional adjustment for BMI attenuated condition-associated decrements, most notably for obesity (-0.0305 to -0.0183), diabetes (-0.0427 to -0.0414), and high blood pressure (-0.0328 to -0.0316). Over time, diabetes- and heart disease-associated decrements attenuated linearly (diabetes: - 0.0489 in 2001 to -0.0406 in 2022; heart disease: -0.0591 to -0.0493). High blood pressure (-0.0337 in 2001, -0.0415 in 2012, -0.0306 in 2022) and obesity (-0.0305 in 2001, -0.0283 in 2012, -0.0367 in 2022) showed nonlinear patterns. ConclusionsCondition-associated HRQoL decrements varied over time, and recent-year utility estimates are recommended for population health research. HRQoL decrements for diabetes and heart disease attenuated, consistent with improvements in treatment and survival. High blood pressure-associated were lowest around 2012, and obesity-associated became more negative after 2012, consistent with worsening blood pressure control and obesity severity.