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Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Objective

To explore academic outcomes in childhood-onset systemic lupus erythematosus (cSLE) and their relationship to variables such as demographic and socioeconomic status, neurocognitive functioning, behavioral/emotional adjustment, and cSLE disease status.

Methods

Forty pairs of children diagnosed with cSLE and healthy best friend controls were rated by parents on a standardized scale of school competence. Information about participants' demographic and socioeconomic status was obtained, along with measures of cSLE disease activity and damage. All of the participants received formal neurocognitive testing and were also rated on standardized scales of behavioral/emotional adjustment and executive functioning.

Results

Compared to healthy controls, school competence was rated as lower in the cSLE group, although the groups did not differ significantly on indices of cognitive, behavioral, emotional, or executive functioning. School competence ratings were correlated with reading and mathematics achievement test scores in both groups, and with ratings of mental self-regulation in the cSLE group. School competence ratings were correlated with measures of cSLE disease activity and treatment intensity.

Conclusion

cSLE is associated with inferior parent-rated academic outcomes compared to those noted in demographically-matched peers, despite similar neurocognitive function. The adverse academic outcomes that distinguish children with cSLE from their demographically-matched peers appear to be mediated by SLE disease activity and treatment.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Systemic lupus erythematosus (SLE) is associated with significant morbidity, negatively affecting health-related quality of life (HRQOL) (1). Academic functioning represents a critical aspect of HRQOL in childhood and adolescence. However, it has attracted surprisingly limited research interest, despite concerns that children with SLE who perform poorly in school go on to meet fewer educational milestones (e.g., high school or college graduation), have less long-term occupational success, and experience higher rates of adult mental illness and substance abuse (2–6). The available data suggest that children with childhood-onset SLE (cSLE) are at risk for adverse academic outcomes (1, 7, 8). If cSLE affects academic functioning, then it is important to explore the mechanisms that underlie this effect and incorporate this knowledge into disease management.

In adults, adverse effects of SLE have been shown to impair cognitive and psychiatric functioning (9). Despite reports that cSLE is associated with cognitive deficits (10) and psychiatric morbidity (11, 12), most previous studies have been limited to chart reviews lacking a well-matched control group. Conversely, a recent case–control study found the rate of neurocognitive dysfunction (NCD) in cSLE to be elevated relative to general population norms but comparable to that of controls who were closely matched on demographic variables (13). This methodologically-rigorous study raises questions regarding the contribution of cSLE disease and treatment factors to cognitive and psychiatric morbidity. It also highlights the importance of exploring other factors besides NCD that might contribute to lower school functioning in patients with cSLE.

The current study used a case–control design to achieve 3 goals: 1) determine whether individuals with cSLE have worse academic functioning than their peers of similar demographic and socioeconomic backgrounds; 2) investigate whether these matched groups differ with respect to cognitive, behavioral, and emotional functioning; and 3) explore demographic, cognitive, behavioral, emotional, and disease-related correlates of school functioning within samples of children with cSLE and unaffected peers.

Significance & Innovations

  • This study found significantly inferior academic outcomes in children and adolescents with systemic lupus erythematosus (cSLE) than in demographically-matched controls.

  • Elevated cSLE disease activity is significantly associated with inferior academic outcomes, although we did not find clear evidence that neurocognitive deficits mediate this relationship. Disruption of school attendance due to cSLE and its treatment may significantly impede patients' academic functioning.

  • Management of cSLE should consider the disruptive effect of illness and treatment upon school attendance and performance, emphasizing the importance of minimizing such disruption by optimizing patient care and treatment adherence.

SUBJECTS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Subjects.

Forty children and adolescents with cSLE and 40 same-sex best friend controls of similar demographic characteristics participated in a study of functional and structural neuroimaging and cognitive functioning in cSLE, conducted at 2 tertiary pediatric rheumatology centers. Analyses of neuroimaging data from the study will be presented in a future article, and are not discussed herein.

All of the participants spoke English as their primary language. This study was approved by the institutional review boards of both institutions and is in accordance with the ethical standards established in the 1964 Declaration of Helsinki. Prior to participation, the study was explained to each participant and their parent, and written informed consent was obtained from the parents of all participants. Written assent was also obtained from all participants ages >11 years.

cSLE.

Participants with cSLE fulfilled the updated American College of Rheumatology classification criteria prior to age 17 years (14). To be eligible for participation, a patient had to be between ages 9 and 18 years at the time of enrollment in the study. Patients with cSLE were excluded from participation if they had a history of comorbid conditions affecting their neurocognitive functioning prior to the diagnosis of cSLE, and if they had known structural brain abnormalities, neuropathies, or movement disorders.

Controls.

Each index patient with cSLE was asked to identify a friend who was within 1 year of their age, of the same sex, and in the same school grade. This “best friend approach” has been shown to result in good case–control matches on demographic variables (15). Controls had to be healthy, without known structural brain abnormalities or known NCD. No potential controls needed to be excluded from participation by these criteria.

Study assessment.

In this cross-sectional study, participants were evaluated during a dedicated research visit lasting approximately 3 to 4 hours. Besides a physical examination and a review of systems, all of the study participants underwent a thorough neurocognitive assessment. Data collected during this research visit are described below.

Demographics.

Demographic information related to ethnicity, maternal education, number of parents in the household, and family income was collected by parent report.

Disease activity and severity (cSLE subjects only).

Disease activity was measured using the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI- 2K) and the British Isles Lupus Assessment Group Index (BILAG), while disease damage was assessed using the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (16, 17). The daily dose of oral prednisone (if prescribed) was recorded as a surrogate of treatment intensity.

Academic functioning.

Academic functioning was assessed via the School Competence scale of the Child Behavior Checklist (CBCL), a standardized questionnaire completed by parents of the study participants (18). Academic functioning can be difficult to measure due to a lack of consistent access to original school records, variations in grading schemes across schools and grade/age levels, and the fact that academic knowledge (assessed via formal testing) is only one of several contributors to classroom performance. Consistent with our goal of assessing classroom performance relative to norms on a common metric, the CBCL School Competence scale is comprised of standardized ratings of the child's functioning across multiple academic domains (e.g., reading/language arts, arithmetic), as well as implementation of academic interventions. Ratings on the CBCL School Competence scale are converted to a single T score based on age- and sex-linked norms, with higher scores indicating better functioning. Table 1 provides descriptions of the other psychometric information obtained from the participants via parent questionnaires and formal neurocognitive testing, along with the functional domains they measure. Further details about these measures are shown below.

Table 1. Descriptions of psychometric variables*
 Functional domainPopulation norms, mean ± SDValence (higher scores reflect …)
  • *

    More information about the measures is shown in the Methods. WASI = Wechsler Abbreviated Scale of Intelligence; IQ = intelligence quotient; WRAML-2 = Wide Range Assessment of Memory and Learning 2; WJ-III = Woodcock-Johnson III Tests of Achievement; CPT-II = Conners' Continuous Performance Test II; CBCL = Child Behavior Checklist; CDI = Children's Depression Inventory; BRIEF = Behavior Rating Inventory of Executive Function.

  • Wechsler Intelligence Scale for Children, 4th edition (age <17 years), and Wechsler Adult Intelligence Scale, 4th edition (age ≥17 years).

WASI Full-Scale IQGeneral intelligence100 ± 15Better functioning
Wechsler Intelligence Scales Working Memory indexWorking memory100 ± 15Better functioning
Wechsler Intelligence Scales Processing Speed indexPsychomotor speed100 ± 15Better functioning
WRAML-2 Memory Screening indexVerbal and visual memory100 ± 15Better functioning
WJ-III Letter-Word Identification subtestReading and decoding skills100 ± 15Better functioning
WJ-III Calculation subtestArithmetic calculation skills100 ± 15Better functioning
CPT-II omissionsAttention50 ± 10Worse functioning
CPT-II commissionsAttention/impulsivity50 ± 10Worse functioning
CPT-II hit reaction time standard errorAttention50 ± 10Worse functioning
CBCL Anxious/Depressed scaleParent-reported symptoms of depression and anxiety50 ± 10Worse functioning
CBCL Externalizing ProblemsParent-reported symptoms of externalizing behavior (e.g., aggression, conduct problems)50 ± 10Worse functioning
CBCL Total ProblemsOverall parent-report behavior and emotion symptoms50 ± 10Worse functioning
CDI total scoreChild's report of depression symptoms50 ± 10Worse functioning
BRIEF Behavioral Regulation IndexParent-reported behavioral self-regulation (e.g., impulse control, emotional control)50 ± 10Worse functioning
BRIEF Metacognition IndexParent-reported mental self-regulation (e.g., organization, planning, self-initiation)50 ± 10Worse functioning
BRIEF Global Executive CompositeOverall self-regulation50 ± 10Worse functioning
Behavioral and emotional functioning.

Participants' behavioral and emotional functioning was obtained by parent report on the CBCL (18). Three CBCL indices were studied. The Externalizing Problems index assesses delinquent and aggressive behaviors. The Anxious/Depressed subscale focuses on mood symptoms and was chosen over the broader Internalizing index because the latter includes physical symptoms that could reflect legitimate medical concerns, rather than mood (19). The Total Problems index is an overall composite of behavioral and emotional concerns. The Children's Depression Inventory (CDI) was completed by the participants as a self-report measure of depressive symptoms (20).

Executive functioning in daily life.

Because executive functioning (e.g., behavior regulation and metacognitive skills such as planning and organization) can be very difficult to validly assess using formal one-on-one neuropsychological tests, parents completed the Behavior Rating Inventory of Executive Functioning (BRIEF) (21), a standardized questionnaire. The Behavioral Regulation, Metacognition, and Global Executive Composite scales of the BRIEF were considered in this study.

Formal neurocognitive testing.

All of the participants underwent formal neurocognitive testing performed by a trained psychometrician, using a standardized neuropsychological battery for cSLE with details provided elsewhere (22). In brief, the battery consisted of the Wechsler Abbreviated Scale of Intelligence (23), which is a well-validated measure of overall intelligence (full-scale intelligence quotient); the Working Memory and Processing Speed subscales of the age-appropriate Wechsler Intelligence Scales (24, 25), which measure working memory and psychomotor speed, respectively; the Wide Range Assessment of Memory and Learning 2 (26), from which the Memory Screening index summarizes an individual's ability to learn and recall new verbal and visual information; selected subtests from the Woodcock-Johnson III Tests of Achievement (WJ-III) (27), which assess basic reading/decoding ability (WJ-III Letter-Word Identification) and written arithmetic skills (WJ-III Calculation); and the Conners' Continuous Performance Test II (CPT-II) (28), which assesses test takers' ability to sustain attention (CPT-II omissions, CPT-II mean hit reaction time standard error) and inhibit impulsive responses (CPT-II commissions) during a long, boring task. Age-normed scores are available for all instruments (22).

Statistical analysis.

Primary analyses used paired-sample t-tests to compare means between the cSLE patients and their demographically-matched best friends (controls), and Pearson's correlation coefficients to assess relationships between continuous variables. In addition, nonparametric Wilcoxon's signed rank tests and Spearman's correlation coefficients were used as supplementary analyses for paired-sample t-tests and Pearson's correlation coefficients, respectively. Multivariate analyses, including mixed-effect models and partial correlation coefficients, were used to compare means and assess relationships after adjusting for sociodemographic characteristics. Only results from primary analyses are included in this article, as there were no important discrepancies between nonparametric and parametric analyses. For categorical variables, associations with cSLE/control group membership were assessed using logistic models after adjusting for within-pair correlations using a generalized estimating equation method. For numerical variables in the cSLE group, their means were compared to those of population norms using Z tests. Strength of correlation was considered “very strong,” “strong,” “moderate,” “weak,” or “poor” if the magnitude of the correlation coefficient was 0.9–1, 0.7–0.9, 0.5–0.7, 0.3–0.5, or 0–0.3, respectively (29). Statistical computations were performed using the SAS software package, version 9.3. P values less than 0.05 were considered statistically significant.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Demographics of the study participants and disease information about the cSLE group are provided in Table 2. Patients with cSLE were somewhat older than the best friend controls, but otherwise the groups were closely matched on major demographic indices. The mean disease duration of the cSLE group was ∼2 years, with mild to moderate disease activity and disease-related damage. Prednisone therapy was used in 31 (77.5%) of the cSLE patients.

Table 2. Demographics of the study population*
 cSLE (n = 40)Controls (n = 40)P
  • *

    cSLE = childhood-onset systemic lupus erythematosus; SLEDAI-2K = Systemic Lupus Disease Activity Index 2000; BILAG = British Isles Lupus Assessment Group Index; SDI = Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index.

  • P < 0.05.

  • Measured on categorical Likert scale, where 0 = inactive cSLE and 10 = very active cSLE.

  • §

    Range 0–104, where 0 = inactive cSLE.

  • A = 9, B = 3, C = 1, and D or E = 0, where lower scores indicate lower cSLE activity.

Age at enrollment, mean ± SD years14.8 ± 2.313.9 ± 3.20.03
Female sex, %85.085.01.0
Ethnicity, %  0.98
 White30.032.5 
 African American45.047.5 
 Hispanic17.515.0 
 Asian and other7.55 
Grade level, %  1.0
 Elementary school (4–6)20.020.0 
 Middle school (7 and 8)17.517.5 
 High school (9–12)62.562.5 
Maternal education level, %  0.7
 No high school diploma7.510.0 
 Completed high school diploma30.037.5 
 Education beyond high school62.552.5 
Family income, %  0.81
 <$25,00020.015.8 
 $25,000–$50,00035.034.2 
 >$50,000 to $75,00020.028.9 
 >$75,00025.021.1 
cSLE duration, mean ± SD months23.7 ± 23.1  
Physician assessment of disease activity, mean ± SD2.4 ± 2.0  
Disease activity (SLEDAI-2K), mean ± SD§4.9 ± 4.4  
Disease activity (BILAG), mean ± SD3.0 ± 3.8  
Disease damage (SDI), mean ± SD0.4 ± 0.8  
Receiving prednisone therapy, %77.5  
Prednisone daily dose (n = 31), mean ± SD mg19.8 ± 17.4  

Academic functioning of the cSLE and control groups.

Parent-rated school competence, as measured by the CBCL, was significantly lower in the cSLE group than the control group (mean ± SD 48.4 ± 9.2 versus 51.4 ± 5.1; P = 0.02). Figure 1 shows a plot of the CBCL School Competence T score ratings for each cSLE best friend control pair. Although the mean score for both groups fell within the normal range, greater variability is apparent in the cSLE group.

thumbnail image

Figure 1. Plots of Child Behavior Checklist (CBCL) School Competence T scores between childhood-onset systemic lupus erythematosus (cSLE) and paired best friends (controls). Each blue line shows a cSLE–best friend pair. A solid red square shows the sample mean. The horizontal solid line shows the normative mean. Two dotted lines show ±1 normative SD from the normative mean.

Download figure to PowerPoint

Comparisons between the cSLE and control subjects on indices of cognitive, behavioral, emotional, and executive function.

As shown in Table 3, the cSLE and demographically-matched control groups were not significantly different in their performance during formal neurocognitive testing or on measures of behavioral, emotional, or executive functioning (CBCL, BRIEF, CDI). Conversely, compared to published norms, the cSLE group had significantly weaker scores on the Wechsler Working Memory and Processing Speed indices, and significantly better scores than published norms on the CBCL Externalizing Problems and BRIEF Behavior Regulation Indices and on the CDI total score of overall depressive symptoms.

Table 3. Group comparisons for cognitive, behavioral, emotional, and executive functioning indices*
 cSLE, mean ± SDControls, mean ± SDP
cSLE vs. controlscSLE vs. population norms
  • *

    Further details of the psychometric variables are shown in Table 1. cSLE = childhood-onset systemic lupus erythematosus; WASI = Wechsler Abbreviated Scale of Intelligence; IQ = intelligence quotient; WRAML-2 = Wide Range Assessment of Memory and Learning 2; WJ-III = Woodcock-Johnson III Tests of Achievement; CPT-II = Conners' Continuous Performance Test II; CBCL = Child Behavior Checklist; CDI = Children's Depression Inventory; BRIEF = Behavior Rating Inventory of Executive Function.

  • Paired-samples t-test.

  • Single-sample Z test.

  • §

    Wechsler Intelligence Scale for Children, 4th edition (age <17 years), and Wechsler Adult Intelligence Scale, 4th edition (age ≥17 years).

  • P < 0.05.

WASI Full-Scale IQ101.0 ± 11.698.6 ± 12.60.240.62
Wechsler Intelligence Scales Working Memory index§90.4 ± 20.094.3 ± 13.30.27< 0.01
Wechsler Intelligence Scales Processing Speed index§94.3 ± 21.2100.0 ± 13.00.090.02
WRAML-2 Memory Screening index102.8 ± 14.1100.2 ± 14.00.370.23
WJ-III Letter-Word Identification subtest96.9 ± 10.496.3 ± 10.60.740.20
WJ-III Calculation subtest96.8 ± 14.594.2 ± 15.60.310.20
CPT-II omissions52.6 ± 13.855.8 ± 17.30.370.10
CPT-II commissions48.5 ± 7.450.4 ± 10.30.350.34
CPT-II hit reaction time standard error48.6 ± 10.749.6 ± 10.00.640.36
CBCL Anxious/Depressed scale52.2 ± 4.052.5 ± 4.00.710.17
CBCL Externalizing Problems46.0 ± 8.047.1 ± 8.60.490.01
CBCL Total Problems48.3 ± 10.047.2 ± 8.30.540.28
CDI total score43.8 ± 7.744.3 ± 8.00.76< 0.01
BRIEF Behavioral Regulation Index46.4 ± 7.047.6 ± 7.60.440.02
BRIEF Metacognition Index49.0 ± 10.048.0 ± 7.40.590.54
BRIEF Global Executive Composite47.8 ± 8.547.6 ± 7.20.880.16

Correlates of academic functioning.

Table 4 summarizes the associations between CBCL School Competence ratings and the behavioral, emotional, executive, and cognitive functioning of participants in the cSLE and matched control groups. Importantly, correlations of these variables with School Competence did not differ significantly between the 2 groups. Variables that were associated with school performance in the cSLE group were, for the most part, similarly strongly associated in the control group. Not surprisingly, CBCL School Competence ratings were moderately correlated with performance on the WJ-III achievement subtests measuring reading and decoding and math calculation skills.

Table 4. Psychometric correlates of school functioning in cSLE, controls, and the entire study population*
 cSLEControlsCombined sample
  • *

    Values are the Pearson's correlation coefficient. Although some of the correlations superficially differed across the cSLE and control groups, none of these differences were statistically significant. cSLE = childhood-onset systemic lupus erythematosus; WASI = Wechsler Abbreviated Scale of Intelligence; IQ = intelligence quotient; WRAML-2 = Wide Range Assessment of Memory and Learning 2; WJ-III = Woodcock-Johnson III Tests of Achievement; CPT-II = Conners' Continuous Performance Test II; CBCL = Child Behavior Checklist; CDI = Children's Depression Inventory; BRIEF = Behavior Rating Inventory of Executive Function.

  • P < 0.05.

  • P < 0.005.

WASI Full-Scale IQ0.170.310.19
Wechsler Intelligence Scales Working Memory index0.270.230.27
Wechsler Intelligence Scales Processing Speed index0.200.310.25
WRAML-2 Memory Screening index0.040.070.04
WJ-III Letter-Word Identification subtest0.480.330.40
WJ-III Calculation subtest0.420.370.37
CPT-II omissions−0.02−0.06−0.01
CPT-II commissions−0.10−0.23−0.13
CPT-II hit reaction time standard error−0.040.050.00
CBCL Anxious/Depressed scale−0.24−0.30−0.23
CBCL Externalizing Problems−0.14−0.07−0.09
CBCL Total Problems−0.26−0.25−0.26
CDI total score−0.17−0.31−0.20
BRIEF Behavioral Regulation Index−0.16−0.28−0.17
BRIEF Metacognition Index−0.54−0.19−0.44
BRIEF Global Executive Composite−0.46−0.28−0.40

Interestingly, while we observed expected weak correlations for the overall sample between School Competence ratings and the Wechsler Working Memory and Processing Speed indices, School Competence was not significantly correlated with measures of overall intelligence, sustained attention, or impulse control. Significant correlations were found, however, between School Competence ratings and the BRIEF Metacognition Index and Global Executive Composite within the cSLE group and in the overall sample.

In addition to the measures collected for all of the participants, we assessed the relationship between CBCL School Competence and measures of cSLE activity, damage, and treatment intensity. As shown in Table 5, School Competence was significantly related to disease activity as indicated by both the SLEDAI-2K and BILAG indices, and also to higher prednisone doses.

Table 5. Disease-related correlates of school functioning in cSLE*
 Pearson's r
  • *

    cSLE = childhood-onset systemic lupus erythematosus; SLEDAI-2K = Systemic Lupus Disease Activity Index 2000; BILAG = British Isles Lupus Assessment Group Index; SDI = Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index.

  • P < 0.005.

  • P < 0.05.

SLE duration, months0.10
Physician assessment of disease activity−0.18
Disease activity (SLEDAI-2K)−0.55
Disease activity (BILAG)−0.54
Disease damage (SDI)−0.22
Prednisone daily dose, mg−0.40

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

We found that children with SLE have significantly inferior academic outcomes than their peers, despite indistinguishable performance levels on neuropsychological tests and equivalent ratings of externalizing behavior, mood, and executive functioning. These academic outcome findings are in line with our previous research (1), which revealed parent- and self-report ratings of school functioning on the Pediatric Quality of Life Inventory to be significantly lower in cSLE than a normal national comparison sample (30). Our current results extend those findings to demonstrate an effect even when comparing patients with cSLE to demographically similar peers.

Given the importance of scholastic performance for children and adolescents, it is important to understand the mechanism by which cSLE might impair school functioning. Our findings indicate that disease activity and treatment intensity are significant correlates of poor school competence in patients with cSLE. Furthermore, cognitive variables that were associated with school competence in healthy controls had similar associations in children with cSLE: reading skill, mathematics skill, short-term attention/working memory, and mental processing speed. Importantly, although participants with cSLE scored differently than published norms on several outcome measures, this appears to be due to their demographic differences from the normative sample. The cSLE group did not significantly differ from the demographically-matched controls on any cognitive, behavioral, emotional, or executive functioning measure. Therefore, while poor school performance in any individual may relate to neuropsychological disturbance, we did not find evidence that such disturbances are the systematic mechanism by which cSLE results in diminished school performance. Based on the associations observed in our cross-sectional analyses, we can entertain 2 possible explanations for the difference in the academic functioning between children and adolescents with cSLE and their peers.

The most obvious explanation to consider is related to the adverse effect that cSLE-related symptoms and signs may have upon school participation. In our study, we clearly demonstrated that cSLE disease activity and prednisone treatment dose are related to worse academic outcomes. Although school attendance was not specifically measured in our study, it would make sense that cSLE patients with more active disease requiring more intense medication regimens are more likely to be absent from school than their less severely affected counterparts. Earlier reports and our own registry data indicate an increased number of missed days of school in patients with cSLE (31). This raises the possibility of a disruptive effect of cSLE as a chronic illness with acute episodes that interfere with children's functioning at school and in other performance contexts.

Second, although we did not identify significant differences between cSLE and the best friend controls on formal neurocognitive measures, one cannot entirely exclude the possibility that cSLE disease activity or its treatment exerts a modest disruptive effect upon cognitive functioning, impacting school performance as a result. While the current sample of 40 children with SLE and 40 matched case controls is the largest sample to date, our study might have been underpowered to detect subtle neurocognitive effects of cSLE on the performance of standardized tests such as the Wechsler Processing Speed index.

The patients included in this study were mostly female, with mild/moderate disease activity approximately 2 years after diagnosis (32), and they reflected the diversity of socioeconomic status commonly seen in cSLE (13, 33). The high incidence of cSLE among US ethnic and racial minorities makes a direct comparison to normative US populations unfitting. Therefore, we used a matched-control design to carefully select an appropriate comparison group that would enable us to better separate the impact of cSLE as a disease from that of sociodemographic effects. We are convinced that we achieved this, based upon the extremely similar demographic composition of the 2 groups. There was a statistically significant difference in age across the 2 groups, but because all of our major outcome measures were age normed, the small age difference would not be expected to impact our results. This assumption is supported by our exploratory analyses that adjusted for age and found no substantive change in the findings reported.

Even though the current parent ratings and test results are within the average range, on a few measures both of the groups showed a trend to deviate from reference norms. For example, an SD of 0.64 was noted in the cSLE group on the Wechsler Working Memory index. While such differences may seem relatively small on an individual basis, their impact within a population such as cSLE can be substantial. A shift of −0.64 SD of the distribution of scores in the cSLE group corresponds to a 248% increase in cSLE Working Memory performances falling in the deficient range. Therefore, even seemingly modest differences in mean scores can result in dramatically increased numbers of children who exceed psychometric thresholds for clinical deviance. Given that cSLE disproportionately affects children of minority and lower socioeconomic status, this impact may be especially apparent.

Our failure to identify disproportionate levels of NCD in cSLE subjects relative to case controls is consistent with the recent report of Williams et al in 2011 (13), where the frequency of NCD was similar in cSLE compared to demographically-matched peers. The replication of this finding highlights the role of demographic and socioeconomic factors in cSLE and the importance of considering how these confounding factors affect patients' HRQOL. In particular, these findings stress a key weakness of other studies that rely exclusively upon comparisons against published norms. If the sample being studied is demographically dissimilar to the normative group, findings may be misleading.

Limitations of the present study are acknowledged. First, our definition of academic outcome was based on a single measure (school competence ratings) from a single informant source (parents). Ideally, a study of academic competence would benefit from the inclusion of additional indicators of academic outcome. Unfortunately, a definition of academic competence that is comprehensive, multi-informant based, objective, and consistent across educational settings is not currently available. While future investigators may consider more objective measures of academic functioning (e.g., copies of report cards), we caution that such an approach is not a panacea, as differences in grading standards across teachers, schools, and curricula would remain. Similarly, academic skills assessed in an artificial office-based setting (i.e., individual achievement testing) at best approximate actual classroom performance, and tests of basic reading and math skills would not necessarily be sensitive to the impact of a disease with onset in middle to late childhood, when the focus of learning turns toward higher-level skills. Second, although this study assessed a broad range of cognitive, behavioral, and emotional functioning, we cannot rule out the possibility of constructs or measures that might be more sensitive to cSLE. Third, although the current sample represents the largest group yet of children and adolescents with SLE to be prospectively studied, the statistical power of our analyses was nevertheless limited by its size. Future multicenter investigations may address this shortcoming. Our experience with “best friend” case–control methodology, however, taught us that this approach can be extremely challenging to implement. Another limitation of the present study that is more easily addressed is our failure to obtain systematic information about school disruption. Future studies of academic outcome in cSLE should quantify the disruptive effect of cSLE upon school participation, using records of school attendance/absence and medical history of patients' hospitalizations and day treatment visits.

The current investigation carries several implications for clinical care in cSLE. First, because our data suggest that disease-related factors may significantly impede patients' academic progress, they highlight the importance of minimizing such disruption by optimizing patient care (e.g., minimizing missed school) and maximizing treatment adherence. Second, although we did not demonstrate systematic deficits of neurocognitive functioning in cSLE relative to demographically-matched peers, this does not mean that such deficits are absent in individual patients. Monitoring for such deficits continues to be an important component of routine patient care, even if the deficits are not clearly related to the disease process. Finally, the current study underscores the importance of considering socioeconomic/demographic variables in both research and clinical care of cSLE and other chronic diseases that are disproportionately seen in minority populations.

In summary, our results indicate that children and adolescents with SLE do in fact experience poorer academic outcomes than demographically-matched healthy peers, and that cSLE disease severity and treatment intensity are associated with school competence. However, neither neurocognitive functioning nor ratings of behavioral, emotional, and executive functioning appear to be the mechanism by which cSLE impacts school competence.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Zelko had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Zelko, Beebe, Klein-Gitelman, Ying, Brunner.

Acquisition of data. Zelko, Beebe, Baker, Nelson, Ali, Cedeno, Dina, Klein-Gitelman, Brunner.

Analysis and interpretation of data. Zelko, Beebe, Ying, Brunner.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

We would like to thank Meredith Amaya, Allison Clarke, Kate Dahl, Antoinette Dezzutti, Donna Diedenhofer, April German, Lev Gottlieb, Jennifer Heil, Jennifer Keller, Andrew Phillips, Michal Rischall, Rebecca Wasserman Lieb, Lisa Welcome, and Mariah Wells for their assistance with neuropsychological testing, and Erin Thomas for her assistance in coordinating and scheduling participants. We would also like to thank Dr. M. Douglas Ris for his assistance in the initial conceptualization of the larger project. We extend special thanks to Ms Elaine Holtkamp for her administrative support of the study and assistance with the manuscript.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
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