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Record W4367302764 · doi:10.1212/wnl.0000000000204329

Associations between cerebral small vessel disease and obstructive sleep apnea in patients with ischemic stroke and TIA (S6.006)

2023· article· en· W4367302764 on OpenAlexaffabout
Ryan T. Muir, Laavanya Dharmakulaseelan, Sandra E. Black, Brian C. Murray, Mark I. Boulos

Bibliographic record

VenueNeurology · 2023
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of TorontoFoothills Medical Centre
Fundersnot available
KeywordsObstructive sleep apneaMedicinePolysomnographyCardiologyStroke (engine)DementiaInternal medicineHyperintensitySleep apneaLeukoaraiosisLogistic regressionApneaPhysical therapyDiseaseMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Objective: (1) To examine the relationship between OSA severity and Small Vessel Disease (SVD) in patients with ischemic stroke/TIA. (1a) To assess whether these associations may be present only in select brain regions with specific types of SVD. (2) To examine the relationship between OSA, SVD and cognition. Background: Cerebral small vessel disease (SVD) is the most common cause of vascular dementia. On MRI SVD manifests as White Matter Hyperintensities (WMH), lacunes, enlarged perivascular spaces and microbleeds. Obstructive Sleep Apnea (OSA) is the most common sleep disorder and meta-analytic data supports a relationship between OSA and SVD. The purpose of this study is to examine relationships between: (1) OSA severity and SVD (2) OSA severity, SVD and cognition. Design/Methods: Patients with ischemic stroke/TIA were prospectively recruited across three independent cohort studies. Years of education, vascular risk factors, stroke severity and Montreal Cognitive Assessment scores were collected. All patients completed MRI and either an in-laboratory polysomnography (PSG) or Home Sleep Apnea Test (HSAT). OSA severity was quantified using the Apnea Hypopnea Index (AHI). The burden of small vessel disease was quantified using validated visual rating scales. Ordinal logistic regression models examined relationships between OSA and SVD, while controlling for covariates. Results: In 237 patients increasing AHI was associated with a greater burden of periventricular WMH (pWMH) OR=1.02 (CI:1.01 to 1.04, p=0.02), deep microbleeds OR=1.03 (CI:1.01 to 1.05, p=0.002) and lobar microbleeds OR=1.02 (CI:1.01 to 1.04, p=0.03). Finally, in an ordinal logistic regression model, lower cognitive scores were related to cerebral microbleeds OR=1.09 (CI:1.01 to 1.18, p = 0.03) while controlling for covariates. Conclusions: OSA severity is associated with greater periventricular WMH and cerebral microbleeds. Cerebral microbleeds are predictive of lower cognitive scores. The relationship between OSA and both lobar and deep microbleeds suggests potential associations with nocturnal hypertension and cerebral amyloid clearance. Disclosure: Dr. Muir has nothing to disclose. Ms. Dharmakulaseelan has nothing to disclose. Dr. Black has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Hoffmann-La Roche. Dr. Black has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Biogen. Dr. Black has received personal compensation in the range of $5,000-$9,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Hoffmann-La Roche. Dr. Black has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Biogen. Dr. Black has received personal compensation in the range of $10,000-$49,999 for serving on a Speakers Bureau for Biogen. The institution of Dr. Black has received research support from Hoffmann-La Roche. The institution of Dr. Black has received research support from Biogen. The institution of Dr. Black has received research support from GE Healthcare. The institution of Dr. Black has received research support from Eli Lilly. The institution of Dr. Black has received research support from Genentech. The institution of Dr. Black has received research support from NovoNordisk. The institution of Dr. Black has received research support from UCB Biopharma. The institution of Dr. Black has received research support from Alkahest Inc. The institution of Dr. Black has received research support from University of Southern California - AHEAD 3-45 Study. The institution of Dr. Murray has received research support from Wake Up Narcolepsy. Dr. Murray has received publishing royalties from a publication relating to health care. Dr. Boulos has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Jazz Pharmaceuticals. Dr. Boulos has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Paladin Labs. Dr. Boulos has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Eisai. Dr. Boulos has received research support from Interaxon. The institution of Dr. Boulos has received research support from The Mahaffy Family Research Fund. The institution of Dr. Boulos has received research support from Canadian Institutes of Health Research. The institution of Dr. Boulos has received research support from Slamen-Fast New Initiatives in Neurology Award. The institution of Dr. Boulos has received research support from Green Mountain . Dr. Boulos has received personal compensation in the range of $5,000-$9,999 for serving as a speaker with Jazz Pharmaceuticals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.255
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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