Volume of White Matter Hyperintensities Predicts Neurocognitive Functioning in Children with Sickle Cell Disease
Authors: Van Der Land Veronica , Hijmans Channa T. , de Ruiter Marieke A. , Mutsaerts Henri J.M.M. , Cnossen Marjon H. , Majoie Charles B.L.M. , Nederveen Aart J. , Grootenhuis Martha A. , Fijnvandraat Karin 
Published: November 2019

Introduction

Approximately 40% of children with a severe form of sickle cell disease (SCD) will develop cerebral white matter hyperintensities (WMHs), visible on magnetic resonance imaging (MRI). This may be associated with impaired neurocognitive functioning. It is unknown whether the volume of these WHMs is associated with the degree of neurocognitive dysfunction. Our objective was to investigate the association between volume of WMHs and neurocognitive functioning.

Methods

We prospectively included children with HbSS or HbS-beta(0)thalassemia aged 8-16 years. Exclusion criteria were prior stroke and chronic blood transfusion therapy. Volume of WMHs was calculated on MRI and patients were ranked by size of WMHs. Neurocognitive function was evaluated by testing intelligence (IQ, intelligence quotient), memory, visuo-motor functioning and executive functioning. Fatigue was measured using a validated questionnaire (Pediatric Quality of Life Inventory Multidimensional Fatigue Scale, PedsQL Fatigue) in which lower scores indicate more symptoms of fatigue. For each neurocognitive outcome, univariate linear regression was used to identify which variables (age, sex and hemoglobin level) were confounders. The independent association of volume of WMHs on neurocognitive outcomes was analyzed by multivariate linear regression, adjusted for these confounders when appropriate. The explained variance (R2) refers to the independently explained variance of volume of WMHs on the neurocognitive outcome and the presented p-value corresponds to the unique contribution of volume of WMHs on the outcome, both adjusted for confounders when appropriate.

Results

We included 38 children; mean age was 12.5 ± 2.7 years, WMHs were present in 50%. Mean full-scale, verbal IQ, performal IQ and Processing Speed Index were all between 85 and 90; this is significantly lower compared to the mean norm scores of 100. Our patients had significantly more symptoms of fatigue compared to Dutch reference values. A higher volume of WMHs was significantly associated with lower scores on full-scale IQ, verbal IQ and Processing Speed Index (see table). In addition, higher volume of WMHs was associated with higher scores of total and cognitive fatigue. Standardized beta coefficients ranged from -0.350 to -0.461, indicating a substantial negative effect of an increasing volume of WMHs on neurocognitive outcome. The volume of WMHs could explain between 12.1% and 21.2% of the variance of these outcomes.

Conclusion

Our findings suggest that the volume of WMHs is an independent predictor of full-scale IQ, verbal IQ, Processing Speed Index and fatigue in children with SCD. As WMHs are mostly found in the frontal lobe, this could explain the association with processing speed, which is an executive function and thought to be located in the frontal lobe. The association between WMHs and measures of fatigue has not been investigated before. Our results suggest the PedsQL Fatigue could be a promising screening tool for larger studies as it is easy and quick to administer and has a high validity. We suggest that future studies should consider taking the total volume of WMHs into account as an independent predictor of neurocognitive outcome, instead of only the presence or absence of WMHs. Taking the volume of WMHs into account is an important approach for individualized diagnostic and treatment strategies that could be further explored in a clinical setting.

Table Prediction model of neurocognitive outcome by volume of white matter hyperintensities ß R2 p Full-scale IQ -0.382 0.146 0.018 Verbal IQ -0.460 0.212 0.004 Performal IQ -0.170 0.029 0.314 Processing Speed Index -0.461 0.212 0.005 PedsQL Fatigue, Total Score -0.350 0.121 0.025 PedsQL Fatigue, General Fatigue -0.289 0.083 0.071 PedsQL Fatigue, Cognitive Fatigue -0.352 0.123 0.026 IQ, Intelligence Quotient; PedsQL Fatigue, Pediatric Quality of Life Inventory Multidimensional Fatigue Scale.

Disclosures

No relevant conflicts of interest to declare.