DavidSLiebeskind,MD

Imaging Data in the NEST: Stroke and the DAISI CRN

Professor of NeurologyDirector, Neurovascular Imaging Research Core

Director, Outpatient Stroke and Neurovascular ProgramsAssociate Neurology Director, UCLA Stroke Center

Consultant to Stryker and Medtronic

October20,2017

Overview

Precision stroke imaging and real-world data

Imaging and angiography core lab perspective

Integration of imaging in registries & evidence generation

Precision stroke imaging

Prototype of stroke as ideal neurological disorder to cultivate precision medicine

Acute presentation

Systematic use of imaging characterization

Expansive dimensions of multimodal & serial imaging

Rapid interaction of therapy, pathophysiology, outcomes

Extensive data collection, utilizing common data elements

Defined infrastructure & multidisciplinary coordination

Common disorder across the globe, easier to study and generalizability

Typical sequence of imaging

CT (CT/CTA/CTP)

DSA

24 hr CT

AE

Local imaging & decision-making

ASPECTS

Occlusion

Go/no-go?

“TICI 3”

local TICI versus central…

bias for “quality” and financial implications

Imaging data to verify measures and time metrics

Core lab perspective - imaging and angiography

Differentiating centralized imaging versus core lab adjudication

History in acute ischemic stroke studies of endovascular therapy

Merci IDE

Trials: IMS III/TREVO EU/TREVO2/DAWN/RESILIENT

Registries: Multi MERCI/MERCI Registry/STAR/societal registries/STRATIS/Trevo Retriever Registry/numerous investigator-initiated international studies – ENDOSTROKE and many others

Others: SWIFT/SWIFT PRIME/HERMES (7)

VISTA-Endovascular/TREAT/Imaging Workgroup of NIH StrokeNet

No imaging – GWTG, Coverdell, NIH SPOTRIAS, SNIS NVQI, NIH StrokeNet

StrokeCloud & Million Brains Initiative™

Concerning core labs

Biases of internal core labs in current trials

Unfamiliarity, lack of infrastructure or experience by most CROs

Pitfalls of commercial core labs

Ideal framework of imaging & angiography core lab integration

Role as separate entity removed from clinical or other data

Blinded

Statistical analyses conducted by independent body

Systematic battery of imaging & angiography CDE

Data collection battery - standard CDE, hierarchies

Routine collection of all elements

Extraction of all time points from DICOM tags (utilized as verification of all key time intervals)

Lessons learned - acquisition, transfer, quality

Variability in angiographic hygiene

Technique/data acquisition varies

Transmittal

Least burdensome approach at sites

Costs - routinely acquired imaging, part of data

Optimized models for multicenter endovascular studies

© DSL

Innovative imaging and interventional registries

rationale for innovative, imaging-intensive interventional registries as pivotal step in realizing precision medicine for cerebrovascular disorders

enhanced registries may serve as a model for expansion of our translational research pipeline to fully leverage the role of phase IV investigations

scope and role of registries in precision medicine

review on the history of stroke and interventional registries

data considerations

critiques or barriers to such initiatives

potential modernization of registry methods into efficient, searchable, imaging-intensive resources

simultaneously offer clinical, research and educational added value

Registries and the DAISI CRN

Need generalizability on a very large scale

Imaging triage and management - OH or MSU and beyond

What is most valuable data variable in a clinical study? Added value?

Imaging and angiography at top of hierarchy of list – before novel markers, prehospital, discharge or rehab variables or economic costs

Imaging before genomic infatuation

Quality of data is essential

Digitally preserved, can be verified, automated, must have expertise

Value of imaging

Imaging routinely acquired

Extension of clinical examination

Real-time and real-world impact

Currently, siloed and inefficient

Pivotal in decision-making

Reflects pathophysiology of effectiveness and safety

Data hierarchy - ASPECTS, collaterals, TICI, FIV over age, NIHSS

Embedded in all trials, digital perseverance

Unique verification capabilities

Failure of omission

Future of big data and precision medicine with stroke imaging

100,000s

Global sites on all continents

StrokeCloud model of anonymized and encrypted CDE of imaging data (baseline, angiography, post)

Automated eASPECTS, RAPID/Olea, CTA/MRA CFD modules, PerfAngio

Machine learning with oversight by expert readers with long track record of experience

Conclusions

Imaging data - data are king, imaging>clinical, need imaging and angiography to characterize patient pathophysiology and specific treatments beyond blackbox description of endovascular therapy

Technology potential exists now, for least burdensome approach, most efficient model, learn maximal amount regarding effectiveness and adverse events (look at modifying variables), learn early why trials fail, how to redirect without abandoning large-scale research studies

Simple linking of existing registries is fruitless and stroke imaging complexity is unlike cardiac or vascular registries

Real-time and real-world

Separation for scientific integrity

Data quality and verification

Investment - examples of how relative expense is trivial

Failure to integrate imaging and angiography ignores pathophysiology of stroke and real-world practice, will culminate in guaranteed failure in research progress from earliest trials to registries