Proof of ConceptProof of ConceptBackgroundBackground As medical conditions are characterized, diagnosed, and treated at a more rapid pace, an increasingly widening gap

has formed between treatment options that are inferred based on a physician’s past experiences (and therefore liable to bias) and those that are supported by actual clinical data.1

The advent and widespread implementation of electronic health records (EHRs) has allowed for an unparalleled digital categorization of real-world clinical data that can be used to close this gap through computational analysis.2

The application of big data analytics in the healthcare industry could be responsible for an estimated $100 billion - $300 billion in additional revenue in the United States per year.3

Central retinal vein occlusion (CRVO) and a related and more common condition, branch retinal vein occlusion (BRVO), can lead to macular edema, sharp decreases in visual acuity (VA), and high intraocular pressure (IOP), which may contribute to the development of glaucoma.4

There are currently five commonly prescribed drugs delivered via intravitreal injection to treat retinal vein occlusion: three are vascular endothelial growth factor inhibitors VEGF: aflibercept, bevacizumab, and ranibizumab and two are corticosteroids: triamcinolone and dexamethasone. Physicians often resort to prescribing various combinations of these drugs and performing repeat injections without sufficient empirical data backing such measures if and when a patient stops responding to treatment.

1. Stewart, W. F., Shah, N. R., Selna, M. J., Paulus, R. A. & Walker, J. M. Bridging The Inferential Gap: The Electronic Health Record And Clinical Evidence. Health Aff 26, w181–w191 (2007). 2. Murdoch TB & Detsky AS. The inevitable application of big data to health care. JAMA 309, 1351–1352 (2013). 3. Manyika, J. et al. Big data: The next frontier for innovation, competition, and productivity. McKinsey & Company. 4. Hayreh, S. S., Zimmerman, M. B., Beri, M. & Podhajsky, P. Intraocular pressure abnormalities associated with central and hemicentral retinal vein occlusion. Ophthalmology 111, 133–141 (2004).

PurposePurposeThis software was developed to allow physicians to quickly and accurately assess the efficacy of certain drugs already approved by the US Food and Drug Administration (and subsequent combinations) via analysis of data extracted from EHRs in an effort to provide patients with more informed, personalized, and effective treatment options. 

By offering physicians cloud based solutions with de-identification, data sharing, and the ability to see only your own patients’ data, that of your group’s or multiple collaborating groups, we aim to broaden the potential for big data to serve as a method of disease management for not only individuals but for the larger population as well.

Methods and FeaturesMethods and Features

Implementation of Big Data Analytics to Assess the Efficacy of Treatments for Implementation of Big Data Analytics to Assess the Efficacy of Treatments for Central and Branch Retinal Vein OcclusionCentral and Branch Retinal Vein Occlusion

Alexander Aabedi, Fred Aabedi1, James M. Maisel, MD2, Rodney P. Coe, MD1Logicoy Inc, Los Angeles, CA and 2ZyDoc, Islandia, NY

NTS

Ranibizumab vs. Bevacizumab and Dexamethasone vs. Triamcinolone in BRVOThis comparison was selected to provide a contrast between the two available corticosteroids and two of the VEGF inhibitors. Increases in VA scores are evident over a twelve month period, with a larger percent increase from baseline evident in patients treated with dexamethasone (though with a limited sample size) and in those treated with bevacizumab.

Ranibizumab vs. Dexamethasone in CRVOA slightly more modest decrease in IOP was observed in patients treated with Ranibizumab as opposed to Dexamethasone. Both drugs were comparable in reduction of mean foveal thickness.In patients receiving ranibizumab, there was a 100% conversion to pseudophakia in 31 months while none was reported for patients receiving bevacizumab.

Summary and ConclusionsSummary and ConclusionsThis proof of concept illustrates the ease with which physicians can make more informed decisions regarding a specific patient’s course of treatment by using visualized analytics based on real clinical data extracted directly from EHRs. Ultimately, with enough collaboration and larger data sets with increased sample sizes, such analytics may improve clinical outcomes for patients with conditions for which multiple overlapping treatment options are available.

Data was extracted from eClinicalWorks EHRs from two retinal specialists using ZyDoc MediSapien NLP and manual methods over period from 2009-2015. Monthly averages were used for aggregate plots.

If patient data was unavailable for a specific time point, last available data was carried forward. Patients with less than six months of data were excluded.

Day 0 on the plot is the day on which the first drug was given. For a combination of drugs, it is the day on which the second drug was given.

For CRVO and BRVO drug data analytics, plots generated include EDTRS (a measure of visual acuity), IOP (intraocular pressure), and OCT (foveal thickness in microns measured by optical coherence tomography) versus time.

Using a provided template, physicians can input their patient’s data by extracting them from EHRs and then upload them via the UI. Currently, spreadsheet templates for retinal vein occlusion and diabetic macular edema are available.

Aggregate graphs are available for multiple drugs and their combinations, plotted with color-coded lines and data points.

Plots can be generated for specific patients designated by their account number.

Plots can also be generated over a specified period of time.