Journal
ADVANCES IN MEDICAL SCIENCES
Volume 62, Issue 1, Pages 151-157Publisher
MEDICAL UNIV BIALYSTOK
DOI: 10.1016/j.advms.2016.09.002
Keywords
Sickle cell disease; Cognitive decline; Magnetic resonance; Biomarker; Predictive modeling
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Funding
- National Institutes of Health [R01 AG13743, R03 EB-009310, R01 NS 046717]
- American Recovery and Reinvestment Act
- ITMAT fellowship, University of Pennsylvania
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Purpose: For 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 methods: This preliminary prospective cohort study included 25 children with SCD, homozygous for hemoglobin S, with no history of stroke and transcranial Doppler mean velocities below 170 cm/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. Results: We 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* algorithm. The accuracy, sensitivity and specificity were 0.84, 0.78 and 0.86, respectively. Conclusions: GM volumetric analysis predicts subsequent IQ decline for children with SCD. (C) 2017 Medical University of Bialystok. Published by Elsevier B.V. All rights reserved.
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