Effects of rare kidney diseases on kidney failure: a longitudinal analysis of the UK National Registry of Rare Kidney Diseases (RaDaR) cohort.

BACKGROUND: Individuals with rare kidney diseases account for 5-10% of people with chronic kidney disease, but constitute more than 25% of patients receiving kidney replacement therapy. The National Registry of Rare Kidney Diseases (RaDaR) gathers longitudinal data from patients with these conditions, which we used to study disease progression and outcomes of death and kidney failure. METHODS: People aged 0-96 years living with 28 types of rare kidney diseases were recruited from 108 UK renal care facilities. The primary outcomes were cumulative incidence of mortality and kidney failure in individuals with rare kidney diseases, which were calculated and compared with that of unselected patients with chronic kidney disease. Cumulative incidence and Kaplan-Meier survival estimates were calculated for the following outcomes: median age at kidney failure; median age at death; time from start of dialysis to death; and time from diagnosis to estimated glomerular filtration rate (eGFR) thresholds, allowing calculation of time from last eGFR of 75 mL/min per 1·73 m2 or more to first eGFR of less than 30 mL/min per 1·73 m2 (the therapeutic trial window). FINDINGS: Between Jan 18, 2010, and July 25, 2022, 27 285 participants were recruited to RaDaR. Median follow-up time from diagnosis was 9·6 years (IQR 5·9-16·7). RaDaR participants had significantly higher 5-year cumulative incidence of kidney failure than 2·81 million UK patients with all-cause chronic kidney disease (28% vs 1%; p<0·0001), but better survival rates (standardised mortality ratio 0·42 [95% CI 0·32-0·52]; p<0·0001). Median age at kidney failure, median age at death, time from start of dialysis to death, time from diagnosis to eGFR thresholds, and therapeutic trial window all varied substantially between rare diseases. INTERPRETATION: Patients with rare kidney diseases differ from the general population of individuals with chronic kidney disease: they have higher 5-year rates of kidney failure but higher survival than other patients with chronic kidney disease stages 3-5, and so are over-represented in the cohort of patients requiring kidney replacement therapy. Addressing unmet therapeutic need for patients with rare kidney diseases could have a large beneficial effect on long-term kidney replacement therapy demand. FUNDING: RaDaR is funded by the Medical Research Council, Kidney Research UK, Kidney Care UK, and the Polycystic Kidney Disease Charity.

Homozygous SMAD6 variants in two unrelated patients with craniosynostosis and radioulnar synostosis.

BACKGROUND: SMAD6 encodes an intracellular inhibitor of the bone morphogenetic protein (BMP) signalling pathway. Until now, rare heterozygous loss-of-function variants in SMAD6 were demonstrated to increase the risk of disparate clinical disorders including cardiovascular disease, craniosynostosis and radioulnar synostosis. Only two unrelated patients harbouring biallelic SMAD6 variants presenting a complex cardiovascular phenotype and facial dysmorphism have been described. CASES: Here, we present the first two patients with craniosynostosis harbouring homozygous SMAD6 variants. The male probands, both born to healthy consanguineous parents, were diagnosed with metopic synostosis and bilateral or unilateral radioulnar synostosis. Additionally, one proband had global developmental delay. Echocardiographic evaluation did not reveal cardiac or outflow tract abnormalities. MOLECULAR ANALYSES: The novel missense (c.[584T>G];[584T>G], p.[(Val195Gly)];[(Val195Gly)]) and missense/splice-site variant (c.[817G>A];[817G>A], r.[(817g>a,817delins[a;817+2_817+228])];[(817g>a,817delins[a;817+2_817+228])], p.[(Glu273Lys,Glu273Serfs*72)];[(Glu273Lys,Glu273Serfs*72)]) both locate in the functional MH1 domain of the protein and have not been reported in gnomAD database. Functional analyses of the variants showed reduced inhibition of BMP signalling or abnormal splicing, respectively, consistent with a hypomorphic mechanism of action. CONCLUSION: Our data expand the spectrum of variants and phenotypic spectrum associated with homozygous variants of SMAD6 to include craniosynostosis.

Long-read sequencing to resolve the parent of origin of a de novo pathogenic UBE3A variant.

Background The ever-increasing capacity of short-read sequencing instruments is driving the adoption of whole genome sequencing (WGS) as a universal approach to the diagnosis of rare genetic disorders. However, many challenging genomic regions remain, for which alternative technologies must be deployed in order to address the clinical question satisfactorily. Methods Here we report the use of long-read sequencing to resolve ambiguity over a suspected diagnosis of Angelman syndrome. Results Despite a normal chromosomal microarray result and methylation studies at the imprinted 15q11q13 locus, the continued clinical suspicion of Angelman Syndrome prompted trio WGS of the proband and his parents. A de novo heterozygous frameshift variant, c.2370_2373del (NM_130838.2) p.(Asp790Glufs*7), in UBE3A was identified. To determine the parental allele on which this variant arose, long-read sequencing of the flanking genomic region was performed. Comparison of the resulting haplotypes allowed us to determine that the pathogenic frameshift variant arose on the maternal allele, confirming a diagnosis of Angelman syndrome in this case. Conclusion Long-read nanopore sequencing provides significant clinical utility when assessing the parental origin of de novo variants.

Uncovering the burden of hidden ciliopathies in the 100 000 Genomes Project: a reverse phenotyping approach.

BACKGROUND: The 100 000 Genomes Project (100K) recruited National Health Service patients with eligible rare diseases and cancer between 2016 and 2018. PanelApp virtual gene panels were applied to whole genome sequencing data according to Human Phenotyping Ontology (HPO) terms entered by recruiting clinicians to guide focused analysis. METHODS: We developed a reverse phenotyping strategy to identify 100K participants with pathogenic variants in nine prioritised disease genes (BBS1, BBS10, ALMS1, OFD1, DYNC2H1, WDR34, NPHP1, TMEM67, CEP290), representative of the full phenotypic spectrum of multisystemic primary ciliopathies. We mapped genotype data 'backwards' onto available clinical data to assess potential matches against phenotypes. Participants with novel molecular diagnoses and key clinical features compatible with the identified disease gene were reported to recruiting clinicians. RESULTS: We identified 62 reportable molecular diagnoses with variants in these nine ciliopathy genes. Forty-four have been reported by 100K, 5 were previously unreported and 13 are new diagnoses. We identified 11 participants with unreportable, novel molecular diagnoses, who lacked key clinical features to justify reporting to recruiting clinicians. Two participants had likely pathogenic structural variants and one a deep intronic predicted splice variant. These variants would not be prioritised for review by standard 100K diagnostic pipelines. CONCLUSION: Reverse phenotyping improves the rate of successful molecular diagnosis for unsolved 100K participants with primary ciliopathies. Previous analyses likely missed these diagnoses because incomplete HPO term entry led to incorrect gene panel choice, meaning that pathogenic variants were not prioritised. Better phenotyping data are therefore essential for accurate variant interpretation and improved patient benefit.

Antigen-driven PD-1+ TOX+ BHLHE40+ and PD-1+ TOX+ EOMES+ T lymphocytes regulate juvenile idiopathic arthritis in situ.

T lymphocytes accumulate in inflamed tissues of patients with chronic inflammatory diseases (CIDs) and express pro-inflammatory cytokines upon re-stimulation in vitro. Further, a significant genetic linkage to MHC genes suggests that T lymphocytes play an important role in the pathogenesis of CIDs including juvenile idiopathic arthritis (JIA). However, the functions of T lymphocytes in established disease remain elusive. Here we dissect the transcriptional and the clonal heterogeneity of synovial T lymphocytes in JIA patients by single-cell RNA sequencing combined with T cell receptor profiling on the same cells. We identify clonally expanded subpopulations of T lymphocytes expressing genes reflecting recent activation by antigen in situ. A PD-1+ TOX+ EOMES+ population of CD4+ T lymphocytes expressed immune regulatory genes and chemoattractant genes for myeloid cells. A PD-1+ TOX+ BHLHE40+ population of CD4+ , and a mirror population of CD8+ T lymphocytes expressed genes driving inflammation, and genes supporting B lymphocyte activation in situ. This analysis points out that multiple types of T lymphocytes have to be targeted for therapeutic regeneration of tolerance in arthritis.

Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT.

PURPOSE: To develop deep learning (DL) systems estimating visual function from macula-centered spectral-domain (SD) OCT images. DESIGN: Evaluation of a diagnostic technology. PARTICIPANTS: A total of 2408 10-2 visual field (VF) SD OCT pairs and 2999 24-2 VF SD OCT pairs collected from 645 healthy and glaucoma subjects (1222 eyes). METHODS: Deep learning models were trained on thickness maps from Spectralis macula SD OCT to estimate 10-2 and 24-2 VF mean deviation (MD) and pattern standard deviation (PSD). Individual and combined DL models were trained using thickness data from 6 layers (retinal nerve fiber layer [RNFL], ganglion cell layer [GCL], inner plexiform layer [IPL], ganglion cell-IPL [GCIPL], ganglion cell complex [GCC] and retina). Linear regression of mean layer thicknesses were used for comparison. MAIN OUTCOME MEASURES: Deep learning models were evaluated using R2 and mean absolute error (MAE) compared with 10-2 and 24-2 VF measurements. RESULTS: Combined DL models estimating 10-2 achieved R2 of 0.82 (95% confidence interval [CI], 0.68-0.89) for MD and 0.69 (95% CI, 0.55-0.81) for PSD and MAEs of 1.9 dB (95% CI, 1.6-2.4 dB) for MD and 1.5 dB (95% CI, 1.2-1.9 dB) for PSD. This was significantly better than mean thickness estimates for 10-2 MD (0.61 [95% CI, 0.47-0.71] and 3.0 dB [95% CI, 2.5-3.5 dB]) and 10-2 PSD (0.46 [95% CI, 0.31-0.60] and 2.3 dB [95% CI, 1.8-2.7 dB]). Combined DL models estimating 24-2 achieved R2 of 0.79 (95% CI, 0.72-0.84) for MD and 0.68 (95% CI, 0.53-0.79) for PSD and MAEs of 2.1 dB (95% CI, 1.8-2.5 dB) for MD and 1.5 dB (95% CI, 1.3-1.9 dB) for PSD. This was significantly better than mean thickness estimates for 24-2 MD (0.41 [95% CI, 0.26-0.57] and 3.4 dB [95% CI, 2.7-4.5 dB]) and 24-2 PSD (0.38 [95% CI, 0.20-0.57] and 2.4 dB [95% CI, 2.0-2.8 dB]). The GCIPL (R2 = 0.79) and GCC (R2 = 0.75) had the highest performance estimating 10-2 and 24-2 MD, respectively. CONCLUSIONS: Deep learning models improved estimates of functional loss from SD OCT imaging. Accurate estimates can help clinicians to individualize VF testing to patients.

Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps.

PURPOSE: To develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SD OCT) optic nerve head images. DESIGN: Evaluation of a diagnostic technology. PARTICIPANTS: A total of 9765 visual field (VF) SD OCT pairs collected from 1194 participants with and without GVFD (1909 eyes). METHODS: Deep learning models were trained to use SD OCT retinal nerve fiber layer (RNFL) thickness maps, RNFL en face images, and confocal scanning laser ophthalmoscopy (CSLO) images to identify eyes with GVFD and predict quantitative VF mean deviation (MD), pattern standard deviation (PSD), and mean VF sectoral pattern deviation (PD) from SD OCT data. MAIN OUTCOME MEASURES: Deep learning models were compared with mean RNFL thickness for identifying GVFD using area under the curve (AUC), sensitivity, and specificity. For predicting MD, PSD, and mean sectoral PD, models were evaluated using R2 and mean absolute error (MAE). RESULTS: In the independent test dataset, the deep learning models based on RNFL en face images achieved an AUC of 0.88 for identifying eyes with GVFD and 0.82 for detecting mild GVFD significantly (P < 0.001) better than using mean RNFL thickness measurements (AUC = 0.82 and 0.73, respectively). Deep learning models outperformed standard RNFL thickness measurements in predicting all quantitative VF metrics. In predicting MD, deep learning models based on RNFL en face images achieved an R2 of 0.70 and MAE of 2.5 decibels (dB) compared with 0.45 and 3.7 dB for RNFL thickness measurements. In predicting mean VF sectoral PD, deep learning models achieved high accuracy in the inferior nasal (R2 = 0.60) and superior nasal (R2 = 0.67) sectors, moderate accuracy in inferior (R2 = 0.26) and superior (R2 = 0.35) sectors, and lower accuracy in the central (R2 = 0.15) and temporal (R2 = 0.12) sectors. CONCLUSIONS: Deep learning models had high accuracy in identifying eyes with GFVD and predicting the severity of functional loss from SD OCT images. Accurately predicting the severity of GFVD from SD OCT imaging can help clinicians more effectively individualize the frequency of VF testing to the individual patient.

Fifteen-minute consultation: Pharmacogenomics: a guide for busy clinicians.

The ultimate goal of pharmacogenomics is to understand how the effects of interindividual genetic variation impact on the response of patients to treatments and in turn optimise those treatments while minimising potential side effects. This review article provides a brief overview of pharmacogenomics, focusing on the methods used to understand how genes of interest are identified, how pharmacogenomics is currently used within paediatric clinical practice within the UK, potential areas for future use and some of the caveats specific to paediatric medicine.

The African Descent and Glaucoma Evaluation Study (ADAGES) III: Contribution of Genotype to Glaucoma Phenotype in African Americans: Study Design and Baseline Data.

PURPOSE: To describe the study protocol and baseline characteristics of the African Descent and Glaucoma Evaluation Study (ADAGES) III. DESIGN: Cross-sectional, case-control study. PARTICIPANTS: Three thousand two hundred sixty-six glaucoma patients and control participants without glaucoma of African or European descent were recruited from 5 study centers in different regions of the United States. METHODS: Individuals of African descent (AD) and European descent (ED) with primary open-angle glaucoma (POAG) and control participants completed a detailed demographic and medical history interview. Standardized height, weight, and blood pressure measurements were obtained. Saliva and blood samples to provide serum, plasma, DNA, and RNA were collected for standardized processing. Visual fields, stereoscopic disc photographs, and details of the ophthalmic examination were obtained and transferred to the University of California, San Diego, Data Coordinating Center for standardized processing and quality review. MAIN OUTCOME MEASURES: Participant gender, age, race, body mass index, blood pressure, history of smoking and alcohol use in POAG patients and control participants were described. Ophthalmic measures included intraocular pressure, visual field mean deviation, central corneal thickness, glaucoma medication use, or past glaucoma surgery. Ocular conditions, including diabetic retinopathy, age-related macular degeneration, and past cataract surgery, were recorded. RESULTS: The 3266 ADAGES III study participants in this report include 2146 AD POAG patients, 695 ED POAG patients, 198 AD control participants, and 227 ED control participants. The AD POAG patients and control participants were significantly younger (both, 67.4 years) than ED POAG patients and control participants (73.4 and 70.2 years, respectively). After adjusting for age, AD POAG patients had different phenotypic characteristics compared with ED POAG patients, including higher intraocular pressure, worse visual acuity and visual field mean deviation, and thinner corneas (all P < 0.001). Family history of glaucoma did not differ between AD and ED POAG patients. CONCLUSIONS: With its large sample size, extensive specimen collection, and deep phenotyping of AD and ED glaucoma patients and control participants from different regions in the United States, the ADAGES III genomics study will address gaps in our knowledge of the genetics of POAG in this high-risk population.

Macular and Optic Nerve Head Vessel Density and Progressive Retinal Nerve Fiber Layer Loss in Glaucoma.

PURPOSE: To investigate prospectively the relationship between macular and peripapillary vessel density and progressive retinal nerve fiber layer (RNFL) loss in patients with mild to moderate primary open-angle glaucoma. DESIGN: Prospective, observational study. PARTICIPANTS: One hundred thirty-two eyes of 83 patients with glaucoma followed up for at least 2 years (average: 27.3±3.36 months). METHODS: Measurements of macular whole image vessel density (m-wiVD) and optic nerve head whole image vessel density (onh-wiVD) were acquired at baseline using OCT angiography. RNFL, minimum rim width (MRW), and ganglion cell plus inner plexiform layer (GCIPL) thickness were obtained semiannually using spectral-domain OCT. Random-effects models were used to investigate the relationship between baseline vessel density parameters and rates of RNFL loss after adjusting for the following confounding factors: baseline visual field mean deviation, MRW, GCIPL thickness, central corneal thickness (CCT), and mean intraocular pressure during follow-up and disc hemorrhage, with or without including baseline RNFL. MAIN OUTCOME MEASURES: Effects of m-wiVD and onh-wiVD on rates of RNFL loss over time. RESULTS: Average baseline RNFL thickness was 79.5±14.8 µm, which declined with a mean slope of -1.07 µm/year (95% confidence interval, -1.28 to -0.85). In the univariate model, including only a predictive factor and time and their interaction, each 1% lower m-wiVD and onh-wiVD was associated with a 0.11-µm/year (P < 0.001) and 0.06-µm/year (P = 0.031) faster rate of RNFL decline, respectively. A similar relationship between low m-wiVD and onh-wiVD and faster rates of RNFL loss was found using different multivariate models. The association between vessel density measurements and rate of RNFL loss was weak (r2 = 0.125 and r2 = 0.033 for m-wiVD and onh-wiVD, respectively). Average CCT also was a predictor for faster RNFL decline in both the univariate (0.11 µm/year; P < 0.001) and multivariate models. CONCLUSIONS: Lower baseline macular and optic nerve head (ONH) vessel density are associated with a faster rate of RNFL progression in mild to moderate glaucoma. Assessment of ONH and macular vessel density may add significant information to the evaluation of the risk of glaucoma progression and prediction of rates of disease worsening.

Geographical CO2 sensitivity of phytoplankton correlates with ocean buffer capacity.

Accumulation of anthropogenic CO2 is significantly altering ocean chemistry. A range of biological impacts resulting from this oceanic CO2 accumulation are emerging, however, the mechanisms responsible for observed differential susceptibility between organisms and across environmental settings remain obscure. A primary consequence of increased oceanic CO2 uptake is a decrease in the carbonate system buffer capacity, which characterizes the system's chemical resilience to changes in CO2 , generating the potential for enhanced variability in pCO2 and the concentration of carbonate [ CO32- ], bicarbonate [ HCO3- ], and protons [H+ ] in the future ocean. We conducted a meta-analysis of 17 shipboard manipulation experiments performed across three distinct geographical regions that encompassed a wide range of environmental conditions from European temperate seas to Arctic and Southern oceans. These data demonstrated a correlation between the magnitude of natural phytoplankton community biological responses to short-term CO2 changes and variability in the local buffer capacity across ocean basin scales. Specifically, short-term suppression of small phytoplankton (<10 µm) net growth rates were consistently observed under enhanced pCO2 within experiments performed in regions with higher ambient buffer capacity. The results further highlight the relevance of phytoplankton cell size for the impacts of enhanced pCO2 in both the modern and future ocean. Specifically, cell size-related acclimation and adaptation to regional environmental variability, as characterized by buffer capacity, likely influences interactions between primary producers and carbonate chemistry over a range of spatio-temporal scales.

Molecular phylogeny of the Achatinoidea (Mollusca: Gastropoda).

This study presents a multi-gene phylogenetic analysis of the Achatinoidea and provides an initial basis for a taxonomic re-evaluation of family level groups within the superfamily. A total of 5028 nucleotides from the nuclear rRNA, actin and histone 3 genes and the 1st and 2nd codon positions of the mitochondrial cytochrome c oxidase subunit I gene were sequenced from 24 species, representing six currently recognised families. Results from maximum likelihood, neighbour joining, maximum parsimony and Bayesian inference trees revealed that, of currently recognised families, only the Achatinidae are monophyletic. For the Ferussaciidae, Ferussacia folliculus fell separately to Cecilioides gokweanus and formed a sister taxon to the rest of the Achatinoidea. For the Coeliaxidae, Coeliaxis blandii and Pyrgina umbilicata did not group together. The Subulinidae was not resolved, with some subulinids clustering with the Coeliaxidae and Thyrophorellidae. Three subfamilies currently included within the Subulinidae based on current taxonomy likewise did not form monophyletic groups.

RetFM-J, an ImageJ-based module for automated counting and quantifying features of nuclei in retinal whole-mounts.

The present article introduces RetFM-J, a semi-automated ImageJ-based module that detects, counts, and collects quantitative data on nuclei of the inner retina from H&E-stained whole-mounted retinas. To illustrate performance, computer-derived outputs were analyzed in inbred C57BL/6J mice. Automated characterization yielded computer-derived outputs that closely matched manual counts. As a method using open-source software that is freely available, inexpensive staining reagents that are robust, and imaging equipment that is routine to most laboratories, RetFM-J could be utilized in a wide variety of experiments benefiting from high-throughput, quantitative, uniform analyses of total cellularity in the inner retina.

Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification.

The inner surface of the retina contains a complex mixture of neurons, glia, and vasculature, including retinal ganglion cells (RGCs), the final output neurons of the retina and primary neurons that are damaged in several blinding diseases. The goal of the current work was two-fold: to assess the feasibility of using computer-assisted detection of nuclei and random forest classification to automate the quantification of RGCs in hematoxylin/eosin (H&E)-stained retinal whole-mounts; and if possible, to use the approach to examine how nuclear size influences disease susceptibility among RGC populations. To achieve this, data from RetFM-J, a semi-automated ImageJ-based module that detects, counts, and collects quantitative data on nuclei of H&E-stained whole-mounted retinas, were used in conjunction with a manually curated set of images to train a random forest classifier. To test performance, computer-derived outputs were compared to previously published features of several well-characterized mouse models of ophthalmic disease and their controls: normal C57BL/6J mice; Jun-sufficient and Jun-deficient mice subjected to controlled optic nerve crush (CONC); and DBA/2J mice with naturally occurring glaucoma. The result of these efforts was development of RetFM-Class, a command-line-based tool that uses data output from RetFM-J to perform random forest classification of cell type. Comparative testing revealed that manual and automated classifications by RetFM-Class correlated well, with 83.2% classification accuracy for RGCs. Automated characterization of C57BL/6J retinas predicted 54,642 RGCs per normal retina, and identified a 48.3% Jun-dependent loss of cells at 35 days post CONC and a 71.2% loss of RGCs among 16-month-old DBA/2J mice with glaucoma. Output from automated analyses was used to compare nuclear area among large numbers of RGCs from DBA/2J mice (n = 127,361). In aged DBA/2J mice with glaucoma, RetFM-Class detected a decrease in median and mean nucleus size of cells classified into the RGC category, as did an independent confirmation study using manual measurements of nuclear area demarcated by BRN3A-immunoreactivity. In conclusion, we have demonstrated that histology-based random forest classification is feasible and can be utilized to study RGCs in a high-throughput fashion. Despite having some limitations, this approach demonstrated a significant association between the size of the RGC nucleus and the DBA/2J form of glaucoma.

Neuropathology of substance use disorders.

Addictions to licit and illicit drugs are chronic relapsing brain disorders that affect circuits that regulate reward, motivation, memory, and decision-making. Drug-induced pathological changes in these brain regions are associated with characteristic enduring behaviors that continue despite adverse biopsychosocial consequences. Repeated exposure to these substances leads to egocentric behaviors that focus on obtaining the drug by any means and on taking the drug under adverse psychosocial and medical conditions. Addiction also includes craving for the substances and, in some cases, involvement in risky behaviors that can cause death. These patterns of behaviors are associated with specific cognitive disturbances and neuroimaging evidence for brain dysfunctions in a diverse population of drug addicts. Postmortem studies have also revealed significant biochemical and/or structural abnormalities in some addicted individuals. The present review provides a summary of the evidence that has accumulated over the past few years to implicate brain dysfunctions in the varied manifestations of drug addiction. We thus review data on cerebrovascular alterations, brain structural abnormalities, and postmortem studies of patients who abuse cannabis, cocaine, amphetamines, heroin, and "bath salts". We also discuss potential molecular, biochemical, and cellular bases for the varied clinical presentations of these patients. Elucidation of the biological bases of addiction will help to develop better therapeutic approaches to these patient populations.

Establishing vitreous glucose and beta-hydroxybutyrate thresholds to assist in the diagnosis of hypothermia.

The post-mortem diagnosis of hypothermia is challenging to establish due to the lack of pathognomonic findings and the confounding problem that any comorbidity may account for death. A 4-year retrospective case-control study was performed to compare the vitreous glucose and beta-hydroxybutyrate (BHB) concentrations between hypothermia deaths and controls. Over the study period 34 cases of hypothermia and 39 controls were analyzed. Hypothermia deaths versus controls had higher mean vitreous glucose (2.93 mmol/L vs. 1.14 mmol/L; p < 0.0001), BHB (1.89 mmol/L vs. 1.35 mmol/L; p = 0.01), and combined glucose+BHB (4.83 mmol/L vs. 2.46 mmol/L; p < 0.0001). Receiver operating characteristic (ROC) curves showed that the best model for predicting hypothermia in all cases was a combined vitreous glucose+BHB threshold of 2.03 mmol/L (sensitivity 88.2 %; specificity 56.4 %). A sub-group analysis broken down by detectable levels of blood ethanol showed that cases of hypothermia with and without ethanol maintained higher median vitreous glucose relative to the controls (2.05 vs. 0.35 mmol/L and 2.70 vs. 0.65 mmol/L; p = 0.02), however median BHB was only significantly elevated when ethanol was absent (1.88 vs. 1.42 mmol/L; p < 0.0001). Subsequent ROC curve analysis demonstrated that a better model for predicting hypothermia was in cases when blood ethanol was absent. In those deaths vitreous BHB alone had the best area under the curve, with an optimum threshold of 1.83 mmol/L (sensitivity 83.3 %; specificity 96.3 %). This study shows that post-mortem vitreous glucose and BHB are useful ancillary studies to assist in the diagnosis of hypothermia. Ethanol however is a confounder and can alter the utility of vitreous BHB when diagnosing hypothermia in those who have consumed alcohol prior to death.

Central visual field damage in glaucoma eyes with choroidal microvasculature dropout with and without high axial myopia.

PURPOSE: To characterise the relationship between a deep-layer microvasculature dropout (MvD) and central visual field (VF) damage in primary open-angle glaucoma (POAG) patients with and without high axial myopia. DESIGN: Cross-sectional study. METHODS: Seventy-one eyes (49 patients) with high axial myopia and POAG and 125 non-highly myopic POAG eyes (97 patients) were enrolled. Presence, area and angular circumference of juxtapapillary MvD were evaluated on optical coherence tomography angiography B-scans and en-face choroidal images. RESULTS: Juxtapapillary MvD was detected more often in the highly myopic POAG eyes (43 eyes, 86%) than in the non-highly myopic eyes (73 eyes, 61.9%; p=0.002). In eyes with MvD, MvD area and angular circumference (95% CI) were significantly larger in the highly myopic eyes compared with the non-highly myopic eyes (area: (0.69 (0.40, 0.98) mm2 vs 0.31 (0.19, 0.42) mm2, p=0.011) and (angular circumference: 84.3 (62.9, 105.8) vs 74.5 (58.3, 90.9) degrees, p<0.001), respectively. 24-2 VF mean deviation (MD) was significantly worse in eyes with MvD compared with eyes without MvD in both groups (p<0.001). After adjusting for 24-2 MD VF, central VF defects were more frequently found in eyes with MvD compared with eyes without MvD (82.7% vs 60.9%, p<0.001). In multivariable analysis, higher intraocular pressure, worse 24-2 VF MD, longer axial length and greater MvD area and angular circumference were associated with worse 10-2 VF MD. CONCLUSIONS: MvD was more prevalent and larger in POAG eyes with high myopia than in non-highly myopic POAG eyes. In both groups, eyes with MvD showed worse glaucoma severity and more central VF defects.

Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection.

Purpose: To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations. Methods: Image quality ground truth was determined by manual review of 2815 fundus photographs of healthy and POAG eyes from the Diagnostic Innovations in Glaucoma Study and African Descent and Glaucoma Evaluation Study (DIGS/ADAGES), as well as 11,350 from the Ocular Hypertension Treatment Study (OHTS). Human experts assessed a photograph as high quality if of sufficient quality to determine POAG status and poor quality if not. A DL quality model was trained on photographs from DIGS/ADAGES and tested on OHTS. The effect of DL quality assessment on DL POAG detection was measured using area under the receiver operating characteristic (AUROC). Results: The DL quality model yielded an AUROC of 0.97 for differentiating between high- and low-quality photographs; qualitative human review affirmed high model performance. Diagnostic accuracy of the DL POAG model was significantly greater (P < 0.001) in good (AUROC, 0.87; 95% CI, 0.80-0.92) compared with poor quality photographs (AUROC, 0.77; 95% CI, 0.67-0.88). Conclusions: The DL quality model was able to accurately assess fundus photograph quality. Using automated quality assessment to filter out low-quality photographs increased the accuracy of a DL POAG detection model. Translational Relevance: Incorporating DL quality assessment into automated review of fundus photographs can help to decrease the burden of manual review and improve accuracy for automated DL POAG detection.

Deep Learning Estimation of 10-2 Visual Field Map Based on Macular Optical Coherence Tomography Angiography Measurements.

PURPOSE: To develop deep learning (DL) models estimating the central visual field (VF) from optical coherence tomography angiography (OCTA) vessel density (VD) measurements. DESIGN: Development and validation of a deep learning model. METHODS: A total of 1051 10-2 VF OCTA pairs from healthy, glaucoma suspects, and glaucoma eyes were included. DL models were trained on en face macula VD images from OCTA to estimate 10-2 mean deviation (MD), pattern standard deviation (PSD), 68 total deviation (TD) and pattern deviation (PD) values and compared with a linear regression (LR) model with the same input. Accuracy of the models was evaluated by calculating the average mean absolute error (MAE) and the R2 (squared Pearson correlation coefficients) of the estimated and actual VF values. RESULTS: DL models predicting 10-2 MD achieved R2 of 0.85 (95% confidence interval [CI], 74-0.92) for 10-2 MD and MAEs of 1.76 dB (95% CI, 1.39-2.17 dB) for MD. This was significantly better than mean linear estimates for 10-2 MD. The DL model outperformed the LR model for the estimation of pointwise TD values with an average MAE of 2.48 dB (95% CI, 1.99-3.02) and R2 of 0.69 (95% CI, 0.57-0.76) over all test points. The DL model outperformed the LR model for the estimation of all sectors. CONCLUSIONS: DL models enable the estimation of VF loss from OCTA images with high accuracy. Applying DL to the OCTA images may enhance clinical decision making. It also may improve individualized patient care and risk stratification of patients who are at risk for central VF damage.