Automated Diabetic Retinopathy Image Assessment Software: Diagnostic Accuracy and Cost-Effectiveness Compared with Human Graders.

OBJECTIVE: With the increasing prevalence of diabetes, annual screening for diabetic retinopathy (DR) by expert human grading of retinal images is challenging. Automated DR image assessment systems (ARIAS) may provide clinically effective and cost-effective detection of retinopathy. We aimed to determine whether ARIAS can be safely introduced into DR screening pathways to replace human graders. DESIGN: Observational measurement comparison study of human graders following a national screening program for DR versus ARIAS. PARTICIPANTS: Retinal images from 20 258 consecutive patients attending routine annual diabetic eye screening between June 1, 2012, and November 4, 2013. METHODS: Retinal images were manually graded following a standard national protocol for DR screening and were processed by 3 ARIAS: iGradingM, Retmarker, and EyeArt. Discrepancies between manual grades and ARIAS results were sent to a reading center for arbitration. MAIN OUTCOME MEASURES: Screening performance (sensitivity, false-positive rate) and diagnostic accuracy (95% confidence intervals of screening-performance measures) were determined. Economic analysis estimated the cost per appropriate screening outcome. RESULTS: Sensitivity point estimates (95% confidence intervals) of the ARIAS were as follows: EyeArt 94.7% (94.2%-95.2%) for any retinopathy, 93.8% (92.9%-94.6%) for referable retinopathy (human graded as either ungradable, maculopathy, preproliferative, or proliferative), 99.6% (97.0%-99.9%) for proliferative retinopathy; Retmarker 73.0% (72.0 %-74.0%) for any retinopathy, 85.0% (83.6%-86.2%) for referable retinopathy, 97.9% (94.9%-99.1%) for proliferative retinopathy. iGradingM classified all images as either having disease or being ungradable. EyeArt and Retmarker saved costs compared with manual grading both as a replacement for initial human grading and as a filter prior to primary human grading, although the latter approach was less cost-effective. CONCLUSIONS: Retmarker and EyeArt systems achieved acceptable sensitivity for referable retinopathy when compared with that of human graders and had sufficient specificity to make them cost-effective alternatives to manual grading alone. ARIAS have the potential to reduce costs in developed-world health care economies and to aid delivery of DR screening in developing or remote health care settings

Global variations and time trends in the prevalence of primary open angle glaucoma (POAG): a systematic review and meta-analysis.

Systematic review of published population based surveys to examine the relationship between primary open angle glaucoma (POAG) prevalence and demographic factors. A literature search identified population-based studies with quantitative estimates of POAG prevalence (to October 2014). Multilevel binomial logistic regression of log-odds of POAG was used to examine the effect of age and gender among populations of different geographical and ethnic origins, adjusting for study design factors. Eighty-one studies were included (37 countries, 216 214 participants, 5266 POAG cases). Black populations showed highest POAG prevalence, with 5.2% (95% credible interval (CrI) 3.7%, 7.2%) at 60 years, rising to 12.2% (95% CrI 8.9% to 16.6%) at 80 years. Increase in POAG prevalence per decade of age was greatest among Hispanics (2.31, 95% CrI 2.12, 2.52) and White populations (1.99, 95% CrI 1.86, 2.12), and lowest in East and South Asians (1.48, 95% CrI 1.39, 1.57; 1.56, 95% CrI 1.31, 1.88, respectively). Men were more likely to have POAG than women (1.30, 95% CrI 1.22, 1.41). Older studies had lower POAG prevalence, which was related to the inclusion of intraocular pressure in the glaucoma definition. Studies with visual field data on all participants had a higher POAG prevalence than those with visual field data on a subset. Globally 57.5 million people (95% CI 46.4 to 73.1 million) were affected by POAG in 2015, rising to 65.5 million (95% CrI 52.8, 83.2 million) by 2020. This systematic review provides the most precise estimates of POAG prevalence and shows omitting routine visual field assessment in population surveys may have affected case ascertainment. Our findings will be useful to future studies and healthcare planning

Global variations and time trends in the prevalence of childhood myopia, a systematic review and quantitative meta-analysis: implications for aetiology and early prevention.

The aim of this review was to quantify the global variation in childhood myopia prevalence over time taking account of demographic and study design factors. A systematic review identified population-based surveys with estimates of childhood myopia prevalence published by February 2015. Multilevel binomial logistic regression of log odds of myopia was used to examine the association with age, gender, urban versus rural setting and survey year, among populations of different ethnic origins, adjusting for study design factors. 143 published articles (42 countries, 374 349 subjects aged 1-18 years, 74 847 myopia cases) were included. Increase in myopia prevalence with age varied by ethnicity. East Asians showed the highest prevalence, reaching 69% (95% credible intervals (CrI) 61% to 77%) at 15 years of age (86% among Singaporean-Chinese). Blacks in Africa had the lowest prevalence; 5.5% at 15 years (95% CrI 3% to 9%). Time trends in myopia prevalence over the last decade were small in whites, increased by 23% in East Asians, with a weaker increase among South Asians. Children from urban environments have 2.6 times the odds of myopia compared with those from rural environments. In whites and East Asians sex differences emerge at about 9 years of age; by late adolescence girls are twice as likely as boys to be myopic. Marked ethnic differences in age-specific prevalence of myopia exist. Rapid increases in myopia prevalence over time, particularly in East Asians, combined with a universally higher risk of myopia in urban settings, suggest that environmental factors play an important role in myopia development, which may offer scope for prevention

Adiposity in early, middle and later adult life and cardiometabolic risk markers in later life; findings from the British regional heart study.

OBJECTIVES: This research investigates the associations between body mass index (BMI) at 21, 40-59, 60-79 years of age on cardiometabolic risk markers at 60-79 years. METHODS: A prospective study of 3464 British men with BMI measured at 40-59 and 60-79 years, when cardiometabolic risk was assessed. BMI at 21 years was ascertained from military records, or recalled from middle-age (adjusted for reporting bias); associations between BMI at different ages and later cardiometabolic risk markers were examined using linear regression. Sensitive period, accumulation and mobility life course models were devised for high BMI (defined as BMI≥75th centile) and compared with a saturated BMI trajectory model. RESULTS: At ages 21, 40-59 and 60-79 years, prevalences of overweight (BMI≥25 kg/m2) were 12%, 53%, 70%, and obesity (≥30 kg/m2) 1.6%, 6.6%, and 17.6%, respectively. BMI at 21 years was positively associated with serum insulin, blood glucose, and HbA1c at 60-79 years, with increases of 1.5% (95%CI 0.8,2.3%), 0.4% (0.1,0.6%), 0.3% (0.1,0.4%) per 1 kg/m2, respectively, but showed no associations with blood pressure or blood cholesterol. However, these associations were modest compared to those between BMI at 60-79 years and serum insulin, blood glucose and HbA1c at 60-79 years, with increases of 8.6% (8.0,9.2%), 0.7% (0.5,0.9%), and 0.5% (0.4,0.7%) per 1 kg/m2, respectively. BMI at 60-79 years was also associated with total cholesterol and blood pressure. Associations for BMI at 40-59 years were mainly consistent with those of BMI at 60-79 years. None of the life course models fitted the data as well as the saturated model for serum insulin. A sensitive period at 50 years for glucose and HbA1c and sensitive period at 70 years for blood pressure were identified. CONCLUSIONS: In this cohort of men who were thin compared to more contemporary cohorts, BMI in later life was the dominant influence on cardiovascular and diabetes risk. BMI in early adult life may have a small long-term effect on diabetes risk

Differences in cardiovascular and diabetes risk markers at mean age 70 years for each 1 kg/m² increase in BMI separately at mean age 21, 50 and 70 years.

<p>n: Number of participants (subset of individuals with BMI available at all age periods).</p><p>Coef: regression coefficient represent difference in cardiovascular risk markers for 1 kg/m2 increase in BMI. Estimates are adjusted for age at the time when the risk markers were measured, and town as a fixed effect.</p><p>Differences in cardiovascular and diabetes risk markers at mean age 70 years for each 1 kg/m² increase in BMI separately at mean age 21, 50 and 70 years.</p

Distribution of BMI at mean age 21 years (early adulthood), 50 years (middle-age), and 70 years (late adulthood).

<p>Distribution of BMI at mean age 21 years (early adulthood), 50 years (middle-age), and 70 years (late adulthood).</p

Incidence of Late-Stage Age-Related Macular Degeneration in American Whites: Systematic Review and Meta-analysis.

PURPOSE: To estimate incidence of age-related macular degeneration (AMD) by subtype in American whites aged ≥50 years. DESIGN: Systematic review and meta-analysis. METHODS: SETTING: Prospective cohort studies of AMD incidence in populations of white European ancestry published in MEDLINE, EMBASE, and Web of Science. STUDY POPULATION: Fourteen publications in 10 populations that examined AMD incident cases were identified. OBSERVATION PROCEDURE: Data on age-sex-specific incidence of late AMD, geographic atrophy (GA) and neovascular AMD (NVAMD), year of recruitment, AMD grading method, and continent were extracted. MAIN OUTCOME MEASURE(S): Annual incidence of late AMD, GA, and NVAMD by age-sex in American whites aged ≥50 years from a Bayesian meta-analysis of incidence studies was compared with incidence extrapolated from published prevalence estimates. RESULTS: Incidence rates from the review agreed with those derived from prevalence, but the latter were based on more data, especially at older ages and by AMD subtypes. Annual incidence (estimated from prevalence) of late AMD in American whites was 3.5 per 1000 aged ≥50 years (95% credible interval 2.5, 4.7 per 1000), equivalent to 293 000 new cases in American whites per year (95% credible interval 207 000, 400 000). Incidence rates approximately quadrupled per decade in age. Annual incidence GA rates were 1.9 per 1000 aged ≥50 years, NVAMD rates were 1.8 per 1000. Late AMD incidence was 38% higher in women vs men (95% credible interval 6%, 82%). CONCLUSIONS: Estimating AMD incidence from prevalence allows better characterization at older ages and by AMD subtype where longitudinal data from incidence studies are limited

Differences in cardiovascular and diabetes risk markers at mean age 70 years, by trajectory of BMI (having a high BMI at different points of the life course) at different ages (mean age 21, 50 and 70 years).

<p>n: Number of participants (subset of individuals with BMI available at all age periods). For BMI at mean 70 years this includes all available data.</p><p>BMI trajectories: Each triplet corresponds to a different trajectory of high BMI at mean age 21, 50 and 70 years; with 0 and 1 denoting BMI below and above the 75th percentile of the BMI distribution, respectively. For example, (0–0-0) denoted low BMI at all age periods, whilst (0–0-1) signified high BMI at mean age 70 years only.</p><p>Coef: Estimates are differences in risk marker from BMI trajectory 0–0-0. Estimates are adjusted for age at the time when the risk markers were measured, and town as fixed effect.</p><p>Differences in cardiovascular and diabetes risk markers at mean age 70 years, by trajectory of BMI (having a high BMI at different points of the life course) at different ages (mean age 21, 50 and 70 years).</p

Cohort characteristics in early, middle, and late adulthood in all men, and by quintile of BMI in early adulthood.

<p>n: Number of participants (subset of individuals with BMI available at all age periods).</p><p>SD: Standard deviation.</p><p>a: Estimates correspond to geometric mean (Q1, Q3).</p><p>Quintiles of BMI at 21 years are calculated using all available data (including individuals with missing BMI at mean age 50 or 70 years).</p><p>Cohort characteristics in early, middle, and late adulthood in all men, and by quintile of BMI in early adulthood.</p

Differences in cardiovascular and diabetes risk markers at mean age 70 years, associated with having high BMI (≥75^th centile) separately at mean ages 21, 50 and 70 years compared to men with normal BMI at all 3 ages (separate analyses based on sensitive period models).

<p>n: Number of participants (subset of individuals with BMI available at all age periods).</p><p>K: percentage of individuals who had high BMI (BMI in the top 25% of the distribution) also at mean age 70 years.</p><p>Coef: regression coefficient for the effect of high BMI (BMI in the top 25% of the distribution) at each sensitive period (mean age 21, 50 or 70 years) as compared with never having had high BMI. Estimates are adjusted for age at the time when the risk markers were measured, and town as fixed effect.</p><p>For each outcome, results are obtained from 3 models fitted separately (one for each period: mean age 21, 50 and 70 years).</p><p>Differences in cardiovascular and diabetes risk markers at mean age 70 years, associated with having high BMI (≥75^th centile) separately at mean ages 21, 50 and 70 years compared to men with normal BMI at all 3 ages (separate analyses based on sensitive period models).</p