1. 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).
PurposePurpose
This 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 forImplementation 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, MD
1
Logicoy Inc, Los Angeles, CA and 2
ZyDoc, Islandia, NY
NT
S
Ranibizumab vs. Bevacizumab and Dexamethasone vs. Triamcinolone in BRVO
This 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 CRVO
A 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 Conclusions
This 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.