Effect of socioeconomic deprivation as determined by the English deprivation deciles on the progression of diabetic retinopathy and maculopathy: a multivariate case-control analysis of 88 910 patients attending a South-East London diabetic eye screening service.

PURPOSE: To determine associations between deprivation using the Index of Multiple Deprivation (IMD and individual IMD subdomains) with incident referable diabetic retinopathy/maculopathy (termed rDR). METHODS: Anonymised demographic and screening data collected by the South-East London Diabetic Eye Screening Programme were extracted from September 2013 to December 2019. Multivariable Cox proportional models were used to explore the association between the IMD, IMD subdomains and rDR. RESULTS: From 118 508 people with diabetes who attended during the study period, 88 910 (75%) were eligible. The mean (± SD) age was 59.6 (±14.7) years; 53.94% were male, 52.58% identified as white, 94.28% had type 2 diabetes and the average duration of diabetes was 5.81 (±6.9) years; rDR occurred in 7113 patients (8.00%). Known risk factors of younger age, Black ethnicity, type 2 diabetes, more severe baseline DR and diabetes duration conferred a higher risk of incident rDR. After adjusting for these known risk factors, the multivariable analysis did not show a significant association between IMD (decile 1 vs decile 10) and rDR (HR: 1.08, 95% CI: 0.87 to 1.34, p=0.511). However, high deprivation (decile 1) in three IMD subdomains was associated with rDR, namely living environment (HR: 1.64, 95% CI: 1.12 to 2.41, p=0.011), education skills (HR: 1.64, 95% CI: 1.12 to 2.41, p=0.011) and income (HR: 1.19, 95% CI: 1.02 to 1.38, p=0.024). CONCLUSION: IMD subdomains allow for the detection of associations between aspects of deprivation and rDR, which may be missed when using the aggregate IMD. The generalisation of these findings outside the UK population requires corroboration internationally.

Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings.

Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existing automated algorithms have mostly been developed from images acquired with high cost mydriatic retinal cameras and cannot be applied in the settings used in most low- and middle-income countries. In this prospective multicentre study, we developed a deep learning system (DLS) that detects referable DR from retinal images acquired using handheld non-mydriatic fundus camera by non-technical field workers in 20 sites across India. Macula-centred and optic-disc-centred images from 16,247 eyes (9778 participants) were used to train and cross-validate the DLS and risk factor based logistic regression models. The DLS achieved an AUROC of 0.99 (1000 times bootstrapped 95% CI 0.98-0.99) using two-field retinal images, with 93.86 (91.34-96.08) sensitivity and 96.00 (94.68-98.09) specificity at the Youden's index operational point. With single field inputs, the DLS reached AUROC of 0.98 (0.98-0.98) for the macula field and 0.96 (0.95-0.98) for the optic-disc field. Intergrader performance was 90.01 (88.95-91.01) sensitivity and 96.09 (95.72-96.42) specificity. The image based DLS outperformed all risk factor-based models. This DLS demonstrated a clinically acceptable performance for the identification of referable DR despite challenging image capture conditions.

Multimodal testing reveals subclinical neurovascular dysfunction in prediabetes, challenging the diagnostic threshold of diabetes.

AIM: To explore if novel non-invasive diagnostic technologies identify early small nerve fibre and retinal neurovascular pathology in prediabetes. METHODS: Participants with normoglycaemia, prediabetes or type 2 diabetes underwent an exploratory cross-sectional analysis with optical coherence tomography angiography (OCT-A), handheld electroretinography (ERG), corneal confocal microscopy (CCM) and evaluation of electrochemical skin conductance (ESC). RESULTS: Seventy-five participants with normoglycaemia (n = 20), prediabetes (n = 29) and type 2 diabetes (n = 26) were studied. Compared with normoglycaemia, mean peak ERG amplitudes of retinal responses at low (16-Td·s: 4.05 µV, 95% confidence interval [95% CI] 0.96-7.13) and high (32-Td·s: 5·20 µV, 95% CI 1.54-8.86) retinal illuminance were lower in prediabetes, as were OCT-A parafoveal vessel densities in superficial (0.051 pixels/mm2 , 95% CI 0.005-0.095) and deep (0.048 pixels/mm2 , 95% CI 0.003-0.093) retinal layers. There were no differences in CCM or ESC measurements between these two groups. Correlations between HbA1c and peak ERG amplitude at 32-Td·s (r = -0.256, p = 0.028), implicit time at 32-Td·s (r = 0.422, p < 0.001) and 16-Td·s (r = 0.327, p = 0.005), OCT parafoveal vessel density in the superficial (r = -0.238, p = 0.049) and deep (r = -0.3, p = 0.017) retinal layers, corneal nerve fibre length (CNFL) (r = -0.293, p = 0.017), and ESC-hands (r = -0.244, p = 0.035) were observed. HOMA-IR was a predictor of CNFD (ß = -0.94, 95% CI -1.66 to -0.21, p = 0.012) and CNBD (ß = -5.02, 95% CI -10.01 to -0.05, p = 0.048). CONCLUSIONS: The glucose threshold for the diagnosis of diabetes is based on emergent retinopathy on fundus examination. We show that both abnormal retinal neurovascular structure (OCT-A) and function (ERG) may precede retinopathy in prediabetes, which require confirmation in larger, adequately powered studies.

Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship.

Corneal confocal microscopy (CCM) is a rapid non-invasive in vivo ophthalmic imaging technique that images the cornea. Historically, it was utilised in the diagnosis and clinical management of corneal epithelial and stromal disorders. However, over the past 20 years, CCM has been increasingly used to image sub-basal small nerve fibres in a variety of peripheral neuropathies and central neurodegenerative diseases. CCM has been used to identify subclinical nerve damage and to predict the development of diabetic peripheral neuropathy (DPN). The complex structure of the corneal sub-basal nerve plexus can be readily analysed through nerve segmentation with manual or automated quantification of parameters such as corneal nerve fibre length (CNFL), nerve fibre density (CNFD), and nerve branch density (CNBD). Large quantities of 2D corneal nerve images lend themselves to the application of artificial intelligence (AI)-based deep learning algorithms (DLA). Indeed, DLA have demonstrated performance comparable to manual but superior to automated quantification of corneal nerve morphology. Recently, our end-to-end classification with a 3 class AI model demonstrated high sensitivity and specificity in differentiating healthy volunteers from people with and without peripheral neuropathy. We believe there is significant scope and need to apply AI to help differentiate between peripheral neuropathies and also central neurodegenerative disorders. AI has significant potential to enhance the diagnostic and prognostic utility of CCM in the management of both peripheral and central neurodegenerative diseases.

Automated image curation in diabetic retinopathy screening using deep learning.

Diabetic retinopathy (DR) screening images are heterogeneous and contain undesirable non-retinal, incorrect field and ungradable samples which require curation, a laborious task to perform manually. We developed and validated single and multi-output laterality, retinal presence, retinal field and gradability classification deep learning (DL) models for automated curation. The internal dataset comprised of 7743 images from DR screening (UK) with 1479 external test images (Portugal and Paraguay). Internal vs external multi-output laterality AUROC were right (0.994 vs 0.905), left (0.994 vs 0.911) and unidentifiable (0.996 vs 0.680). Retinal presence AUROC were (1.000 vs 1.000). Retinal field AUROC were macula (0.994 vs 0.955), nasal (0.995 vs 0.962) and other retinal field (0.997 vs 0.944). Gradability AUROC were (0.985 vs 0.918). DL effectively detects laterality, retinal presence, retinal field and gradability of DR screening images with generalisation between centres and populations. DL models could be used for automated image curation within DR screening.

Multicenter Evaluation of Diagnostic Circulating Biomarkers to Detect Sight-Threatening Diabetic Retinopathy.

Importance: It is a global challenge to provide regular retinal screening for all people with diabetes to detect sight-threatening diabetic retinopathy (STDR). Objective: To determine if circulating biomarkers could be used to prioritize people with type 2 diabetes for retinal screening to detect STDR. Design, Setting, and Participants: This cross-sectional study collected data from October 22, 2018, to December 31, 2021. All laboratory staff were masked to the clinical diagnosis, assigned a study cohort, and provided with the database containing the clinical data. This was a multicenter study conducted in parallel in 3 outpatient ophthalmology clinics in the UK and 2 centers in India. Adults 40 years and older were categorized into 4 groups: (1) no history of diabetes, (2) type 2 diabetes of at least 5 years' duration with no evidence of DR, (3) nonproliferative DR with diabetic macular edema (DME), or (4) proliferative DR. STDR comprised groups 3 and 4. Exposures: Thirteen previously verified biomarkers were measured using enzyme-linked immunosorbent assay. Main Outcomes and Measures: Severity of DR and presence of DME were diagnosed using fundus photographs and optical coherence tomography. Weighted logistic regression and receiver operating characteristic curve analysis (ROC) were performed to identify biomarkers that discriminate STDR from no DR beyond the standard clinical parameters of age, disease duration, ethnicity (in the UK) and hemoglobin A1c. Results: A total of 538 participants (mean [SD] age, 60.8 [9.8] years; 319 men [59.3%]) were recruited into the study. A total of 264 participants (49.1%) were from India (group 1, 54 [20.5%]; group 2, 53 [20.1%]; group 3, 52 [19.7%]; group 4, 105 [39.8%]), and 274 participants (50.9%) were from the UK (group 1, 50 [18.2%]; group 2, 70 [25.5%]; group 3, 55 [20.1%]; group 4, 99 [36.1%]). ROC analysis (no DR vs STDR) showed that in addition to age, disease duration, ethnicity (in the UK) and hemoglobin A1c, inclusion of cystatin C had near-acceptable discrimination power in both countries (area under the receiver operating characteristic curve [AUC], 0.779; 95% CI, 0.700-0.857 in 215 patients in the UK with complete data; AUC, 0.696; 95% CI, 0.602-0.791 in 208 patients in India with complete data). Conclusions and Relevance: Results of this cross-sectional study suggest that serum cystatin C had good discrimination power in the UK and India. Circulating cystatin-C levels may be considered as a test to identify those who require prioritization for retinal screening for STDR.

The prevalence of retinopathy in prediabetes: A systematic review.

Diabetic retinopathy is a leading cause of vision loss globally. The current diagnostic thresholds for diabetes are still based on historic data correlating glycemic parameters with retinopathy; however, an excess prevalence of retinopathy has also been reported in prediabetes. We aimed to determine the reported prevalence of retinopathy in adults with prediabetes. We performed searches using MEDLINE, EMBASE, PubMed, Web of Science, CINAHL, Google Scholar and the Cochrane databases from inception to August 1, 2020. We evaluated methodological quality and certainty of the evidence using a validated risk of bias tool and GRADE, respectively. Twenty-four studies (8,759 participants with prediabetes) were included after screening 5,155 abstracts and reviewing 98 full-text records. Nineteen studies (79%) reported population-based data. Retinopathy prevalence estimates ranged between 0.3-14.1% (median 7.1%, interquartile range 2.4-9.7%), with high variance in estimates due to differing screening methods, retinopathy grading protocols and study populations. We judged this as low-certainty evidence using GRADE, downgrading for risk of bias and inconsistency. From studies that compared both populations, post hoc analysis revealed a lower median retinopathy prevalence in normal glucose tolerance (3.2%, interquartile range 0.3-7.3%) than prediabetes (6.6%, interquartile range 1.9-9.8%). These data suggest an excess prevalence of retinopathy in prediabetes.

Evaluating a Deep Learning Diabetic Retinopathy Grading System Developed on Mydriatic Retinal Images When Applied to Non-Mydriatic Community Screening.

Artificial Intelligence has showcased clear capabilities to automatically grade diabetic retinopathy (DR) on mydriatic retinal images captured by clinical experts on fixed table-top retinal cameras within hospital settings. However, in many low- and middle-income countries, screening for DR revolves around minimally trained field workers using handheld non-mydriatic cameras in community settings. This prospective study evaluated the diagnostic accuracy of a deep learning algorithm developed using mydriatic retinal images by the Singapore Eye Research Institute, commercially available as Zeiss VISUHEALTH-AI DR, on images captured by field workers on a Zeiss Visuscout® 100 non-mydriatic handheld camera from people with diabetes in a house-to-house cross-sectional study across 20 regions in India. A total of 20,489 patient eyes from 11,199 patients were used to evaluate algorithm performance in identifying referable DR, non-referable DR, and gradability. For each category, the algorithm achieved precision values of 29.60 (95% CI 27.40, 31.88), 92.56 (92.13, 92.97), and 58.58 (56.97, 60.19), recall values of 62.69 (59.17, 66.12), 85.65 (85.11, 86.18), and 65.06 (63.40, 66.69), and F-score values of 40.22 (38.25, 42.21), 88.97 (88.62, 89.31), and 61.65 (60.50, 62.80), respectively. Model performance reached 91.22 (90.79, 91.64) sensitivity and 65.06 (63.40, 66.69) specificity at detecting gradability and 72.08 (70.68, 73.46) sensitivity and 85.65 (85.11, 86.18) specificity for the detection of all referable eyes. Algorithm accuracy is dependent on the quality of acquired retinal images, and this is a major limiting step for its global implementation in community non-mydriatic DR screening using handheld cameras. This study highlights the need to develop and train deep learning-based screening tools in such conditions before implementation.

Returning to cataract surgery after a hiatus: a UK survey report.

OBJECTIVES: Cataract surgeons may periodically take time away from operating which can lead to skills fade. There is a paucity of research investigating the experiences of returning cataract surgeons and how different individual circumstances impact on their return. Our aim was to investigate the subjective experiences of UK ophthalmologists simultaneously returning to surgery following the nationwide elective surgical hiatus due to the Covid-19 pandemic. METHODS: An online survey was nationally distributed between 01/09/2020 and 29/10/2020 to registered UK ophthalmologists. Participants indicating a surgical hiatus of 8 weeks or more were included. RESULTS: 232 of 264 responses were analysed. Covid-19 was the most frequent reason for a surgical hiatus (median 15 weeks). Perceived operating difficulties were found in 29.1%. Transient anxiety (51.7%), reduced confidence, and perceived increased surgical time were commonplace. Trainees and females were more likely to encounter negative experiences (p < 0.001) and barriers to resource accessibility. Eyesi® and online videos were the most available and accessed pre-return resources. Childcare was five times more likely to present as a barrier to resource access for females than males. CONCLUSIONS: Technical skills fade such as capsulorhexis difficulties were commonly perceived by trainee surgeons in addition to transient anxiety, reported in more than half of all surgeons following a hiatus as short as 8 weeks. Eyesi® simulation offers the potential to negate technical de-skilling. Few had formal return plans or awareness of RCOphth guidance. We believe there is scope for more specialised and targeted support for future returning cataract surgeons thereby optimising patient care.

Deep learning for gradability classification of handheld, non-mydriatic retinal images.

Screening effectively identifies patients at risk of sight-threatening diabetic retinopathy (STDR) when retinal images are captured through dilated pupils. Pharmacological mydriasis is not logistically feasible in non-clinical, community DR screening, where acquiring gradable retinal images using handheld devices exhibits high technical failure rates, reducing STDR detection. Deep learning (DL) based gradability predictions at acquisition could prompt device operators to recapture insufficient quality images, increasing gradable image proportions and consequently STDR detection. Non-mydriatic retinal images were captured as part of SMART India, a cross-sectional, multi-site, community-based, house-to-house DR screening study between August 2018 and December 2019 using the Zeiss Visuscout 100 handheld camera. From 18,277 patient eyes (40,126 images), 16,170 patient eyes (35,319 images) were eligible and 3261 retinal images (1490 patient eyes) were sampled then labelled by two ophthalmologists. Compact DL model area under the receiver operator characteristic curve was 0.93 (0.01) following five-fold cross-validation. Compact DL model agreement (Kappa) were 0.58, 0.69 and 0.69 for high specificity, balanced sensitivity/specificity and high sensitivity operating points compared to an inter-grader agreement of 0.59. Compact DL gradability model performance was favourable compared to ophthalmologists. Compact DL models can effectively classify non-mydriatic, handheld retinal image gradability with potential applications within community-based DR screening.

Prevalence of retinopathy in prediabetes: protocol for a systematic review and meta-analysis.

INTRODUCTION: There is growing evidence of a higher than expected prevalence of retinopathy in prediabetes. This paper presents the protocol of a systematic review and meta-analysis of retinopathy in prediabetes. The aim of the review is to estimate the prevalence of retinopathy in prediabetes and to summarise the current data. METHODS AND ANALYSIS: This protocol is developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) guidelines. A comprehensive electronic bibliographic search will be conducted in MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Google Scholar and the Cochrane Library. Eligible studies will report prevalence data for retinopathy on fundus photography in adults with prediabetes. No time restrictions will be placed on the date of publication. Screening for eligible studies and data extraction will be conducted by two reviewers independently, using predefined inclusion criteria and prepiloted data extraction forms. Disagreements between the reviewers will be resolved by discussion, and if required, a third (senior) reviewer will arbitrate.The primary outcome is the prevalence of any standard features of diabetic retinopathy (DR) on fundus photography, as per International Clinical Diabetic Retinopathy Severity Scale (ICDRSS) classification. Secondary outcomes are the prevalence of (1) any retinal microvascular abnormalities on fundus photography that are not standard features of DR as per ICDRSS classification and (2) any macular microvascular abnormalities on fundus photography, including but not limited to the presence of macular exudates, microaneurysms and haemorrhages. Risk of bias for included studies will be assessed using a validated risk of bias tool for prevalence studies. Pooled estimates for the prespecified outcomes of interest will be calculated using random effects meta-analytic techniques. Heterogeneity will be assessed using the I2 statistic. ETHICS AND DISSEMINATION: Ethical approval is not required as this is a protocol for a systematic review and no primary data are to be collected. Findings will be disseminated through peer-reviewed publications and presentations at national and international meetings including Diabetes UK, European Association for the Study of Diabetes, American Diabetes Association and International Diabetes Federation conferences. PROSPERO REGISTRATION NUMBER: CRD42020184820.

Multimodal Imaging in a Case of Peripapillary Choroidal Neovascular Membrane Associated With Idiopathic Intracranial Hypertension.

ABSTRACT: Optical coherence tomography angiography is one of the latest noninvasive imaging modalities for visualizing the vasculature of retina and choroid. We describe its application in the diagnosis, treatment, and monitoring of a patient with peripapillary choroidal neovascular membrane in the setting of idiopathic intracranial hypertension, who responded well to a course of ranibizumab intravitreal injections.

Comparing diabetic retinopathy lesions in scanning laser ophthalmoscopy and colour fundus photography.

PURPOSE: To evaluate the detection of different lesions of diabetic retinopathy (DR) in scanning laser ophthalmoscopy (SLO) compared to colour fundus photographs within the same retinal field. METHODS: Scanning laser ophthalmoscopy and colour fundus photographs of 67 eyes taken on the same day of dilated patients with different grades of diabetic retinopathy were retrospectively examined. SLO images were then cropped to match the size and position of its 45-degree colour fundus photograph equivalent. Five independent masked graders assessed the images. Features of DR including haemorrhages, microaneurysms, exudates, cotton wool spots, intraretinal microvascular abnormalities, venous beading and neovascularization were analysed. Additionally, each image was classified according to Early Treatment of Diabetic Retinopathy Study (ETDRS) grading and English Diabetic Eye Screening Programme (DESP) grading. RESULTS: There was moderate to good agreement in ETDRS and DESP grading between SLO and colour fundus photographs within individual graders (weighted kappa range 0.45-0.65). The average number of microaneurysms for SLO and colour photographs showed a high degree of agreement (ICC: 0.92). Equally, the average number of blot haemorrhages in SLO and colour images showed strong agreement (ICC: 0.98) and the average number of cotton wool spots in SLO and colour images correlated well (ICC: 0.92). For these three lesions, the intergrader reliability was good (ICC ≥0.66) for both modalities. CONCLUSION: There was good correlation in detected microaneurysms, blot haemorrhages and cotton wool spots between SLO and colour fundus photographs. However, there was intergrader variability in flame haemorrhage, intraretinal microvascular abnormalities, venous beading and neovascularization grading with both modalities.

Factors affecting cataract surgery operating time among trainees and consultants.

PURPOSE: To define factors affecting cataract surgery operating time for operating room planning, optimizing throughput, enhancing patient experiences, minimizing costs, and allocating training time. SETTING: Epsom and St. Helier University National Health Service Trust, London, United Kingdom. DESIGN: Retrospective case series. METHODS: All patients who had primary manual phacoemulsification cataract surgery from January 1, 2012, to December 30, 2016, were included. Combined anterior and posterior segment procedures and surgeons with fewer than 50 cases were excluded. Anonymized data collected were demographics, anesthesia, operating time, surgeon grade, case complexity, pupil size, pupil expander or capsular tension ring (CTR) use, intraocular lens type, posterior capsule or zonular fiber rupture or dialysis, vitreous loss, and automated anterior vitrectomy. RESULTS: From 11 067 cases, 9552 (86.3%) had a recorded operating time. The mean ± SD operating times in minutes were as follows: consultants 19 ± 10, junior 30 ± 11, intermediate 27 ± 12, senior trainees 24 ± 10, and fellows 31 ± 11. Operating time was significantly shorter for topical than for sub-Tenon or general anesthesia, especially among trainees. Consultant operating time remained unchanged with increasing case complexity, except for high-complexity cases. Small pupils, pupil expander or CTR use, posterior capsule or zonular fiber rupture or dialysis with or without vitreous loss (mean 45 ± 23) were associated with increased operating times. Iris hooks were associated with greater increases in operating time than Malyugin rings (16 minutes versus 6 minutes; P < .001). There was a modest 3-minute decrease in operating time among consultants over 5 years. CONCLUSION: Cataract surgery operating time was significantly influenced by anesthesia type, surgeon grade, high case complexity, pupil size, pupil expander use/type, CTR use, and intraoperative complications.

Iris hooks versus a pupil expansion ring: Operating times, complications, and visual acuity outcomes in small pupil cases.

PURPOSE: To compare iris hooks and a pupil expansion ring (Malyugin ring) in eyes with a small pupil. SETTING: Epsom and St. Helier University National Health Service Trust, London, United Kingdom. DESIGN: Retrospective case series. METHODS: Patients who had primary phacoemulsification cataract surgery from January 1, 2012, to December 30, 2016, were included. Combined procedures and surgeons with fewer than 50 cases were excluded. Anonymized data were demographics, surgeon grade, case complexity, iris hooks or Malyugin ring use, posterior capsule rupture, zonular fiber rupture or dialysis, vitreous loss, operating time, postoperative complications, and visual acuity. RESULTS: Of the 9552 cases included, 425 (4.4%) had a small pupil, 314 of which required a pupil expansion ring and 95 iris hooks. Compared with no pupil expander, iris hooks were associated with an additional operating time of 14 minutes for consultants and 24 minutes for trainees. The pupil expansion ring was associated with an additional operating time of 4 minutes and 6 minutes, respectively. Neither pupil expander was associated with more intraoperative complications. The pupil expansion ring was associated with higher postoperative anterior uveitis and corneal edema rates. Pupil expander cases achieved equivalent visual acuity gains. CONCLUSIONS: The pupil expansion ring and iris hooks were safe and effective in minimizing intraoperative complications in eyes with a small pupil. The pupil expansion ring was faster to use than iris hooks. Monitoring for signs of postoperative anterior uveitis or cornea edema with careful insertion or removal of pupil expansion rings is advocated.

Updated cataract surgery complexity stratification score for trainee ophthalmic surgeons.

PURPOSE: To devise a comprehensive cataract surgery complexity score system for the selection of appropriate cases for trainees using evidence-based, validated risk factors for posterior capsule rupture, patient-specific factors, and complexity stratification recommendations to minimize complications, optimize outcomes, and maximize patient safety. SETTING: Epsom and St. Helier University National Health Service Trust, London, United Kingdom. DESIGN: Retrospective cohort study. METHODS: Patients having primary phacoemulsification cataract surgery from January 1, 2011 until December 31, 2016 were included; combined corneal, glaucoma, or posterior segment procedures were excluded. Anonymized data on demographics, pupil size, pupil expander use, intraoperative and postoperative complications, and postoperative distance visual acuity were extracted. Patients were stratified by complexity score and surgeon grade (consultant, junior, intermediate, and senior trainee, and fellow). RESULTS: From 11 468 included cases, 8200 (71.5%) had a complexity score. Small pupil, pupil expander use, iris damage during phacoemulsification, zonular dialysis, postoperative raised intraocular pressure, and corneal edema (odds ratio, 3.17; 95% confidence interval, 2.05-4.92) were significantly associated with increasing complexity. Appropriate case allocation by complexity and surgeon grade resulted in no association between posterior capsule rupture and complexity score. Increasing complexity scores were associated with lower postoperative distance visual acuity. CONCLUSIONS: The updated evidence-based comprehensive cataract complexity score system is a useful tool for the stratification of case complexity and guides appropriate case selection to match trainee experience. Higher complexity scores were associated with greater intraoperative and postoperative complications and lower postoperative distance visual acuity. Patients with higher scores should be made aware of the guarded prognosis when obtaining consent.

The association between individual metabolic syndrome components, primary liver cancer and cirrhosis: A study in the Swedish AMORIS cohort.

Metabolic syndrome (MetS) is associated with non-alcoholic fatty liver disease, which may progress to cirrhosis, a significant risk factor of hepatocellular carcinoma (HCC), the commonest malignant primary liver cancer (PLC). We investigated the association between the individual components of MetS (lipids, apolipoproteins, raised glucose, diabetes and obesity), PLC and cirrhosis. A total of 509,436 participants from the Swedish AMORIS cohort, recruited between January 1985 and December 1996 (end-date December 2011), aged ≥20 with baseline triglycerides (TG), total cholesterol (TC), glucose and liver enzymes were included. Those with baseline benign liver tumours, PLC or cirrhosis were excluded. Multivariate Cox regression, adjusted for age, gender, socio-economic status, liver disease (excluding cirrhosis) and MetS factors were used to estimate the association with PLC and cirrhosis. There were 766 PLC and 2,775 cirrhosis cases over 13 years. Raised TG, low TC, raised glucose, diabetes and low HDL were associated with an increased risk of developing PLC and cirrhosis. ApoB/ApoA-I ratio were also associated with PLC, whilst low LDL, raised TG/HDL, low ApoA-I and low ApoB were associated with cirrhosis. Obesity was significantly associated with PLC but not cirrhosis. Raised TG, low TC, raised glucose and diabetes showed stronger associations with PLC in participants with cirrhosis but many participants developed PLC without cirrhosis. Individual components of MetS (lipids, apolipoproteins, raised glucose, diabetes and obesity) were associated with an increased risk of developing PLC or cirrhosis. MetS components were more strongly associated with PLC with preceding cirrhosis history but many participants developed PLC without cirrhosis.