Quantifying dilated perivascular spaces in children with sickle cell disease.
Kristine A KarkoskaJahnavi GollamudiRussell P SawyerDaniel WooHyacinth I HyacinthPublished in: Pediatric blood & cancer (2024)
Sickle cell disease (SCD)-related neurological effects are particularly devastating. Dilated perivascular spaces (dPVS) are a well-described component of cerebral small vessel disease in older adults without SCD. However, the burden and association of dPVS with neurological complications in children with SCD have not been described. In this study, we used the international consensus criteria to quantify dPVS in the centrum semiovale and basal ganglia in T2-weighted magnetic resonance images (MRI) of children with SCD who were randomized as part of the Silent Cerebral Infarct Transfusion (SIT) trial. We examined the relationship between global and/or regional dPVS burden and presence or area of silent cerebral infarctions, hematological measures, demographic variables, and full-scale intelligence quotient (FSIQ) scores. The study included 156 SIT trial participants who had pre-randomization and study exit MRI. Their median age was 9.6 (5-15) years, 39% were female, and 94 (60%) participants had a high dPVS burden. Participants randomized to the blood transfusion arm and who had a high dPVS burden at baseline had a moderate decline in dPVS score over 36 months compared to no change in the observation group. On multivariable logistic regression, intelligence quotient was not associated with dPVS burden. Children with SCD included in the SIT trial have a high burden of dPVS compared to children without SCD. However, dPVS do not appear to have the same pathophysiology of silent cerebral infarcts. Further study is needed to determine both their etiology and clinical relevance.
Keyphrases
- magnetic resonance
- young adults
- subarachnoid hemorrhage
- magnetic resonance imaging
- phase ii
- risk factors
- sickle cell disease
- study protocol
- heart failure
- randomized controlled trial
- open label
- computed tomography
- physical activity
- machine learning
- deep learning
- atrial fibrillation
- clinical practice
- placebo controlled
- brain injury
- optical coherence tomography
- blood brain barrier
- convolutional neural network
- high intensity