Evidence map›Paper›PMID 40575848›Full record

ArticleStroke2025

Association of Covert Cerebrovascular Disease With Falls Requiring Medical Attention.

Úna Clancy, Eric J Puttock, Wansu Chen, William N Whiteley, Ellen M Vickery, Lester Y Leung, Patrick H Luetmer, David F Kallmes, Sunyang Fu, Chengyi Zheng and 2 more

Abstract read
In one paragraph

Article in Stroke, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Úna ClancyCentre for Clinical Brain Sciences, Edinburgh Imaging, and UK Dementia Research Institute, University of Edinburgh, United Kingdom (U.C., W.N.W.).ORCID 0000-0002-8062-7537
Eric J PuttockDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (E.J.P., W.C., C.Z.).ORCID 0000-0003-3963-314X
Wansu ChenDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (E.J.P., W.C., C.Z.).
William N WhiteleyCentre for Clinical Brain Sciences, Edinburgh Imaging, and UK Dementia Research Institute, University of Edinburgh, United Kingdom (U.C., W.N.W.).ORCID 0000-0002-4816-8991
Ellen M VickeryPredictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS) (E.M.V., D.M.K.), Tufts Medical Center, Boston, MA.
Lester Y LeungDepartment of Neurology (L.Y.L.), Tufts Medical Center, Boston, MA.ORCID 0000-0002-5027-7740
Patrick H LuetmerDepartment of Radiology, Mayo Clinic, Rochester, MN (P.H.L., D.F.K.).ORCID 0000-0003-4660-7644
David F KallmesDepartment of Radiology, Mayo Clinic, Rochester, MN (P.H.L., D.F.K.).
Sunyang FuCenter for Translational AI Excellence and Applications in Medicine, University of Texas Health Science Center, Houston (S.F., H.L.).
Chengyi ZhengDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (E.J.P., W.C., C.Z.).ORCID 0000-0002-4194-0029
Hongfang LiuCenter for Translational AI Excellence and Applications in Medicine, University of Texas Health Science Center, Houston (S.F., H.L.).ORCID 0000-0003-2570-3741
David M KentPredictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS) (E.M.V., D.M.K.), Tufts Medical Center, Boston, MA.ORCID 0000-0002-9205-5070

Funding

Enabling Comparative Effectiveness Research in Silent Brain Infarction Through Natural Language Processing and Big DataR01NS102233 · NINDS · TUFTS MEDICAL CENTER · PI DAVID M KENT · 2017 to 2026
$3.4M
Covert Cerebrovascular Disease Detected by Artificial Intelligence (C2D2AI): A Platform for Pragmatic Evidence Generation for Stroke and Dementia PreventionRF1NS102233 · NINDS · TUFTS MEDICAL CENTER · PI KENT, DAVID M · 2023 to 2025
$3.1M
Covert Cerebrovascular Disease Detected by Artificial Intelligence (C2D2AI): Pragmatic Neuroimaging Biomarkers for Future Stroke and Dementia RiskR01NS134859 · NINDS · TUFTS MEDICAL CENTER · PI DAVID M KENT · 2024 to 2026
$2.3M
NINDS NIH HHS R01 NS102233NINDS NIH HHS R01 NS134859NINDS NIH HHS RF1 NS102233
6 · The paper itself

Abstract

backgroundThe impact of covert cerebrovascular disease on falls in the general population is not well-known. Here, we determine the time to a first fall following incidentally detected covert cerebrovascular disease during a clinical neuroimaging episode.

methodsThis longitudinal cohort study assessed computed tomography (CT) and magnetic resonance imaging from 2009 to 2019 of patients aged >50 years registered with Kaiser Permanente Southern California which is a healthcare organization combining health plan coverage with coordinated medical services, excluding those with before stroke/dementia. We extracted evidence of incidental covert brain infarcts (CBI) and white matter hyperintensities/hypoattenuation (WMH) from imaging reports using natural language processing. We examined associations of CBI and WMH with falls requiring medical attention, using Cox proportional hazards regression models with adjustment for 12 variables including age, sex, ethnicity multimorbidity, polypharmacy, and incontinence.

resultsWe assessed 241 050 patients, mean age 64.9 (SD, 10.42) years, 61.3% female, detecting covert cerebrovascular disease in 31.1% over a mean follow-up duration of 3.04 years. A recorded fall occurred in 21.2% (51 239/241 050) during follow-up. On CT, single fall incidence rate/1000 person-years (p-y) was highest in individuals with both CBI and WMH on CT (129.3 falls/1000 p-y [95% CI, 123.4-135.5]), followed by WMH (109.9 falls/1000 p-y [108.0-111.9]). On magnetic resonance imaging, the incidence rate was the highest with both CBI and WMH (76.3 falls/1000 p-y [95% CI, 69.7-83.2]), followed by CBI (71.4 falls/1000 p-y [95% CI, 65.9-77.2]). The adjusted hazard ratio for single index fall in individuals with CBI on CT was 1.13 (95% CI, 1.09-1.17); versus magnetic resonance imaging 1.17 (95% CI, 1.08-1.27). On CT, the risk for single index fall incrementally increased for mild (1.37 [95% CI, 1.32-1.43]), moderate (1.57 [95% CI, 1.48-1.67]), or severe WMH (1.57 [95% CI, 1.45-1.70]). On magnetic resonance imaging, index fall risk similarly increased with increasing WMH severity: mild (1.11 [95% CI, 1.07-1.17]), moderate (1.21 [95% CI, 1.13-1.28]), and severe WMH (1.34 [95% CI, 1.22-1.46]).

conclusionsIn a large population with neuroimaging, CBI and WMH are independently associated with greater risks of an index fall. Increasing severities of WMH are associated incrementally with fall risk across imaging modalities.

Indexed as

Accidental FallsCerebrovascular DisordersAgedAged, 80 and overCohort StudiesFemaleHumansIncidenceLongitudinal StudiesMagnetic Resonance ImagingMaleMiddle AgedTomography, X-Ray Computedaccidental fallsbrain infarctionnatural language processingwhite matter

Identifiers

PMID40575848
PMCPMC12291196

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.