Big data and ophthalmic research

Trends in admission rates of primary angle closure diseases for the urban population in China, 2011-2021

Background

Cataract surgery and laser peripheral iridotomy (LPI) are effective approaches for preventing primary angle closure diseases (PACDs), as well as acute primary angle closure (APAC). Due to the development of population screening and increases in cataract surgery rates, this study aimed to examine trends in the admission rates of PACD among the urban population in China.

Methods

This cross-sectional study examined patients who were admitted to a hospital for PACD, and who underwent cataract surgery or LPI operations. The data were obtained from the Yinzhou Regional Health Information Platform (YRHIP) from 2011 to 2021. The annual rates of PACD and APAC admissions, cataract surgery and LPI were analyzed, with the number of cases used as numerators and the annual resident population in Yinzhou district used as denominators.

Results

A total of 2,979 patients with PACD admissions, 1,023 patients with APAC admissions, 53,635 patients who underwent cataract surgery and 16,450 patients who underwent LPI were included. The number of annual admissions for PACD gradually increased from 22 cases (1.6/100000) in 2011 to 387 cases (30.8/100000) in 2016, after which it decreased to 232 cases (16.2/100000) in 2019 and then increased to 505 cases (30.6/100000) in 2021. The number of cataract surgeries gradually increased from 1728 (127.7/100000) in 2011 to 7002 (424.9/100000) in 2021. Similarly, the number of LPI gradually increased from 109 (8.0/100000) in 2011 to 3704 (224.8/100000) in 2021.

Conclusion

The admission rates of PACD for the urban population in China have declined in recent years after a long increasing trend in the rates of cataract surgery and LPI. However, it increased rapidly during the COVID-19 epidemic. The national health database should be further utilized to investigate temporal trends in the prevalence of PACD.

Effects of Cost Sharing on Ophthalmic Care Utilization in the Affordable Care Act Marketplace

PURPOSE

To determine the distribution and quantity of ophthalmic care consumed on Affordable Care Act (ACA) plans, the demographics of the population utilizing these services, and the relationship between ACA insurance coverage plan tier, cost sharing, and total cost of ophthalmic care consumed.

METHODS

This cross-sectional study analyzed ACA individual and small group market claims data from the Wakely Affordable Care Act (WACA) 2018 dataset, which contains detailed claims, enrollment, and premium data from Edge Servers for 3.9 million individual and small group market lives. We identified all enrollees with ophthalmology-specific billing, procedure, and national drug codes. We then analyzed the claims by plan type and calculated the total cost and out-of-pocket (OOP) cost.

RESULTS

Among 3.9 million enrollees in the WACA 2018 dataset, 538,169 (13.7%) had claims related to ophthalmology procedures, medications, and/or diagnoses. A total of $203 million was generated in ophthalmology-related claims, with $54 million in general services, $42 million in medications, $20 million in diagnostics and imaging, and $86 million in procedures. Average annual OOP costs were $116 per member, or 30.9% of the total cost, and were lowest for members with platinum plans (16% OOP) and income-driven cost sharing reduction (ICSR) subsidies (17% OOP). Despite stable ocular disease distribution across plan types, beneficiaries with silver ICSR subsidies consumed more total care than any other plan, higher than platinum plan enrollees and almost 1.5× the cost of bronze plan enrollees.

CONCLUSIONS

Ophthalmic care for enrollees on ACA plans generated substantial costs in 2018. Plans with higher OOP cost sharing may result in lower utilization of ophthalmic care.

Design and Development of a Web-Based Prospective Nationwide Registry for Ocular Inflammatory Diseases: UVEITE.PT - The Portuguese Ocular Inflammation Registry

Uveitis is a heterogeneous collection of infrequent diseases, which poses significant challenges to cost-effective research in the field. Medical registries are being increasingly recognized as crucial tools to provide high-quality data, thus enabling prospective clinical research. This paper describes the design and technical structure development of an innovative countrywide electronic medical record for uveitis, Uveite.pt, and gives an overview of the cohort registered since its foundation, March 2020.Uveite.pt is an electronic medical record platform developed by the Portuguese Ocular Inflammation Group (POIG), a scientific committee of the Portuguese Ophthalmology Society. This is a nationwide customized web-based platform for uveitis patients useful for both clinical practice and real-world-based research, working as a central repository and reporting tool for uveitis. This paper describes the technical principles, the design and the development of a web-based interoperable registry for uveitis in Portugal and provides an overview of more than 400 patients registered in the first 18 months since inception.In infrequent diseases, the existence of registries enables to gather evidence and increase research possibilities to clinicians. The adoption of this platform enables standardization and improvement of clinical practice in uveitis. It is useful to apprehend the repercussion of medical and surgical treatments in uveitis and scleritis, supporting clinicians in the strict monitoring of drug adverse reactions and surgical outcomes.

Diagnostic accuracy of self-reported age-related macular degeneration in the ASPREE Longitudinal Study of Older Persons

BACKGROUND

The validity of findings from epidemiological studies using self-report of ophthalmic conditions depends on several factors. We assessed the diagnostic accuracy of self-reported age-related macular degeneration (AMD) among older Australians enroled in a primary prevention clinical trial and compared diagnostic accuracy between demographic subgroups.

METHODS

At baseline (2010-2015), Australian sub-study participants of the ASPirin in Reducing Events in the Elderly (ASPREE) trial, underwent bilateral two-field, 45° non-mydriatic colour retinal photography. Beckman classification of any-stage AMD was used as the reference standard diagnosis. Participants were asked whether a doctor had ever diagnosed them with "macular degeneration" (the index test) via a paper-based questionnaire as part of the ASPREE Longitudinal Study of Older Persons (ALSOP) within the first year of enrolment.

RESULTS

In total, 4193 participants were included (aged 70-92 years, 50.8% female). Of those, 262 (6.3%) reported having AMD and 92 (2.2%) were unsure. Retinal grading detected 2592 (61.8%) with no AMD, 867 (20.7%) with early, 686 (16.4%) with intermediate and 48 (1.1%) with late AMD (n = 1601 with any-stage AMD, 38.2%). Self-reported AMD had 11.4% sensitivity (95% CI 9.9-13.1) and 96.9% specificity (95% CI 96.2-97.6) for any-stage AMD, with 69.8% and 63.9% positive and negative predictive values. Sensitivity was higher among participants with late-stage AMD (87.5%), older participants (26.8%), and those with poorer vision (41.0%).

CONCLUSIONS

Although most participants with late-stage AMD were aware of having AMD, the majority with early and intermediate AMD were not. Therefore, findings from studies that rely on disease self-report should be interpreted with caution.

GATEKEEPER's Strategy for the Multinational Large-Scale Piloting of an eHealth Platform: Tutorial on How to Identify Relevant Settings and Use Cases

BACKGROUND

The World Health Organization's strategy toward healthy aging fosters person-centered integrated care sustained by eHealth systems. However, there is a need for standardized frameworks or platforms accommodating and interconnecting multiple of these systems while ensuring secure, relevant, fair, trust-based data sharing and use. The H2020 project GATEKEEPER aims to implement and test an open-source, European, standard-based, interoperable, and secure framework serving broad populations of aging citizens with heterogeneous health needs.

OBJECTIVE

We aim to describe the rationale for the selection of an optimal group of settings for the multinational large-scale piloting of the GATEKEEPER platform.

METHODS

The selection of implementation sites and reference use cases (RUCs) was based on the adoption of a double stratification pyramid reflecting the overall health of target populations and the intensity of proposed interventions; the identification of a principles guiding implementation site selection; and the elaboration of guidelines for RUC selection, ensuring clinical relevance and scientific excellence while covering the whole spectrum of citizen complexities and intervention intensities.

RESULTS

Seven European countries were selected, covering Europe's geographical and socioeconomic heterogeneity: Cyprus, Germany, Greece, Italy, Poland, Spain, and the United Kingdom. These were complemented by the following 3 Asian pilots: Hong Kong, Singapore, and Taiwan. Implementation sites consisted of local ecosystems, including health care organizations and partners from industry, civil society, academia, and government, prioritizing the highly rated European Innovation Partnership on Active and Healthy Aging reference sites. RUCs covered the whole spectrum of chronic diseases, citizen complexities, and intervention intensities while privileging clinical relevance and scientific rigor. These included lifestyle-related early detection and interventions, using artificial intelligence-based digital coaches to promote healthy lifestyle and delay the onset or worsening of chronic diseases in healthy citizens; chronic obstructive pulmonary disease and heart failure decompensations management, proposing integrated care management based on advanced wearable monitoring and machine learning (ML) to predict decompensations; management of glycemic status in diabetes mellitus, based on beat to beat monitoring and short-term ML-based prediction of glycemic dynamics; treatment decision support systems for Parkinson disease, continuously monitoring motor and nonmotor complications to trigger enhanced treatment strategies; primary and secondary stroke prevention, using a coaching app and educational simulations with virtual and augmented reality; management of multimorbid older patients or patients with cancer, exploring novel chronic care models based on digital coaching, and advanced monitoring and ML; high blood pressure management, with ML-based predictions based on different intensities of monitoring through self-managed apps; and COVID-19 management, with integrated management tools limiting physical contact among actors.

CONCLUSIONS

This paper provides a methodology for selecting adequate settings for the large-scale piloting of eHealth frameworks and exemplifies with the decisions taken in GATEKEEPER the current views of the WHO and European Commission while moving forward toward a European Data Space.

Application of big data in ophthalmology

The advents of information technologies have led to the creation of ever-larger datasets. Also known as big data, these large datasets are characterized by its volume, variety, velocity, veracity, and value. More importantly, big data has the potential to expand traditional research capabilities, inform clinical practice based on real-world data, and improve the health system and service delivery. This review first identified the different sources of big data in ophthalmology, including electronic medical records, data registries, research consortia, administrative databases, and biobanks. Then, we provided an in-depth look at how big data analytics have been applied in ophthalmology for disease surveillance, and evaluation on disease associations, detection, management, and prognostication. Finally, we discussed the challenges involved in big data analytics, such as data suitability and quality, data security, and analytical methodologies.

Visual Acuity – Assessment of Data Quality and Usability in an Electronic Health Record System

Objective

To examine the data quality and usability of visual acuity (VA) data extracted from an electronic health record (EHR) system during ophthalmology encounters and provide recommendations for consideration of relevant VA end points in retrospective analyses.

Design

Retrospective, EHR data analysis.

Participants

All patients with eyecare office encounters at any 1 of the 9 locations of a large academic medical center between August 1, 2013, and December 31, 2015.

Methods

Data from 13 of the 21 VA fields (accounting for 93% VA data) in EHR encounters were extracted, categorized, recoded, and assessed for conformance and plausibility using an internal data dictionary, a 38-item listing of VA line measurements and observations including 28 line measurements (e.g., 20/30, 20/400) and 10 observations (e.g., no light perception). Entries were classified into usable and unusable data. Usable data were further categorized based on conformance to the internal data dictionary: (1) exact match; (2) conditional conformance, letter count (e.g., 20/30^+2-3); (3) convertible conformance (e.g., 5/200 to 20/800); (4) plausible but cannot be conformed (e.g., 5/400). Data were deemed unusable when they were not plausible.

Main Outcome Measures

Proportions of usable and unusable VA entries at the overall and subspecialty levels.

Results

All VA data from 513 036 encounters representing 166 212 patients were included. Of the 1 573 643 VA entries, 1 438 661 (91.4%) contained usable data. There were 1 196 720 (76.0%) exact match (category 1), 185 692 (11.8%) conditional conformance (category 2), 40 270 (2.6%) convertible conformance (category 3), and 15 979 (1.0%) plausible but not conformed entries (category 4). Visual acuity entries during visits with providers from retina (17.5%), glaucoma (14.0%), neuro-ophthalmology (8.9%), and low vision (8.8%) had the highest rates of unusable data. Documented VA entries with providers from comprehensive eyecare (86.7%), oculoplastics (81.5%), and pediatrics/strabismus (78.6%) yielded the highest proportions of exact match with the data dictionary.

Conclusions

Electronic health record VA data quality and usability vary across documented VA measures, observations, and eyecare subspecialty. We proposed a checklist of considerations and recommendations for planning, extracting, analyzing, and reporting retrospective study outcomes using EHR VA data. These are important first steps to standardize analyses enabling comparative research.

Impact of Artificial Intelligence on Medical Education in Ophthalmology

Clinical care in ophthalmology is rapidly evolving as artificial intelligence (AI) algorithms are being developed. The medical community and national and federal regulatory bodies are recognizing the importance of adapting to AI. However, there is a gap in physicians’ understanding of AI and its implications regarding its potential use in clinical care, and there are limited resources and established programs focused on AI and medical education in ophthalmology. Physicians are essential in the application of AI in a clinical context. An AI curriculum in ophthalmology can help provide physicians with a fund of knowledge and skills to integrate AI into their practice. In this paper, we provide general recommendations for an AI curriculum for medical students, residents, and fellows in ophthalmology.

A Bibliometric and Mapping Analysis of Glaucoma Research between 1900 and 2019

PURPOSE

To explore the relevance of scientific production on glaucoma using bibliometric tools.

DESIGN

Bibliographic study.

PARTICIPANTS

Original articles published from 1900 through 2019.

METHODS

We performed a search in Web of Science for documents published between 1900 and 2019. We used bibliometric indicators to explore documents production, dispersion, distribution, time of duplication, and annual growth, as characterized by Price's law of scientific literature growth, Lotka's law, the transient index, and the Bradford model. We also calculated the participation index of different countries and institutions. Finally, we explored with bibliometric mapping the co-occurrence networks for the most frequently used terms in glaucoma research.

MAIN OUTCOME MEASURES

Bibliometric indicators for individuals, institutions, and countries.

RESULTS

A total of 33 631 original articles were collected from the timeframe 1900 through 2019. Price's law showed an exponential growth. Scientific production was adjusted better to exponential growth (r = 0.967) than linear growth (r = 0.755). Literature on glaucoma research increased its growth in the last 30 years at a rate of 5.1% per year with a production that doubled its size every 13.9 years. The transience index was 60.08%; this indicates that most of the scientific production is the output of very few authors. Bradford's law showed a high concentration of articles published in a small core of specialized journals. Lotka's law indicated that the distribution of authors is concentrated heavily in small producers. The United States and University of London demonstrated the highest production of original articles. Map network visualization showed the generated term map detailing clusters of closely related terms.

CONCLUSIONS

Glaucoma literature has grown exponentially. A very high rate of transience was found that indicates the presence of numerous authors who sporadically publish on this topic. No evidence of a saturation point in the glaucoma literature was observed.

[Diagnostics of diseases of the optic nerve head in times of artificial intelligence and big data]

Hintergrund

Der Einsatz von künstlicher Intelligenz (KI) ist unter anderem in der automatischen Bildsegmentierung, -analyse und Klassifikation interessant und bereits für verschiedene Bereiche der Augenheilkunde beschrieben.

Fragestellung

Diese Arbeit soll einen Überblick über aktuelle Ansätze und Fortschritte bei der Anwendung von Big Data und KI bei verschiedenen Erkrankungen des Sehnervenkopfes geben.

Material und Methode

Es wurde eine PubMed-Recherche durchgeführt. Gesucht wurde nach Studien, die klinische Fragestellungen mithilfe von Big-Data-Ansätzen beantworteten oder klassische Methoden des maschinellen Lernens bei der Analyse von multimodaler Bildgebung des Sehnervenkopfes verwendeten.

Ergebnisse

Big Data kann bei Volkskrankheiten wie dem Glaukom helfen, klinische Fragestellungen zu beantworten. KI findet sowohl bei der Segmentierung von multimodaler Bildgebung des Sehnervenkopfes als auch bei der Klassifikation von Erkrankungen wie dem Glaukom oder der Stauungspapille auf diesen Bilddaten Anwendung.

Schlussfolgerung

Mithilfe von Big Data und KI können Zusammenhänge besser erkannt und die Diagnostik und Verlaufsbeurteilung von Erkrankungen des Sehnervenkopfes erleichtert oder automatisiert werden. Eine Voraussetzung für die klinische Anwendung ist in Europa die CE-Kennzeichnung als ein Medizinprodukt und in den USA die Zulassung durch die Food and Drug Administration.

Big Data Applications in Medical Field: A Literature Review

Digital imaging and medical reporting have acquired an essential role in healthcare, but the main challenge is the storage of a high volume of patient data. Although newer technologies are already introduced in the medical sciences to save records size, Big Data provides advancements by storing a large amount of data to improve the efficiency and quality of patient treatment with better care. It provides intelligent automation capabilities to reduce errors than manual inputs. Large numbers of research papers on big data in the medical field are studied and analyzed for their impacts, benefits, and applications. Big data has great potential to support the digitalization of all medical and clinical records and then save the entire data regarding the medical history of an individual or a group. This paper discusses big data usage for various industries and sectors. Finally, 12 significant applications for the medical field by the implementation of big data are identified and studied with a brief description. This technology can be gainfully used to extract useful information from the available data by analyzing and managing them through a combination of hardware and software. With technological advancement, big data provides health-related information for millions of patient-related to life issues such as lab tests reporting, clinical narratives, demographics, prescription, medical diagnosis, and related documentation. Thus, Big Data is essential in developing a better yet efficient analysis and storage healthcare services. The demand for big data applications is increasing due to its capability of handling and analyzing massive data. Not only in the future but even now, Big Data is proving itself as an axiom of storing, developing, analyzing, and providing overall health information to the physicians.

The 100 Most Cited Articles in Ophthalmology in Asia

PURPOSE

The aim of this study was to review the top 100 most-cited articles in ophthalmology in Asia since 1970.

METHODS

The Scopus database was used to identify the top 100 most-cited ophthalmology articles published in ophthalmology (T100-Eye) and nonophthalmology (T100-General) journals.

RESULTS

The T100-Eye articles were published between 1982 and 2015, and T100-General from 1982 to 2017. T100-Eye had higher citations [median (range) = 317 (249-1326)] than T100-General [158 (105-2628)], but T100-General were published in journals with higher impact factor (IF) than T100-Eye (median IF= 5.5 vs 4.4) and produced more landmark papers (3 vs 1 articles that were cited >1000 times). Fifty-five % of T100-Eye were published in 3 journals: Ophthalmology (n = 22), Investigative Ophthalmology and Visual Science (n = 17), and American Journal of Ophthalmology (n = 16). T100-Eye had 88 original research articles and 12 reviews, whereas T100-General had 84 original research and 16 reviews. The most-frequent studied disease categories were myopia (n = 16) and age-related macular degeneration (n = 15) in T100-Eye and diabetic retinopathy (n = 24) and glaucoma (n = 16) in T100-General. Japan and Singapore contributed most to T100-Eye (n = 42, n = 17) and T100-General (n = 36, n = 26) articles. More than 80% and 95% of first and last authors were male in both lists. Emerging research topics were optical coherence tomography in T100-Eye and artificial intelligence in T100-General.

CONCLUSIONS

Our citation analysis reveals differences in the focus of research topics of top-cited ophthalmology articles published in ophthalmology and nonophthalmology journals in Asia. It highlights that certain eye diseases are studied more in Asia and shows the contribution of specific countries to highly cited publications in ophthalmology research in Asia.

Big Data in Ophthalmology

Big data is the fuel of mankind's fourth industrial revolution. Coupled with new technology such as artificial intelligence and deep learning, the potential of big data is poised to be harnessed to its maximal in years to come. In ophthalmology, given the data-intensive nature of this specialty, big data will similarly play an important role. Electronic medical records, administrative and health insurance databases, mega national biobanks, crowd source data from mobile applications and social media, and international epidemiology consortia are emerging forms of "big data" in ophthalmology. In this review, we discuss the characteristics of big data, its potential applications in ophthalmology, and the challenges in leveraging and using these data. Importantly, in the next phase of work, it will be pertinent to further translate "big data" findings into real-world applications, to improve quality of eye care, and cost-effectiveness and efficiency of health services in ophthalmology.

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology

Widespread adoption of electronic health records (EHRs) has resulted in the collection of massive amounts of clinical data. In ophthalmology in particular, the volume range of data captured in EHR systems has been growing rapidly. Yet making effective secondary use of this EHR data for improving patient care and facilitating clinical decision-making has remained challenging due to the complexity and heterogeneity of these data. Artificial intelligence (AI) techniques present a promising way to analyze these multimodal data sets. While AI techniques have been extensively applied to imaging data, there are a limited number of studies employing AI techniques with clinical data from the EHR. The objective of this review is to provide an overview of different AI methods applied to EHR data in the field of ophthalmology. This literature review highlights that the secondary use of EHR data has focused on glaucoma, diabetic retinopathy, age-related macular degeneration, and cataracts with the use of AI techniques. These techniques have been used to improve ocular disease diagnosis, risk assessment, and progression prediction. Techniques such as supervised machine learning, deep learning, and natural language processing were most commonly used in the articles reviewed.

Eye injury registries - A systematic review

OBJECTIVES

Registries are integral to monitoring, surveying, treating, preventing and prognosticating trauma. The quantity and quality of data must justify a change or intervention in treatment and/or preventive strategies and must be collected while balancing the cost and time invested in the registry. This review documents the quality, completeness and operational and funding models for ocular trauma registries worldwide.

METHODS

The databases CENTRAL, MEDLINE, EMBASE and Informit Health Collection were searched using key word and mesh terms for: "Eye injury, "Ocular trauma", "Eye injury prevention", "Eye protection", "Registry". To find relevant unpublished articles and theses, clinicaltrials.gov, Trip, MedNar and Google Scholar were searched using the key words "eye injury" OR "ocular trauma" AND "registry*". No date or language restrictions were applied. The quality of registry data was assessed against published measures including design, operation and data quality.

RESULTS

The electronic search retrieved 528 distinct published articles; 61 articles were assessed for eligibility. Of the 61 articles identified, 28 were eligible to be included in the review, with cross-referencing identifying a further 7 articles. The source of most articles on ocular trauma registries was the United States, followed by Germany and China. Patient follow-up was conducted in 31 studies, with 6 months being the most frequently reported period. Issues with data quality included incomplete data such as presence or absence of eye protection and initial visual acuity. Attrition bias was controlled by the United States Eye Injury Register with follow-up. Patients without follow-up data were removed for some studies and this may have introduced bias.

CONCLUSION

National, state and hospital-based ocular trauma registries have contributed significantly to our understanding of ocular trauma. The United States has the most frequently cited and well-resourced ocular trauma registries. It is anticipated that this review will guide the development of future registries for ocular trauma in order to inform evidence-based prevention strategies and, ultimately, improve visual outcomes. We recommend the development of a consensus guidelines for international ocular trauma registry that includes mechanism and context of injury and visual outcomes, to permit international comparison that can be implemented at low cost with secure data capture.

The Increasing Burden of Myopia in Israel among Young Adults over a Generation: Analysis of Predisposing Factors

PURPOSE

To determine the trends in prevalence of myopia in Israeli young adults over approximately a generation, as well as associated factors and variation in the impact of these factors on myopia prevalence in this region over time.

DESIGN

Retrospective, cross-sectional study.

PARTICIPANTS

One hundred four thousand six hundred eighty-nine consecutive persons 16 to 19 years of age born between 1971 and 1994 who had not yet enlisted in the Israeli Army but had completed the medical profiling process.

METHODS

Using data collected at a north Israel recruitment center, the prevalence of myopia over time was estimated, and a polynomial regression analysis was performed to assess significance of nonlinear trends. Associations of demographic and socioeconomic factors with myopia were assessed, and trends over time were analyzed using a factorial logistic regression.

MAIN OUTCOME MEASURES

The primary outcome measure was factors associated with the prevalence of myopia over time. The secondary outcome measure was a description of the change in prevalence of myopia over time.

RESULTS

The prevalence of myopia increased 1.284-fold over 24 years from 20.4% among participants born between 1971 and 1982 to 26.2% among participants born between 1983 and 1994. A quite similar increase was observed among males (from 17.9% to 22.7%, respectively) and females (from 23.9% to 30.8%, respectively). The factors found to be associated with myopia were as follows: more recent date of birth, female gender, more years of education, being the eldest child, non-Israeli ethnic origin, and urban residence. However, there were significant trends over time in the effects of some of these factors, most notably an attenuation of the difference between participants of different religions in the recent birth-years period. Most of these associations and trends were observed in both males and females separately, with some gender-specific variations. Immigrants from Ethiopia who were raised in Israel were highly more likely to demonstrate myopia than those who arrived at an older age.

CONCLUSIONS

This study demonstrated an increase in the prevalence of myopia and the possible associations of urbanization- and higher education-related factors among several subpopulations and the risk for myopia developing.

Clinical Registries in Ophthalmology

TOPIC

Clinical registries in ophthalmology.

CLINICAL RELEVANCE

In recent years, advancements in digital technology and increasing use of electronic medical records in health systems have led to the dramatic growth in large clinical data sets. Clinical data registries are organized systems that collect data on patients diagnosed with a disease or condition or who undergo a certain procedure.

METHODS

A search of the PUBMED database was conducted in January 2018 for clinical registries in ophthalmology.

RESULTS

Ninety-seven clinical eye registries were found, with significant growth in numbers in the last 4 decades. The most common conditions captured were blindness or low vision, corneal transplantation, glaucoma, and cataract surgery. Most registries originate in the European region, North America, and Australia. Nine registries had multinational coverage, whereas 48 were national registries. As the numbers and scope of clinical registries have expanded, valuable observational data have been used to study real-world clinical outcomes in healthcare quality measurement and improvement and to develop new guidelines and standards. Pertinent areas of its use include studying treatments and outcomes in cataract surgery, corneal transplantation, and macular degeneration.

CONCLUSIONS

The use of clinical registries for quality improvement and research has grown significantly in the last few decades, and this trend will continue as information technology infrastructures develop.

Is combined cataract surgery associated with acute postoperative endophthalmitis? A nationwide study from 2005 to 2014

PURPOSE

To assess the incidence of acute postoperative endophthalmitis (POE) after cataract surgery combined with corneal, glaucoma or vitreoretinal surgical procedures from 2005 to 2014 in France.

METHODS

In this cohort study, acute POE occurring within 6 weeks after surgery was identified by means of billing codes recorded in a national database in patients operated for cataract extraction with phacoemulsification, or corneal, glaucoma or vitreoretinal surgical procedures, either combined or stand-alone.

RESULTS

From January 2005 to December 2014, up to 6 260 477 eyes underwent phacoemulsification cataract surgery as a single procedure and 115 468 eyes underwent phacoemulsification combined with corneal, glaucoma or vitreoretinal surgical procedures. The crude incidence of acute POE after stand-alone or combined cataract surgery was 0.102% and 0.149%, respectively. In multivariate Poisson analysis, combined surgery taken as a whole was at higher risk than cataract stand-alone surgery, with an adjusted incidence rate ratio (IRR) (95% CI) of 1.38 (1.11 to 1.70; p=0.0054). Glaucoma surgeries were associated with a lower acute POE incidence compared with phacoemulsification, conversely to vitreoretinal surgical procedures: IRR 0.63 (0.47 to 0.85; p<0.001) and IRR 1.78 (1.58 to 2.01; p<0.001), respectively.

CONCLUSION

A higher incidence of acute POE after combined cataract surgery than after cataract surgery done as a stand-alone procedure was observed based on the French nationwide medical-administrative database. The incidence of acute POE after combined surgery was related to the type of surgery performed simultaneously with cataract extraction.

Concurrence of big data analytics and healthcare: A systematic review

BACKGROUND

The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care.

PURPOSE

This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges.

DATA SOURCES

A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered.

STUDY SELECTION

Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected.

DATA EXTRACTION

Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare.

RESULTS

A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare.

CONCLUSION

This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries.

Big Data's Role in Precision Public Health

Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.

Predicting Future Elective Colon Resection for Diverticulitis Using Patterns of Health Care Utilization

Background

Recurrent diverticulitis is the most common reason for elective colon surgery and, although professional societies now recommend against early resection, its use continues to rise. Shared decision making decreases use of low-value surgery but identifying which patients are most likely to elect surgery has proven difficult. We hypothesized that Machine Learning algorithms using health care utilization (HCU) data can predict future clinical events including early resection for diverticulitis.

Study Design

We developed models for predicting future surgery among patients with new diagnoses of diverticulitis (2009-2012) from the MarketScan® database. Claims data (diagnosis, procedural, and drug codes) were used to train three Machine Learning algorithms to predict surgery occurring between 52 and 104 weeks following diagnosis.

Results

Of 82,231 patients with incident diverticulitis (age 51 ± 8 years, 52% female), 1.2% went on to elective colon resection. Using maximal training data (152 consecutive weeks of claims), the Gradient Boosting Machine model predicted elective surgery with an area under the curve (AUC) of 75% (95% uncertainty interval [UI] 71-79%). Models trained on less data resulted in less accurate prediction (AUC: 68% [64-74%] using 128 weeks, 57% [53-63%] using 104 weeks). The majority of resections (85%) were identified as low-value.

Conclusion

By applying Machine Learning to HCU data from the time around a diagnosis of diverticulitis, we predicted elective surgery weeks to months in advance, with moderate accuracy. Identifying patients who are most likely to elect surgery for diverticulitis provides an opportunity for effective shared decision making initiatives aimed at reducing the use of costly low-value care.

Trends in operating room-based glaucoma procedures in France from 2005 to 2014: a nationwide study

PURPOSE

To report the trends in operating room-based glaucoma procedures from 2005 to 2014 in France.

METHODS

We identified operating room-based glaucoma procedures (trabeculectomies, deep sclerectomies, aqueous shunts and ciliary body destructions) performed in France from 2005 to 2014 by means of billing codes from a national database. The annual rates and incidence of these procedures per 100 000 inhabitants were analysed globally and in three age groups: 0-14 years, 15-59 years and over 60 years.

RESULTS

The annual rate of trabeculectomies decreased slightly during the study period, while the rate for other surgical techniques (deep sclerectomies, aqueous drainage procedures and ciliary body destructions) increased. The overall rate of glaucoma surgeries was higher in areas with populations of African descent than in areas predominantly composed of Caucasian populations: 1.60 (95% CI 1.51 to 1.70, p<0.0001).

CONCLUSIONS

Trabeculectomy was the most commonly performed operating room-based glaucoma procedure in France from 2005 to 2014. Other modalities such as deep sclerectomies, aqueous drainage procedures and ciliary body destruction gained greater acceptance among French ophthalmologists during this 10-year period.