10/19/2015
Accelerating Clinical and Translational Science with Clinical Research 2.0
Anthony S. Kim, MD, MAS Assistant Professor, Online/Mobile/User-Generated Data/ Department of Neurology; Participatory Research Medical Director, UCSF Stroke Center
ANA Translational & Clinical Research Course – July 21, 2015
Disclosures
• Anthony S. Kim, MD, MAS – Accelerating Clinical and Translational Science with Clinical Research 2.0 • Research Support – NIH NINDS (NS 086494, NS 062835, NS 081760) / NIH NCATS (TR 000004) – SanBio (related- participant recruitment registries and recruitment cores for clinical trials of modified stem cell therapy for chronic stroke and TBI) – Biogen (unrelated- predictive modeling to improve efficiency and power of stroke clinical trials) • Unlabeled/Unapproved Uses – None
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Objectives
• Review current challenges and opportunities for innovation in clinical and translational research • Explore examples of new approaches that enable and accelerate clinical discovery • Modernizing research processes • Online recruitment / pre-screening • Remote interventions / measurements / outcome assessments • Virtual clinical trials / direct-to-participant studies • Integrated Comparative Effectiveness Research • Overall Goal: Motivate thinking about ideas that may be applicable to your own research area
Challenges for Clinical Research
• High cost – Limits the number and scope of clinical questions to be addressed – Opportunity cost / less resources for other priorities – Justify ROI for key stakeholders (patients / taxpayers / society) • Slow progress – May not keep up with clinical innovations (5-10 yrs?) – Delays implementation and dissemination to the bedside • Poorly equipped for Precision Medicine – Average effect size / heterogeneity of tx effect / limited power for subgroups
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Clinical Research 1.0 - 1.5
• Modernizing typical business practices – i.e. what most other industries have already done • But fundamentally same use-case (w/ improved scalability / efficiency) – Paper records CDs cloud – Electronic IRB submission – Electronic Case Report Forms • paper / FAX / OCR / web – Clinical Trial Websites / Registries • Usually investigator-facing (not participant-facing) • Static information from a study brochure online • Not typically designed to engage participants post- enrollment
Medical officer Alexander Fleming reviews a New Drug Application at FDA headquarters in the early 1990s. The sheer volume of paper on these shelves, holding a single NDA, illustrates the amount of materials to be reviewed for the approval process.
http://www.fda.gov/AboutFDA/WhatWeDo/History/ThisWeek/ucm117841.htm
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Electronic Case Report Form
Clinical Research 2.0
• Leverage technology to enable new use cases – [Clinical Trial Management System (v1.5)] • Study calendaring, data capture • Coordinator and site training • Study Finances and IRB
• Online recruitment, interventions, measurements • Virtual / Direct-to-Participant (D2P) studies • Integrated comparative effectiveness research (CER) / Point of Care randomization
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The Challenge of Recruitment
• 30-40% of studies never recruit enough patients • > 80% have recruitment delays • ~2m patients/year needed for US clinical trials • 30-40% of clinical trial costs for recruitment
• Lasagna’s Law (version of Murphy’s Law) – The incidence of patients available for research studies sharply decreases when a clinical trial begins and returns to its original level as soon as the trial is completed.
Recruitment 1.0 2.0
• 1.0 idea: Identify patients in your clinic (Clinician or practice-based recruitment)
• 1.5 idea: Utilize EHR to efficiently identify potential candidates across clinics / sites (cohort identification) / real-time
• 2.0 idea: Participant-facing / Engage patient communities / use participant recruitment registries and patients powered networks / social media / internet advertisements
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6 10/19/2015
Recruitment 1.0 2.0
• 1.0 idea: Identify patients in your clinic (Clinician or practice-based recruitment)
• 1.5 idea: Utilize EHR to efficiently identify potential candidates across clinics / sites (cohort identification) / real-time
• 2.0 idea: Participant-facing / Engage patient communities / use participant recruitment registries and patients powered networks / social media / internet advertisements
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Recruitment: Cohort Identification
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Data elements (EHR-based)
• Demographics • Procedures – Age – ICD-9 based – Ethnicity • Laboratory – Gender – 100+ top UC ordered – Language labs and values – Marital Status • Medications – Race • Vital Status – Religion – as reported to UC Health) • Diagnosis – ICD-9 based • Vital Signs
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Cohort Identification Limitations
• Huge data harmonization effort – Expensive multi-year process even with same EHR • e.g. coding of demographics was different across sites (not CDE) • Limited clinical or domain-specific detail
• Next step would be to approach potential participants (low yield “cold” contact) if allowed • Limitations on initial contact
• Clinical care site for EHR and research sites may be distinct
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8 10/19/2015
Recruitment 1.0 2.0
• 1.0 idea: Identify patients in your clinic
• 1.5 idea: Utilize EHR to efficiently identify potential candidates across clinics / sites (cohort identification) / real-time
• 2.0 idea: Engage patient communities / participant recruitment registries and patients powered networks / social media / internet advertisements
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Examples: Real-Time Participant ID
• BPA to identify patient for SHINE (U Mich) – Admission diagnosis: stroke – Head CT ordered? – Glucose within range
• Real-time page for warfarin-related ICH in ED (HL-7 stream “sniffing”) – Age – INR > 3 – Head CT scan ordered
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9 10/19/2015
Recruitment 1.0 2.0
• 1.0 idea: Identify patients in your clinic
• 1.5 idea: Utilize EHR to efficiently identify potential candidates across clinics / sites (cohort identification) / real-time
• 2.0 idea: Engage patient communities / participant recruitment registries and patients powered networks / social media / internet advertisements
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Direct-to-Participant Recruitment
• Typical clinician or practice-based recruitment – Lower motivation: Lower rates of enrollment of eligible patients – Large infrastructure / slow startup of multiple sites • Mitigated by using an established clinical trial network
• Direct to participant-based recruitment – Higher motivation: Higher rates of enrollment for eligible patients – Potentially higher early screening rejections • Relies on easily elicited initial screening items available by self-report
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Breast Cancer Treatment Trials
www.breastcancertrials.org
Fox Trial Finder
foxtrialfinder.michaeljfox.org
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PatientsLikeMe
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UCSF Research Participant Registry
registry.ucsf.edu
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Internet-Based Recruitment
Reaching a Disease Community
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ROI for FaceBook outreach for T1D
• Campaign: 5/10 – 6/30 – 717,278 Impressions – 3,624 Clicks – 3,082 Actions – 0.51% CTR (Click through rate) – $1,082.58 Spend – $1.51 CPM (Cost per 1000 impressions) – $0.30 CPC (Cost per click)
Online Patient Eligibility Prescreen
• Diabetic Survey – Surveys started = 929 – Surveys completed = 671 (72%) – Non-Eligible = 14 • Non-Diabetic Relative Survey – Survey started = 335 – Survey completed = 109 (32%) – Non-Eligible = 27
• TOTAL started/completed = 1264 / 780 (62%) • < 2 months!
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Example: Transient Ischemic Attack
• Individuals with TIA often do not seek medical attention – High early risk for subsequent stroke (< 48h) – Effective interventions to modify this risk
• Public education campaigns – Media (print, TV, radio, etc.) • e.g. Stroke Awareness Month – Inefficient and with uncertain impact • Outcome: Media impressions?
WebTIA Rationale
• Internet has become an important source of first-line medical information – 61% of US adults look online for health info – 52% of health inquiries made on behalf of others – 60% - influenced a real-life medical decision – 56% - changed overall approach to health – 38% - affected decision whether to see a doctor
Hesse et al. NEJM 2010; Pew Internet and American Life. The Social Life of Health Information. 2009
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WebTIA Rationale
• Is it feasible to identify a population of those seeking information on TIA online in order to encourage urgent evaluation and treatment? – Online recruitment – Individual engagement – Individualized recommendation to seek medical attention – Can measure impact directly • # of individual referred for evaluation • Follow-up info on how treated
http://tia.ucsf.edu/
• Web/phone-based cross-sectional study – Online recruitment – Online eligibility screening – Online informed consent – Online enrollment – Online data collection (self-administered online questionnaire) – Telephone follow-up by two vascular neurologists • Independent assessment of the likelihood that symptoms were due to TIA/stroke
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Text Ads for Online Recruitment
• Targeted Internet Text Advertisements – Google AdWords, Google Inc., Mountain View, CA
• Keywords related to TIA/stroke/“mini-stroke” symptoms – “mini-stroke symptoms” – “TIA” and “transient ischemic attack” – “Am I having a stroke?” • Included in > 350 searches (> 3/day) – etc…
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Rapid and Efficient Recruitment
• 122 day enrollment period • 4.6 million ad impressions (37,700/day) • 26,602 hits (218 hits/day) • Average of 38 seconds per visit
• Visits from all 50 states and Washington, DC
Nationwide Reach
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Potential TIA patients identified soon after symptom onset
• 175 participants (titrated to 1-2 enrollments/day) – From 40 states and Washington DC • Mean age: 58.5 (range 23-88) – 52 (30%) were >= 65 years old • Majority were Female (n=110; 63%) • One third had not sought medical advice for their symptoms (n=60; 34%) • Many enrolled soon after symptoms occurred – 37 (22%) within 24 hours – 54 (32%) within 48 hours – 88 (52%) within 1 week
TIA / Stroke and Other Serious Diagnoses Were Common
• 68 (39%) had a probable/definite TIA or stroke
• 107 (62%) other diagnoses
- Other nonspecific symptoms - Peripheral neuropathy (7) (33) - Myocardial infarction (4) - Migraine (27) - Cervical myelopathy (3) - Seizure (12) - Bell’s Palsy (2) - Dizziness (11) - TGA (1) - Syncope (7)
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ABCD 2 Score* Inadequate to Exclude Possibility of Stroke/TIA
100%
80%
60%
40%
20%
Percent withof Participants Stroke TIAor 0% 0 1 2 3 4 5 6 n=12 n=25 n=38 n=44 n=38 n=17 n=1 Risk Score c-statistic = 0.66
* excluding BP component
Limitations
• Highly selected study population – ? more participants with nonspecific symptoms eluding previous diagnosis – ? more participants with limited alternative access to care or limited health literacy • Difficulty of authenticating of participants over the Internet and potential for informative loss to follow-up – 10% with out-of-service or incorrect numbers • Potential misclassification of TIA/Stroke outcomes by telephone – Independent adjudication/formally assessed inter- rater reliability
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Conclusions
• Identified a subpopulation that may be a promising target for interventions to reduce delays to medical evaluation for TIA – Many identified very early on after symptom onset – Some identified before formal medical evaluation
• Self-reported ABCD 2 score was inadequate to exclude TIA/stroke—even with 0 score – But personalized advice could be more persuasive than a blanket recommendation
Conclusions
• Basis for development of internet-based public health interventions – Efficiently reach large geographic areas – Very low incremental costs per participant – Quantify public health impact (ROI) with automated contact for follow-up on outcome of referral • # who would not have otherwise have sought urgent medical attention referred
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Recruitment 1.0 2.0
• 1.0 idea: Identify patients in your clinic
• 1.5 idea: Utilize EHR to efficiently identify potential candidates across clinics / sites (cohort identification) / real-time
• 2.0 idea: Engage patient communities / participant recruitment registries and patients powered networks / social media / internet advertisements
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Participant Powered Research Networks
• Direct partnership with patient advocacy groups • Direct input and engagement of participants at all stages of research (not just at recruitment phase and beyond) • Provides a mechanism delivering interventions for comparative effectiveness research trials, quality improvement programs, and dissemination of research findings • Provides “Blue Button” mechanism for sharing electronic medical record data
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25 10/19/2015
Health eHeart Study www.health-eheartstudy.org
Mark J. Pletcher, MD (UCSF); Jeffrey E. Olgin, MD (UCSF); Gregory Marcus, MD (UCSF); James H. Fowler, PhD (UCSD); Michael Blum (UCSF); Ida Sim, MD, PhD (UCSF) • Online recruitment, consent, HIPAA waiver and a participant community • Both observational studies and experimental designs possible for individual and public health interventions • Self-reported info / medical records / remote sensors and apps – EKG / Sleep Tracking / Telemetry monitoring / BP monitoring / hospitalizations (geofencing) • Research Data Stored in the Cloud
www.health-eheartstudy.org
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Health eHeart Study
Measurements 1.0 2.0
• Old measurements 1.5 – Self-reported survey instruments (online) – Patient-oriented outcome measures (PCORI, PROMIS) – Use large survey panels • Sociology, political science, etc. – Automated telephone assessments • New measurements 2.0 – High-granularity data (EKG, activity, Quantified Self) – Finger-tapping task / Vocal analysis (mPower) – Language / cognitive assessments – Geofencing for hospitalizations
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ResearchKit
• Modules for – Informed Consent – Tasks / Surveys – Sensor Data
• Only latest devices / iOS versions / No current Android support
• Data goes to a repository / data access ownership issues
• Open source
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Parkinson mPower study app
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Opportunities for Innovative Approaches
• Once infrastructure is in place, can add-in various research questions on top of this framework – Incremental costs of an additional enrollee or additional measurement can be very low
• Much easier to do repeated, longitudinal measurements cheaply and efficiently
• Bridges the gap between clinical care of the individual patient and a public health focus at the population level
Traditional Multicenter Studies 1.0
• Maintaining clinical sites expensive and slow – Multiple IRBs cIRB – Cumbersome Subcontracting Progress – Personnel Management / Coordinator Training / Regulatory Documentation – Bricks and mortar
– 75% of usual trial costs and slow ramp up • NETT / StrokeNet / NeuroNEXT
– Study often limited to subjects who live near participating centers
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Virtual Clinical Trial 2.0
• Online recruitment • Online authentication – Using publicly available datasets (address / account verification, etc.) • Online consent • Delivery of intervention by mail • Centralized study coordinating site (no sites) • Lab/Draws/Nursing visits through existing network of clinical labs and visiting nurse providers – e.g. REGARDS Study / insurance exams • Self-reported outcome assessment
REMOTE study
• Web-based virtual randomized clinical trial – Online informed consent – Online authentication of identity – Study drug mailed to participants – Participant-reported efficacy assessments via mobile phone
• Stopped due to poor enrollment – Missing in-person engagement with providers and clinicians
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Limitations
• Less helpful for studies with specialized examinations or measurements • Less efficient for complex trials – If procedures or steps are complicated, most subjects need help to get through the process
• Evolving regulatory concerns
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Population Health Interventions Practice-Based Improvements Behavioral Interventions Outcomes Research Comparative Effectiveness Research
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Integrated Comparative Effectiveness Research Example
• Choosing a statin, which one? – Clinician orders a statin; alert comes up about study – Point-of-care randomization to a “statin” in EHR – EHR shows study statin order—study drug sent to patient by study pharmacy – EHR used for follow-up for clinical events / adverse events – Link to comprehensive genotyping data (e.g. KP Research Program on Genes, Environment, and Health [RPGEH] for subgroup analyses) • Rapid-optimization within a health system to improve outcomes—a learning health system…
Adaptive / Efficient Data-Driven Approach
• e.g. Roll out and test frequent changes – 500 changes to search algorithm / year @ Google – 0.1 sec delay in response = 1% decrease in sales @ Amazon – 10,000 “RCTs” (A/B) on FaceBook at any given time • Collect real-time data and adapt more quickly – Moore’s Law • Data and processing power is cheap • Bits are scalable and efficient • How can we bring this type of nimble and hyper- efficient mindset to clinical research?
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The Digital Divide
• Reach of online access is not complete – e.g. Low rates of internet use among elderly Latinas • Mobile phone > personal computers
• In developing countries – Skipping the PC to go directly to the mobile device – 7 billion mobile subscriptions = 95.5% population – 121% of population with mobile subscriptions in developed countries – 90% of population with mobile subscriptions in developing countries • 69% in Africa
Pitfalls
• Authentication
On the internet, nobody knows you’re a dog.
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Other Limitations
• May not directly apply to acute interventions – Time sensitive situations – Emergency consent – Direct patient contact required • Specialized examinations • Specialized equipment • May be difficult to apply to complex study procedures
• Privacy – health information is a special case
Objectives
• Review current challenges and opportunities for innovation in clinical and translational research • Explore examples of new approaches that enable and accelerate clinical discovery • Online recruitment/pre-screening • Online / remote interventions / study procedures / measurements • Virtual Clinical Trials / Direct-to-Participant Studies • Integrated Comparative Effectiveness Research • Overall Goal: Motivate thinking about ideas that may be applicable to your own research area
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