RF1NS102233
Project Grant
Overview
Grant Description
Covert Cerebrovascular Disease Detected by Artificial Intelligence (C2D2AI): A Platform for Pragmatic Evidence Generation for Stroke and Dementia Prevention - Project Summary
It is a common clinical occurrence that neuroimaging scans obtained in the course of routine clinical care discover covert cerebrovascular disease (CCD), comprising covert brain infarction (CBI) and white matter disease (WMD), in patients with no history of stroke or transient ischemic attack. Indeed, epidemiologic studies indicate that covert CBI is far more common than clinically-evident strokes, and these imaging findings are strong, independent risk factors for future stroke and dementia. However, there are no proven preventive treatments or guidelines for initiating risk factor-modifying therapy.
While there is strong evidence that antiplatelet therapy and statin therapy are effective in preventing recurrent stroke in patients with prior stroke, it is unclear to what degree these results apply to patients with CCD. Additionally, patients and providers are rarely aware of these findings, even when they are detected.
As part of our previous grant (R01-NS102233), we developed a natural language processing (NLP) algorithm to identify incidentally discovered (ID-) CCD from neuroimaging reports, which we ported into a large integrated healthcare system. We identified a cohort of almost a quarter million patients over age 50 who received either a head CT or MRI and were stroke- and dementia-free at the time of the index scan.
Key findings of our analyses include: NLP can identify ID-CBI and ID-WBD from neuroimage reports as well as human readers; that ID-CCD is present in about one-third of these scans in an age- and vascular risk factor dependent manner; that ID-CCD increases the risk of future stroke and future dementia by approximately 2- to 3-fold; that NLP is able to extract additional important prognostic information on WMD severity from routinely obtained imaging reports; and finally, these patients are generally not given risk factor modifying treatment following the discovery of ID-CCD.
Given the difficulty of recruiting this at-risk population, we now propose to leverage this NLP system as a platform to plan and conduct prospective randomized comparative effectiveness studies to identify optimal treatment strategies for ID-CCD. Thus, our aims are:
AIM 1: To inform the enrollment criteria of prevention clinical trials and ensure consistency of findings, we will expand the cohort to Kaiser Permanente Northern California and further characterize patients with ID-CCD regarding their future stroke and dementia risk.
AIM 2: To determine optimal treatment algorithms, we will leverage established simulation models to estimate the treatment effects of different risk factor modification algorithms in patients with ID-CCD on future stroke and dementia.
AIM 3: To determine optimal recruitment strategies in demographically diverse populations, we will examine the feasibility of recruiting this novel population based on NLP-identified findings both prospectively (i.e. concurrent with clinical identification) and retrospectively (as identified from pre-existing scans).
AIM 4: Based on the above findings, we will plan a multicenter clinical trial for the prevention of stroke and dementia in this population with CCD.
It is a common clinical occurrence that neuroimaging scans obtained in the course of routine clinical care discover covert cerebrovascular disease (CCD), comprising covert brain infarction (CBI) and white matter disease (WMD), in patients with no history of stroke or transient ischemic attack. Indeed, epidemiologic studies indicate that covert CBI is far more common than clinically-evident strokes, and these imaging findings are strong, independent risk factors for future stroke and dementia. However, there are no proven preventive treatments or guidelines for initiating risk factor-modifying therapy.
While there is strong evidence that antiplatelet therapy and statin therapy are effective in preventing recurrent stroke in patients with prior stroke, it is unclear to what degree these results apply to patients with CCD. Additionally, patients and providers are rarely aware of these findings, even when they are detected.
As part of our previous grant (R01-NS102233), we developed a natural language processing (NLP) algorithm to identify incidentally discovered (ID-) CCD from neuroimaging reports, which we ported into a large integrated healthcare system. We identified a cohort of almost a quarter million patients over age 50 who received either a head CT or MRI and were stroke- and dementia-free at the time of the index scan.
Key findings of our analyses include: NLP can identify ID-CBI and ID-WBD from neuroimage reports as well as human readers; that ID-CCD is present in about one-third of these scans in an age- and vascular risk factor dependent manner; that ID-CCD increases the risk of future stroke and future dementia by approximately 2- to 3-fold; that NLP is able to extract additional important prognostic information on WMD severity from routinely obtained imaging reports; and finally, these patients are generally not given risk factor modifying treatment following the discovery of ID-CCD.
Given the difficulty of recruiting this at-risk population, we now propose to leverage this NLP system as a platform to plan and conduct prospective randomized comparative effectiveness studies to identify optimal treatment strategies for ID-CCD. Thus, our aims are:
AIM 1: To inform the enrollment criteria of prevention clinical trials and ensure consistency of findings, we will expand the cohort to Kaiser Permanente Northern California and further characterize patients with ID-CCD regarding their future stroke and dementia risk.
AIM 2: To determine optimal treatment algorithms, we will leverage established simulation models to estimate the treatment effects of different risk factor modification algorithms in patients with ID-CCD on future stroke and dementia.
AIM 3: To determine optimal recruitment strategies in demographically diverse populations, we will examine the feasibility of recruiting this novel population based on NLP-identified findings both prospectively (i.e. concurrent with clinical identification) and retrospectively (as identified from pre-existing scans).
AIM 4: Based on the above findings, we will plan a multicenter clinical trial for the prevention of stroke and dementia in this population with CCD.
Awardee
Funding Goals
(1) TO SUPPORT EXTRAMURAL RESEARCH FUNDED BY THE NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE (NINDS) INCLUDING: BASIC RESEARCH THAT EXPLORES THE FUNDAMENTAL STRUCTURE AND FUNCTION OF THE BRAIN AND THE NERVOUS SYSTEM, RESEARCH TO UNDERSTAND THE CAUSES AND ORIGINS OF PATHOLOGICAL CONDITIONS OF THE NERVOUS SYSTEM WITH THE GOAL OF PREVENTING THESE DISORDERS, RESEARCH ON THE NATURAL COURSE OF NEUROLOGICAL DISORDERS, IMPROVED METHODS OF DISEASE PREVENTION, NEW METHODS OF DIAGNOSIS AND TREATMENT, DRUG DEVELOPMENT, DEVELOPMENT OF NEURAL DEVICES, CLINICAL TRIALS, AND RESEARCH TRAINING IN BASIC, TRANSLATIONAL AND CLINICAL NEUROSCIENCE. THE INSTITUTE IS THE LARGEST FUNDER OF BASIC NEUROSCIENCE IN THE US AND SUPPORTS RESEARCH ON TOPICS INCLUDING BUT NOT LIMITED TO: DEVELOPMENT OF THE NERVOUS SYSTEM, INCLUDING NEUROGENESIS AND PROGENITOR CELL BIOLOGY, SIGNAL TRANSDUCTION IN DEVELOPMENT AND PLASTICITY, AND PROGRAMMED CELL DEATH, SYNAPSE FORMATION, FUNCTION, AND PLASTICITY, LEARNING AND MEMORY, CHANNELS, TRANSPORTERS, AND PUMPS, CIRCUIT FORMATION AND MODULATION, BEHAVIORAL AND COGNITIVE NEUROSCIENCE, SENSORIMOTOR LEARNING, INTEGRATION AND EXECUTIVE FUNCTION, NEUROENDOCRINE SYSTEMS, SLEEP AND CIRCADIAN RHYTHMS, AND SENSORY AND MOTOR SYSTEMS. IN ADDITION, THE INSTITUTE SUPPORTS BASIC, TRANSLATIONAL AND CLINICAL STUDIES ON A NUMBER OF DISORDERS OF THE NERVOUS SYSTEM INCLUDING (BUT NOT LIMITED TO): STROKE, TRAUMATIC INJURY TO THE BRAIN, SPINAL CORD AND PERIPHERAL NERVOUS SYSTEM, NEURODEGENERATIVE DISORDERS, MOVEMENT DISORDERS, BRAIN TUMORS, CONVULSIVE DISORDERS, INFECTIOUS DISORDERS OF THE BRAIN AND NERVOUS SYSTEM, IMMUNE DISORDERS OF THE BRAIN AND NERVOUS SYSTEM, INCLUDING MULTIPLE SCLEROSIS, DISORDERS RELATED TO SLEEP, AND PAIN. PROGRAMMATIC AREAS, WHICH ARE PRIMARILY SUPPORTED BY THE DIVISION OF NEUROSCIENCE, ARE ALSO SUPPORTED BY THE DIVISION OF EXTRAMURAL ACTIVITIES, THE DIVISION OF TRANSLATIONAL RESEARCH, THE DIVISION OF CLINICAL RESEARCH, THE OFFICE OF TRAINING AND WORKFORCE DEVELOPMENT, THE OFFICE OF PROGRAMS TO ENHANCE NEUROSCIENCE WORKFORCE DEVELOPMENT, AND THE OFFICE OF INTERNATIONAL ACTIVITIES. (2) TO EXPAND AND IMPROVE THE SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, TO INCREASE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION. TO UTILIZE THE SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM, TO STIMULATE AND FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH AND DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO FOSTER TECHNOLOGY TRANSFER BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION.
Grant Program (CFDA)
Place of Performance
Boston,
Massachusetts
021111552
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 6% from $2,894,758 to $3,076,281.
Tufts Medical Center Parent was awarded
C2D2AI: Platform for Stroke & Dementia Prevention
Project Grant RF1NS102233
worth $3,076,281
from the National Institute of Allergy and Infectious Diseases in February 2023 with work to be completed primarily in Boston Massachusetts United States.
The grant
has a duration of 3 years and
was awarded through assistance program 93.310 Trans-NIH Research Support.
The Project Grant was awarded through grant opportunity Administrative Supplements to Existing NIH Grants and Cooperative Agreements (Parent Admin Supp Clinical Trial Optional).
Status
(Ongoing)
Last Modified 9/5/25
Period of Performance
2/1/23
Start Date
1/31/26
End Date
Funding Split
$3.1M
Federal Obligation
$0.0
Non-Federal Obligation
$3.1M
Total Obligated
Activity Timeline
Transaction History
Modifications to RF1NS102233
Additional Detail
Award ID FAIN
RF1NS102233
SAI Number
RF1NS102233-3985750108
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Nonprofit With 501(c)(3) IRS Status (Other Than An Institution Of Higher Education)
Awarding Office
75NQ00 NIH National Institute of Neurological Disorders and Stroke
Funding Office
75NA00 NIH OFFICE OF THE DIRECTOR
Awardee UEI
MY2ERHGDV956
Awardee CAGE
9G994
Performance District
MA-07
Senators
Edward Markey
Elizabeth Warren
Elizabeth Warren
Budget Funding
Federal Account | Budget Subfunction | Object Class | Total | Percentage |
---|---|---|---|---|
National Institute on Aging, National Institutes of Health, Health and Human Services (075-0843) | Health research and training | Grants, subsidies, and contributions (41.0) | $2,894,758 | 100% |
Modified: 9/5/25