Intelligence and memory outcomes within 10 years of childhood convulsive status epilepticus

2.1. Participants and procedures

In this prospective cohort study, we targeted recruitment of all surviving children originally identified during the NLSTEPSS [21] (detailed recruitment methods described elsewhere) [20]. Children were classified into All CSE, and subsequently stratified according to etiology into PFS and non-PFS. Prolonged febrile seizure was defined as a seizure lasting longer than 30 min and occurring between the ages of 6 months and 5 years in the presence of fever and not caused by an acute or a remote insult to the central nervous system.

For the recruitment of healthy controls, we sent all-user emails to employees of Great Ormond Street Hospital (GOSH), a specialist tertiary care hospital based in London, and Young Epilepsy (YE), a national epilepsy charity. Parents volunteered participation of their children. Exclusion criteria for healthy control participation were a diagnosis of seizures/epilepsy and/or the presence of neurodevelopmental delay, as reported by their parents. We obtained written informed consent from all participants' parents/guardians, and where appropriate, we obtained assent from participants themselves. To examine the influence of socioeconomic status (SES) on outcomes, indices of multiple deprivation (IMDs) based on residential postal codes were determined for all participants (http://www.ons.gov.uk) as a measure of their SES.

2.2. Assessments

All participants were invited for a magnetic resonance imaging (MRI) and neuropsychological assessments at UCL Great Ormond Street Institute of Child Health (ICH) in London. The Wechsler Abbreviated Scales of Intelligence (WASI) [23] was used to obtain a full-scale intelligence quotient (FSIQ), a verbal intelligence quotient (VIQ), and a performance intelligence quotient (PIQ). The Children's Memory Scale (CMS) [24] was used to obtain a global memory scores (GMS). Both tests have a normative mean of 100 and a standard deviation of 15.

Brain MRIs were carried out on an Avanto 1.5 Tesla scanner (Siemens, Erlangen, Germany) using conventional brain MRI sequences. This was done for the following: (a) to assess the presence of structural brain abnormalities in this cohort and (b) to extract hippocampal and intracranial volume (ICV) measurements to investigate their relationship with memory and cognitive indices. Images were reviewed by an experienced pediatric neuroradiologist who determined whether scans were (1) normal, (2) contained a minor abnormality (abnormal feature thought to be either unrelated to this CSE episode/no functional significance), or (3) contained a major abnormality (abnormal feature likely to have significant impact on the child/represent a cause for this CSE episode) [25]. Two of the authors (SP and MM), blind to all clinical details, carried out the manual tracing of each hippocampus on 3D fast low angle shot (FLASH) images. Their measurements were averaged to arrive at a mean hippocampal value (HV). Intracranial volume was calculated using the brain extraction tool in FSL (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki).

2.3. Statistical analysis

Analyses were conducted with Predictive Analytics Software (PASW) version 21 (Chicago, IL, U.S.A.) for Windows. Independent sample t-tests and chi-squared tests were used for intergroup comparisons. Univariate analysis of variance (ANOVA) controlling for age and SES was used to compare PFS, non-PFS, and control groups on FSIQ score and GMS; analysis was restricted to FSIQ rather than its constituents VIQ and PIQ as we did not observe any discrepancies between VIQ and PIQ in our sample. One sample t-tests were conducted to compare CSE group means with the population norms. Significant difference in test results was set at p < 0.05.

To identify factors associated with lower intelligence and memory scores according to etiological classification, we performed separate regression analyses for all patients with CSE, PFS, and non-PFS. To assess whether any CSE features were directly related to outcome, we investigated the following in the regression analyses: (a) duration of CSE (min), (b) CSE type (intermittent versus continuous), and (c) CSE loci (focal versus generalized). In addition, given past literature in this field, we also investigated whether (d) age at CSE, (e) recurrence of CSE, (f) active epilepsy at follow-up, (g) cognitive delay at baseline (yes/no), (h) motor delay at baseline (yes/no), (i) previous seizures at baseline (febrile, afebrile/epilepsy, no seizures), (j) MRI visible structural abnormalities at follow-up (no/minor abnormalities/major abnormalities), and (k) prematurity (yes/no) affected outcomes. Antiepileptic drugs were not entered as an independent variable as they are highly correlated with active epilepsy at follow-up. Finally, given the well-established role of the hippocampus in cognition, we also included (l) mean HV (mm³) in our regression analyses as well as (m) ICV (mm³), which served as a proxy for total brain volume. Bootstrapping of 1000 samples was applied to all our analyses. Any factors significant on univariable analyses at the p < 0.1 level were subsequently included in multivariable modeling (stepwise regression) to identify the factors associated with neurocognitive outcomes after adjustment of others.

Finally, given the nature of the study population, we anticipated that some participants would be unable to participate in our neuropsychological assessment because of their severe developmental delay. Such data would not have been missing at random. The overhauling majority of participants who were not assessable on the WASI and the CMS had non-PFS (bar one child with PFS). To address this matter, we imputed the minimum value (FSIQ = 50) for each participant with non-PFS that was not assessable on the WASI; a similar strategy has been used in other studies of children with seizures [26]. Imputed FSIQ scores were only applied in the regression analysis to compare with findings from the completed-case approach.

3.1. Participant characteristics

We identified 183 (90%) of the 203 survivors from the inception CSE cohort for participation in the current study; 20 subjects were lost to follow-up. One hundred and thirty-two children (65% of inception cohort survivors) participated in the study (i.e., agreed to take part in neuropsychological assessments and provided clinical data), forty-nine refused study participation altogether, and two provided follow-up data but refused neuropsychological and MRI scanning (51 refusals in total) (see Fig. 1 for flow chart of study recruitment). From the 132 participants, a bigger proportion was preterm and had generalized-onset CSE compared with the 71 nonparticipants (51 refusals and 20 lost to follow-up). The two groups were similar in all other CSE-related, clinical and demographic variables (Supplemental Table 1).

From the 132 participants, 94 children (34 PFS, 60 non-PFS) were assessed with the WASI, a mean of 8.6 years following CSE to derive a FSIQ. From the remaining participants, 38 (1 PFS, 37 non-PFS) were not assessed with our neuropsychological instruments, due to their severe developmental delay (2 out of these 38 children had an MRI). Children with PFS who undertook neuropsychological testing (N = 34) were similar to the nonparticipating PFS survivors (N = 22) for all variables except for a larger representation of females in study participants (χ² (1) = 5.04, p = 0.03). In the non-PFS group of children who completed the FSIQ assessment (N = 60), there was a significantly smaller proportion of children with cognitive delay (χ² (1) = 15.31, p < 0.001) and motor delay at baseline (χ² (1) = 7.57, p = 0.007) than children who did not participate (N = 87). Moreover, children with non-PFS classified as having any type of seizures (i.e., febrile or afebrile) prior to CSE baseline were less likely to participate in our neuropsychological assessments (χ² (1) = 12.4, p = 0.002).

Seventy-seven children (34 PFS, 43 non-PFS) out of 94 children who were assessed on the WASI were also tested on the CMS. Seventeen non-PFS cases could not be assessed on the CMS because of their severe learning difficulties (mean FSIQ difference between participants able to complete CMS (mean: 94.6) and those unable (mean: 60.2): t (92) = − 9.4, p < 0.001).

Seventeen neurologically healthy control subjects without a history of febrile or afebrile seizures (see Table 1 for group characteristics) provided CMS and WASI data for comparison.

3.2. Neuropsychological results

3.3. Regression results

The results of the univariable regression analyses can be found in Table 3 and Supplemental Table 2. In the All CSE group analysis, lower FSIQ scores were independently associated with smaller ICVs at follow-up (B = 2.78, p = 0.008), cognitive delay at baseline CSE (B = − 13.31, p = 0.02), the occurrence of previous seizures at CSE (B = − 5.16, p = 0.05), and the presence of active epilepsy at follow-up (B = − 11.72, p = 0.06) (R² = 0.51, p < 0.001). Lower GMS were associated with the occurrence of previous seizures at the time of CSE (B = − 9.21, p = 0.007) and cognitive delay at baseline CSE (B = − 23.66, p = 0.001) (R² = 0.36, p < 0.001).

For the PFS group, no variable was retained in the multivariable model. For the non-PFS group, a smaller ICV at follow-up (B = 4.02, p = 0.002), active epilepsy at follow-up (B = − 16.96, p = 0.005), and cognitive delay at CSE baseline (B = − 3.22, p = 0.011) were associated with lower FSIQ scores (R² = 0.57, p < 0.001). The same result was obtained in our all-inclusive non-PFS regression analysis (R² = 0.61, p < 0.001). Lower GMS were associated with cognitive delay at baseline (B = − 29.14, p < 0.001) (R² = 0.33, p < 0.001).

For the control group, at the multivariable level, ICV was predictive of FSIQ (R² = 0.46, p = 0.003).

4.1. Study limitations

One possible limitation of the current study is attrition, with 34% of the inception cohort not providing follow-up data. Nevertheless, loss to follow-up is inevitable in long-term cohort studies, and the 34% dropout observed in the present study is considered acceptable for such long-term follow-up studies [39], [40], [41]. A comprehensive comparison of participants and nonparticipants on demographics and clinical characteristics revealed differences between the two groups solely in the proportion of children born prematurely and children who had a generalized-onset CSE versus a focal onset. The two groups were similar in all other variables suggesting that the studied group is a good representation of the entire cohort.

Secondly, 28.5% of our sample was unable to provide numerical data because of their inability to complete assessments. Nevertheless, rather than being a limitation, this attrition reflects the outlook of children following CSE associated with neurological complications and speaks to their pervasive difficulties. In addition, this level of attrition did not take place within the PFS group who, in their overhauling majority, undertook neuropsychological assessments (bar one child with intellectual disability and autism). Moreover, when we included the whole non-PFS group by substituting nonscores with the lowest possible FSIQ score, regression results remained unchanged suggesting that the predictors identified in our study are generalizable to the entire studied population.

Thirdly, it could be argued that we recruited a modestly sized control group that were the offspring of hospital and university employees and, consequently, may have been an intrinsically higher functioning group. Nevertheless, the CSE group as a whole was not statistically different in SES, gender, and age to our group of controls. In addition, we controlled for age and SES in all our subgroup analyses. On top of that, our overall findings remained consistent when comparisons were made with the instruments' population normative data arguing against this notion. Using the more recent edition of the WASI (i.e., WASI-II) may have been preferable as the older version (WASI-I), which was published in 1999, may have overestimated FSIQs in the present study. Nevertheless, study recruitment was well underway (July 2009–February 2013) when the WASI-II was released in 2011 [41].

Finally, with recent advances in the identification of certain genetic mutations (e.g., SCN1A), the current classification system may be obscuring further divisions within each group driven by their genetic heterogeneity. Thus, our findings should be considered keeping these limitations in mind.