ArticleAdvances in medical sciences2017
Brain morphometric analysis predicts decline of intelligence quotient in children with sickle cell disease: A preliminary study.
Article in Advances in medical sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Brain Volumes and Cognition in Patients with Sickle Cell Anaemia: A Systematic Review and Meta-Analysis.Children (Basel, Switzerland) · 2023Review
- Structural connectivity mediates the relationship between blood oxygenation and cognitive function in sickle cell anemia.Blood advances · 2023Article
- Effects of regional brain volumes on cognition in sickle cell anemia: A developmental perspective.Frontiers in neurology · 2023Article
- Quantification of Silent Cerebral Infarction on High-Resolution FLAIR and Cognition in Sickle Cell Anemia.Frontiers in neurology · 2022Article
- Considerations for Selecting Cognitive Endpoints and Psychological Patient-Reported Outcomes for Clinical Trials in Pediatric Patients With Sickle Cell Disease.Frontiers in neurology · 2022Review
- MRI detection of brain abnormality in sickle cell disease.Expert review of hematology · 2021Article
- Vascular Instability and Neurological Morbidity in Sickle Cell Disease: An Integrative Framework.Frontiers in neurology · 2019Article
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Authors and funding
6 authors.
Funding
Abstract
purposeFor children with sickle cell disease (SCD) and at low risk category of stroke, we aim to build a predictive model to differentiate those with decline of intelligence-quotient (IQ) from counterparts without decline, based on structural magnetic-resonance (MR) imaging volumetric analysis. MATERIALS AND
methodsThis preliminary prospective cohort study included 25 children with SCD, homozygous for hemoglobin S, with no history of stroke and transcranial Doppler mean velocities below 170cm/s at baseline. We administered the Kaufman Brief Intelligence Test (K-BIT) to each child at yearly intervals for 2-4 years. Each child underwent MR examination within 30 days of the baseline K-BIT evaluation date. We calculated K-BIT change rates, and used rate of change in K-BIT to classify children into two groups: a decline group and a non-decline group. We then generated predictive models to predict K-BIT decline/non-decline based on regional gray-matter (GM) volumes computed from structural MR images.
resultsWe identified six structures (the left median cingulate gyrus, the right middle occipital gyrus, the left inferior occipital gyrus, the right fusiform gyrus, the right middle temporal gyrus, the right inferior temporal gyrus) that, when assessed for volume at baseline, are jointly predictive of whether a child would suffer subsequent K-BIT decline. Based on these six regional GM volumes and the baseline K-BIT, we built a prognostic model using the K
conclusionsGM volumetric analysis predicts subsequent IQ decline for children with SCD.
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