Evidence map›Paper›PMID 35487789›Full record

Observational studyMayo Clinic proceedings2022

Risk Factors for Silent Brain Infarcts and White Matter Disease in a Real-World Cohort Identified by Natural Language Processing.

Lester Y Leung, Yichen Zhou, Sunyang Fu, Chengyi Zheng, Patrick H Luetmer, David F Kallmes, Hongfang Liu, Wansu Chen, David M Kent

Open access · greenAbstract readObservational Study
In one paragraph

Observational study in Mayo Clinic proceedings, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 35% of its field
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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
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

9 authors at 4 institutions in 1 country.

Lester Y LeungDepartment of Neurology, Tufts Medical Center, Boston, MA, USA. Electronic address: lleung@tuftsmedicalcenter.org.
Yichen ZhouDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.
Sunyang FuDepartment of AI and Informatics, Mayo Clinic, Rochester, MN, USA.
Chengyi ZhengDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.
Patrick H LuetmerDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
David F KallmesDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Hongfang LiuDepartment of AI and Informatics, Mayo Clinic, Rochester, MN, USA.
Wansu ChenDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.
David M KentPredictive Analytics and Comparative Effectiveness Center, Tufts Medical Center, Boston, MA, USA.
Kaiser Permanente · USMayo Clinic · USMayo Clinic in Arizona · USTufts Medical Center · US

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
NINDS NIH HHS R01 NS102233
6 · The paper itself

Abstract

objectiveTo assess the frequency of silent brain infarcts (SBIs) and white matter disease (WMD) and associations with stroke risk factors (RFs) in a real-world population. PATIENTS AND

methodsThis was an observational study of patients 50 years or older in the Kaiser Permanente Southern California health system from January 1, 2009, through June 30, 2019, with head computed tomography or magnetic resonance imaging for nonstroke indications and no history of stroke, transient ischemic attack, or dementia. A natural language processing (NLP) algorithm was applied to the electronic health record to identify individuals with reported SBIs or WMD. Multivariable Poisson regression estimated risk ratios of demographic characteristics, RFs, and scan modality on the presence of SBIs or WMD.

resultsAmong 262,875 individuals, the NLP identified 13,154 (5.0%) with SBIs and 78,330 (29.8%) with WMD. Stroke RFs were highly prevalent. Advanced age was strongly associated with increased risk of SBIs (adjusted relative risks [aRRs], 1.90, 3.23, and 4.72 for those aged in their 60s, 70s, and ≥80s compared with those in their 50s) and increased risk of WMD (aRRs, 1.79, 3.02, and 4.53 for those aged in their 60s, 70s, and ≥80s compared with those in their 50s). Magnetic resonance imaging was associated with a reduced risk of SBIs (aRR, 0.87; 95% CI, 0.83 to 0.91) and an increased risk of WMD (aRR, 2.86; 95% CI, 2.83 to 2.90). Stroke RFs had modest associations with increased risk of SBIs or WMD.

conclusionAn NLP algorithm can identify a large cohort of patients with incidentally discovered SBIs and WMD. Advanced age is strongly associated with incidentally discovered SBIs and WMD.

Indexed as

LeukoencephalopathiesStrokeAgedBrain InfarctionHumansMagnetic Resonance ImagingNatural Language ProcessingRisk Factors

Identifiers

PMID35487789
PMCPMC9284412
OpenAlexW4224936411

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.