Prevalence of chronic disease in older adults in multitier eye-care facilities in South India: Electronic medical records-driven big data analytics report

Purpose: To study the prevalence of systemic conditions in older adults, either self-reported or discovered during routine eye examinations, at multitier eye-care facilities over the past decade, and to explore their association with vision and common ocular disorders, including cataract, glaucoma, and retinopathy. Methods: Retrospective review of a large data set compiled from the electronic medical records of patients older than 60 years who presented to an eye facility of a multitier ophthalmology network located in 200 different geographical locations that included urban and rural eye-care centers spread across four states in India over a 10-year period. Results: 618,096 subjects aged 60 or older were identified as visiting an eye facility over the 10-year study period. The mean age of the study individuals was 67·28 (±6·14) years. A majority of older adults (66·96%) reported being free of systemic illnesses. Patients from lower socioeconomic status had a lower prevalence of chronic systemic disease, but the presenting vision was poorer. Hypertension (21·62%) and diabetes (18·77%) were the most commonly reported chronic conditions in patients who had concomitant systemic illness with visual concerns. Conclusion: The prevalence of chronic systemic illnesses in older adults presenting to multitier eye-care facilities is relatively low, except in those with diabetic retinopathy. These observations suggest a need to include active screening for common chronic diseases in standalone eye-care facilities to achieve a more accurate assessment of chronic disease burden in the older population

Prevalence of Age-Related Macular Degeneration and Associated Factors in Indian Cohort in a Tertiary Care Setting

PURPOSE: To report a big data analysis of risk and protective factors in patients with AMD, as well as report on the age-adjusted prevalence in a geriatric Indian cohort in a hospital setting. METHODS: This retrospective, observational study of all patients older than 60 years of age. Multiple logistic regression was performed for the binary outcome and the presence of AMD. Variables analyzed include age, gender, socioeconomic status, occupation, urban-rural-metropolitan distribution, self-reported history of diabetes mellitus (DM), hypertension (HTN), or coronary artery disease (CAD), ocular comorbidities, history of cataract surgery, and presenting VA. Odds ratios (OR) and 99% confidence intervals were calculated. RESULTS: Of the 608,171 patients over the age of 60 years who attended our clinics, 1.68% of subjects had a diagnosis of AMD (N = 10,217). Less than half (4,621 of 10,217 with AMD) of them were diagnosed to have dry AMD. Cataract, glaucoma, and diabetic retinopathy were associated with lower risk of AMD. Cataract surgery was associated with the higher risk of AMD (OR = 1.20; 99% CI 1.13-1.29). Smoking was not associated with AMD. CONCLUSION: Big data analysis from a hospital setting shows that the prevalence of AMD above the age of 60 years is low. More patients with wet AMD present for treatment compared to dry AMD. Smoking was not associated with AMD in the Indian population. Cataract surgery was associated with higher prevalence of AMD

Alterations in the Ocular Surface Fungal Microbiome in Fungal Keratitis Patients

Keratitis, an inflammatory disease of the eye, when neglected could lead to sight-threatening complications and ultimately blindness. Globally, over a million people are affected by keratitis annually. Keratitis has a microbial etiology and is caused by bacteria, fungi, viruses, etc. The present study compared the ocular surface fungal microbiome of healthy individuals and individuals with fungal keratitis. Fungal microbiomes from the conjunctival swabs of healthy individuals and from conjunctival swabs and corneal scrapings of individuals with fungal keratitis were generated using ITS2 region amplicons. Microbiomes were sequenced using Illumina MiSeq 2 × 250 base pair chemistry with a paired-end protocol. Based on Alpha diversity indices, phylum and genera level diversity, abundance differences, and heat map analysis, the fungal microbiomes of conjunctival swabs and corneal scrapings of individuals with fungal keratitis exhibited dysbiosis (alterations in the diversity and abundance) compared to the ocular surface microbiome of the healthy control individuals. This is the first report indicating dysbiosis in the fungal microbiome of conjunctival swabs and corneal scrapings in individuals with fungal keratitis. A total of 11 genera present in the majority of the eyes constituted the variable core ocular microbiome

Forecast of Outpatient Visits to a Tertiary Eyecare Network in India Using the EyeSmart Electronic Medical Record System

India is home to 1.3 billion people. The geography and the magnitude of the population present unique challenges in the delivery of healthcare services. The implementation of electronic health records and tools for conducting predictive modeling enables opportunities to explore time series data like patient inflow to the hospital. This study aims to analyze expected outpatient visits to the tertiary eyecare network in India using datasets from a domestically developed electronic medical record system (eyeSmart™) implemented across a large multitier ophthalmology network in India. Demographic information of 3,384,157 patient visits was obtained from eyeSmart EMR from August 2010 to December 2017 across the L.V. Prasad Eye Institute network. Age, gender, date of visit and time status of the patients were selected for analysis. The datapoints for each parameter from the patient visits were modeled using the seasonal autoregressive integrated moving average (SARIMA) modeling. SARIMA (0,0,1)(0,1,7)7 provided the best fit for predicting total outpatient visits. This study describes the prediction method of forecasting outpatient visits to a large eyecare network in India. The results of our model hold the potential to be used to support the decisions of resource planning in the delivery of eyecare services to patients

Leveraging big data for pattern recognition of socio-demographic and climatic factors in correlation with eye disorders in Telangana State, India

Purpose: Big data is the new gold, especially in health care. Advances in collecting and processing electronic medical records (EMR) coupled with increasing computer capabilities have resulted in an increased interest in the use of big data in health care. Ophthalmology has been an area of focus where results have shown to be promising. The objective of this study was to determine whether the EMR at a multi-tier ophthalmology network in India can contribute to the management of patient care, through studying how climatic and socio-demographic factors relate to eye disorders and visual impairment in the State of Telangana. Methods: The study was designed by merging a dataset obtained from the Telangana State Development Society to an existing EMR of approximately 1 million patients, who presented themselves with different eye symptoms and diagnosed with several diseases from the years (2011–2019). The dataset obtained included weather and climatic variables to be tested alongside eye disorders. AI creative featuring techniques have been used to narrow down the variables most affected by climatic and demographic factors, with the application of the Cynefin Framework as a guide to simplify and structure the dataset for analysis. Results: Our findings revealed a high presence of cataract in the state of Telangana, mostly in rural areas and throughout the different weather seasons in India. Males tend to be the most affected as per the number of visits to the clinic, while home makers make the most visit to the hospital, in addition to employees, students, and laborers. While cataract is most dominant in the older age population, diseases such as astigmatism, conjunctivitis, and emmetropia, are more present in the younger age population. Conclusion: The study appeared useful for taking preventive measures in the future to manage the treatment of patients who present themselves with eye disorders in Telangana. The use of clinical big datasets helps to identify the burden of ocular disorders in the population. The overlaying of meteorological data on the clinical presentation of patients from a geographic region lends insight into the complex interaction of environmental factors on the prevalence of ocular disorders in them

Mycobiome changes in the vitreous of post fever retinitis patients.

Fungi have been associated with various diseases of the eye like keratitis, uveitis and endophthalmitis. Despite this fact, fungal microbiome (mycobiome) studies compared to the bacterial microbiome studies have remained neglected. In the present study, using metagenomic sequencing, the mycobiomes of the vitreous of healthy control individuals (VC, n = 15) and individuals with post fever retinitis + non-PFR uveitis (PFR+, n = 9) were analysed and compared. The results indicated that Ascomycota was the most predominant phylum in both VC and PFR+ groups. Further, at the genera level it was observed that the abundance of 17 fungal genera were significantly different in post fever retinitis (PFR, n = 6) group compared to control group. Of these 17 genera, it was observed that 14 genera were relatively more abundant in PFR group and the remaining 3 genera in the VC group. Genus Saccharomyces, a commensal of the gut and skin, was predominantly present in the vitreous of both the cohorts, however it was significantly less abundant in PFR group. Further, significant increase in the genera that have a pathogenic interaction with the host were observed in PFR group. On the whole the mycobiome in both the groups differed significantly and formed two distinct clusters in the heatmap and Principal co-ordinate analysis. These results demonstrate significant changes in the mycobiome from the vitreous of post fever retinitis patients compared to healthy controls thus implying that dysbiotic changes in the fungal vitreous microbiome are associated with PFR

Comparison of the Vitreous Fluid Bacterial Microbiomes between Individuals with Post Fever Retinitis and Healthy Controls

Ocular microbiome research has gained momentum in the recent past and has provided new insights into health and disease conditions. However, studies on sight threatening intraocular inflammatory diseases have remained untouched. In the present study, we attempted to identify the bacterial microbiome associated with post fever retinitis using a metagenomic sequencing approach. For this purpose, bacterial ocular microbiomes were generated from vitreous samples collected from control individuals (VC, n = 19) and individuals with post fever retinitis (PFR, n = 9), and analysed. The results revealed 18 discriminative genera in the microbiomes of the two cohorts out of which 16 genera were enriched in VC and the remaining two in PFR group. These discriminative genera were inferred to have antimicrobial, anti-inflammatory, and probiotic function. Only two pathogenic bacteria were differentially abundant in 20% of the PFR samples. PCoA and heatmap analysis showed that the vitreous microbiomes of VC and PFR formed two distinct clusters indicating dysbiosis in the vitreous bacterial microbiomes. Functional assignments and network analysis also revealed that the vitreous bacterial microbiomes in the control group exhibited more evenness in the bacterial diversity and several bacteria had antimicrobial function compared to the PFR group

Gut mycobiomes are altered in people with type 2 Diabetes Mellitus and Diabetic Retinopathy.

Studies have documented dysbiosis in the gut mycobiome in people with Type 2 diabetes mellitus (T2DM). However, it is not known whether dysbiosis in the gut mycobiome of T2DM patients would be reflected in people with diabetic retinopathy (DR) and if so, is the observed mycobiome dysbiosis similar in people with T2DM and DR. Gut mycobiomes were generated from healthy controls (HC), people with T2DM and people with DR through Illumina sequencing of ITS2 region. Data were analysed using QIIME and R software. Dysbiotic changes were observed in people with T2DM and DR compared to HC at the phyla and genera level. Mycobiomes of HC, T2DM and DR could be discriminated by heat map analysis, Beta diversity analysis and LEfSE analysis. Spearman correlation of fungal genera indicated more negative correlation in HC compared to T2DM and DR mycobiomes. This study demonstrates dysbiosis in the gut mycobiomes in people with T2DM and DR compared to HC. These differences were significant both at the phyla and genera level between people with T2DM and DR as well. Such studies on mycobiomes may provide new insights and directions to identification of specific fungi associated with T2DM and DR and help developing novel therapies for Diabetes Mellitus and DR