MétaCan
Menu
Back to cohort
Record W3096202787 · doi:10.1182/blood-2020-142002

Sickle Cell Cerebrovascular Reactivity to a CO2 Stimulus Is Both Too Little and Too Slow

2020· article· en· W3096202787 on OpenAlexaffabout
Stéphanie Forté, Olivia Sobczyk, Julien Poublanc, James Duffin, Joe Fisher, David J. Mikulis, Kevin H.M. Kuo

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsThornhill Medical (Canada)University of TorontoCentre Hospitalier de l’Université de MontréalUniversity Health Network
Fundersnot available
KeywordsCerebral blood flowMedicineVasodilationCardiologySickle cell anemiaInternal medicineStroke (engine)AnesthesiaDisease

Abstract

fetched live from OpenAlex

Background: By age 30, over 50% of sickle cell disease (SCD) patients have suffered a cerebral infarct. In response to anemia and the reduction in oxygen-carrying capacity, cerebral blood flow (CBF) increases to match metabolic demand. Increased velocity of CBF in major cerebral arteries is a strong risk factor for stroke in SCD children and adolescents. Despite generally increased CBF, silent cerebral infarcts (SCI) can still occur in patients receiving optimal transfusions. This suggests that the increased CBF does not meet metabolic demand and that vasodilatory response is compromised. Hypothesis: In adult patients with SCD, cerebrovascular reactivity (slope of the vasodilatory response to CO2 (CVR) and the steady-state CVR (amplitude) and speed of the vasodilatory response (tau) to a standardized vasodilatory stimulus CO2), are reduced compared to normal subjects. We also explored for possible associations with clinical characteristics. Methods: Functional brain imaging performed as part of routine care in adult (≥18) SCD patients (any phenotype) at the University Health Network Comprehensive Sickle Cell Center (Toronto, Canada) between 2017 and 2018 were reviewed. Patients with known cerebral vasculopathy were excluded. CVR was calculated as the change in CBF measured as the blood oxygenation level dependent (BOLD)-MRI signal, in response to a standard vasoactive stimulus of CO2 (delivered by RespirActTM). To calculate the dynamic (tau) and steady-state CVR (amplitude) components of the BOLD signal response, the PET CO2 waveform was convolved with an exponential decay function. The tau corresponding to the best fit between the convolved CO2 and BOLD signal was defined as the speed of vascular response. The slope of the regression between the convolved CO2 and BOLD signal was defined as amplitude. CVR, amplitude and tau were normalized voxel-wise relative to the mean and standard deviation of the same metric in the corresponding voxels of a previously generated atlas of 42 healthy controls (Z scores). These Z scores were averaged over the vascular territories of the brain for both grey (GM) and white matter (WM). Fisher exact and Pearson correlations were performed to identify possible associations between CVR metrics and SCD comorbid conditions, laboratory parameters, and use of disease-modifying therapy. Associations with univariate P <0.20 were included in the multiple linear regression model. Multi-collinearity was assessed. Results: Fifteen patients were included in the study. The median age was 27 [IQR22-35]. 5/15 (33.3%) were male. 9/15 (60%) were SS or S/b0 and 5/15 (33.3%) were SC. 4/15 (26.7%) were on transfusion. MRI/MRA uncovered Moya moya in 1 patient. SCI were present in 3/15 (21.4%). Compared to the reference atlas of normal subjects, CVR and amplitude were reduced both in GM and WM (mean Z-score for CVR -0.52 [-1.8 - 0.28] and -0.63 [-2.31 - 0.66]; amplitude -0.26 [-2.61 - 0.66] and -0.28 [-2.70 - 0.60] respectively). Tau was lengthened in GM and WM (mean tau Z-score +0.90 [-0.49 - 3.32] and +0.76 [-0.66 - 2.78] respectively). These abnormal metrics were observed with varying severities in all 3 main vascular territories (Figure). CVR decreased linearly with decreasing hematocrit (Hct) (r=0.59, p=0.03). There was also a trend towards lower CVR in SS or S/b0 patients (t=-1.41, p=0.18, d=0.76) and was highly collinear with Hct. Hematocrit was the only significant independent predictor of CVR metrics on multivariable regression. Conclusions: All three measures of cerebrovascular health (CVR, amplitude and tau) in SCD patients were abnormal compared to normal controls. Hematocrit appears to be the strongest independent predictor of these measures. The protocol we applied for measuring CVR provides a standardized reproducible vasodilatory stimulus, enabling comparison against a population of healthy individuals for more accurate assessment of CVR in individual subjects. Furthermore, the stimulus protocol produces rapid changes in arterial CO2 levels within one breath that can be used to measure the speed of response of the vasculature representing a novel metric of vascular performance postulated to represent vessel compliance and functional endothelial integrity. These findings show that CVR methodology represents a promising tool to assess disease state, stroke risk, and therapeutic efficacy in sickle cell patients and merits further investigation. Figure 1 Disclosures Forté: Canadian Hematology Society: Research Funding; Pfizer - Global Medical Grants: Research Funding. Sobczyk:Thornhill Research Inc.: Current Employment. Duffin:Thornhill Research Inc.: Current Employment. Fisher:Thornhill Research Inc.: Current equity holder in private company. Mikulis:Thornhill Research Inc.: Current equity holder in private company. Kuo:Bioverativ: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria; Bluebird Bio: Consultancy; Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees; Alexion: Consultancy, Honoraria; Pfizer: Consultancy, Research Funding; Apellis: Consultancy; Celgene: Consultancy.

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)0.000
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueBloodSame topicHemoglobinopathies and Related DisordersFrench-language works237,207