MétaCan
Menu
← Back to cohort

Quantitative MRI of Hemodynamic Compromise in Children with Sickle Cell Disease: New Insights into Pathophysiology

2015· article· en· W2582786483 on OpenAlexaff
Paula Croal, Malambing G. Serafin, Przemysław Kosiński, Jackie Leung, Suzan Williams, Andrea Kassner

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsMedicinePathophysiologyStroke (engine)Cerebral blood flowStenosisCardiologyInternal medicineMagnetic resonance imagingAcute chest syndromeHemodynamicsPopulationDiseaseSickle cell anemiaRadiology

Abstract

fetched live from OpenAlex

Abstract Introduction: Sickle cell disease (SCD) is the leading cause of overt stroke in children, resulting in increased morbidity and mortality (Prengler et al. 2002). Neurologic injury in SCD is caused by a cascade of complex physiological processes that may manifest as abnormal cerebral blood flow (CBF), abnormal oxygen extraction fraction (OEF), and ultimately lead to oxygen deprivation of the brain tissue (Adams et al. 2007., Cheung et al. 2002), however, the underlying pathophysiology is not well understood. While much research has focused on the role of vasculopathy in neurologic injury in SCD, over one third of children with sickle cell disease who suffer an overt ischemic event do not present with arterial stenosis (Hulbert et al. 2011). Extending the work of Nur et al. (2009), we have recently shown in children without stenosis in SCD that elevated cerebral blood flow and diminished cerebrovascular reserve is associated with the severity of anemia (Kosinski et al. 2015). However, to date, little is known about the relative contributions of non-vasculopathic hemodynamic compromise that may occur in children with SCD. Therefore, a comprehensive model of pathophysiology in this population will help stratify stroke risk and guide therapeutic strategies. Aim: To noninvasively quantify both CBF and OEF in children with SCD using magnetic resonance imaging. Methods: 11 patients (7M/4F; age 14 ± 2.7 years (mean ± SD)) with SCD and no history of stroke nor arterial stenosis were imaged on a 3T MRI system. 9 patients were on hydroxyurea (average dose: 19.7 ± 3 mg/kg/day). Hematological parameters were acquired at a clinic visit no more than one month prior to the MRI scan (Table 1). Pulsed arterial spin labelling MRI was used to noninvasively assess grey matter cerebral blood flow according to a standard kinetic model. A T2*-weighted flow-compensated image provided phase information, with the phase signal difference between the sagittal sinus and surrounding tissue used to quantify venous oxygenation (SvO2) (Driver et al. 2014). Arterial oxygen saturation (SaO2) was measured via peripheral pulse oximetry and OEF was determined by (SaO2 - SvO2/SaO2) according to Fick's principle of arteriovenous difference. Results: A significant positive association was observed between CBF and OEF (p = 0.82, p = 0.02, pearson product moment correlation coefficient), as shown in Figure 1. Mean values (± SD) were 0.75 ± 0.08, 0.23 ± 0.08 and 54.1 ± 11 ml/min/100g for SvO2, OEF and CBF respectively. Discussion: The observed correlation between CBF and OEF suggests that children with SCD who do not present with vasculopathy, utilize both the perfusion and metabolic reserve in order to meet the cerebral demand for O2. This allows CBF and OEF to increase simultaneously rather than sequentially as proposed by Powers et al. (1991). This non-sequential hemodynamic compromise is in agreement with previous literature in cerebrovascular disease (Kanno et al. 1988) and may serve to minimise increases in cardiac output (Varat et al. 1972). This data suggests that elevated OEF may serve as an early risk marker for overt stroke in addition to elevated blood flow velocity and thus may help better screen for those who will most benefit from transfusion therapy. Table 1. SCD Patients Sample Size (N) 11 Gender (M/F) 7/4 Age (y) 13.7 ± 2.7 BMI (Kg/m2) 18.8 ± 3.0 Hematocrit 0.282 ± 0.03 Hemoglobin (g/L) 100 ± 10.3 SaO2 0.99 ± 0.02 SvO2 0.75 ± 0.08 Absolute Reticulocyte (K/uL) 205 ± 72 Neutrophil Count (K/uL) 5.3 ± 3.7 Clinical parameters acquired within 30 days of the MRI session Figure 1. A significant positive association was observed between grey matter cerebral blood flow and oxygen extraction fraction (r = 0.76, p = 0.002) in children with sickle cell disease Figure 1. A significant positive association was observed between grey matter cerebral blood flow and oxygen extraction fraction (r = 0.76, p = 0.002) in children with sickle cell disease Disclosures No relevant conflicts of interest to declare.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueBlood→Same topicHemoglobinopathies and Related Disorders→French-language works237,207→