Acute seizure risk in patients with encephalitis: development and validation of clinical prediction models from two independent prospective multicentre cohorts

Introduction

Encephalitis is inflammation of the brain parenchyma, caused by infectious or immune-mediated processes, and is associated with significant morbidity and mortality despite antiviral and/or immune therapies.1 Globally, 500 000 children and adults are affected each year.2 The clinical presentation is variable, but typically includes acute or subacute onset of altered mental state alongside fever, headache, new-onset focal neurological signs and, in some, seizures.3 Seizures have particular significance as they are associated with a worse outcome and may well be amenable to prophylaxis.4 Although seizures may be a proxy marker of severe encephalitis, there are a number of mechanisms by which they could lead to further brain inflammation and damage, including hypoxia, excitotoxicity and raised intracranial pressure.5 However, the incidence of acute symptomatic seizures is highly variable (between 2% and 67%).5 Although there is some limited evidence that possible risk factors include the aetiology of encephalitis, younger age and the degree of cortical involvement, our capacity to predict who is at risk of seizures remains very poor.4 5 Consequently, there is insufficient evidence to recommend the use of antiseizure medications (ASM) as standard of care as either primary or secondary prophylaxis.4 6 Initiation and escalation of ASM is possible in most healthcare settings, and if proven to improve outcome, could be started rapidly as ASM therapy is agnostic to eventual aetiology.

Therefore, if a high-risk group were established, this could be used to stratify patients for future clinical trials of primary and secondary prophylaxis with ASM or, as a minimum, to identify which patients should be managed in settings with adequate capacity to manage this severe complication.4 This study aims to establish the factors associated with seizures in encephalitis as well as develop and validate a seizure prediction model of clinical utility for patients presenting with an acute encephalitis syndrome, in accordance with the WHO approach.7 8

Methods

Statistical analysis

Model development

Clinical prediction modelling was designed to be applicable to routine clinical practice to stratify seizure risk in patients presenting with the clinical features of acute encephalitis syndrome. Binary logistic regression was used to ascertain predictor variables of seizures in Cohort 1 (SPSS V.26). Due to the limited proportion of patients with a clinically indicated EEG and the risk of data availability bias, EEG results were not considered for inclusion. Collinearity was assessed using correlation matrices and one of any two highly correlated variables omitted. The pattern of missing data was reviewed to assess whether data were missing completely at random. Data not missing ‘completely at random’ by Little’s test but deemed to be missing ‘at random’ were imputed using multiple imputation in clinical variables with >5% missing data. Candidate variables were selected by univariate selection (p<0.25) and those identified in the literature. A selection of strongest contributing predictors was made through backward selection based on likelihood ratio. The final binary logistic regression model based on pooled estimates was converted to a provisional clinical scoring system by dividing regression coefficients of each factor by the smallest regression coefficient among the variables to the nearest integer.

Results

Seizures during the acute illness

In Cohort 1, 121 (60%) patients had a seizure during their acute illness and 103 (51%) had a seizure while an inpatient. Of patients with a known presenting report, 43/167 (26%) presented with a history of seizures, which were reported most frequently in patients with autoantibody-associated, 7/14 (50%), and Herpes simplex virus (HSV), 13/29 (45%), aetiologies. A semiotic description of the seizures was available for 73 (36%) patients, of whom 43 (59%) had generalised seizures only, 14 (19%) had focal seizures only and 16 (22%) had both. Four patients had a history of epilepsy, of whom three had a seizure.

Overall, patients with seizures had a lower median (IQR) age at 25 (9–50) years than those without 39 (11–60), (p=0.051) and presented to hospital earlier, at 5 (1–12) versus 9 (4–24) days from symptom onset (p=0.012) (figure 1).

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Figure 1

Demographic, clinical and investigatory factors associated with seizures in encephalitis. CSF, cerebrospinal fluid; EEG, electroencephalogram; GCS, Glasgow Coma Scale; HSV, Herpes simplex virus.

Patients with seizures were less likely to have CSF pleocytosis (OR 0.45 (95% CI 0.22 to 0.90), p=0.021) or low CSF glucose (OR 0.52 (95% CI 0.28 to 0.98), p=0.042).

Seizures at any point during the acute illness were associated with a worse outcome, with 42/80 (53%) of those with seizures having a poor outcome as opposed to 44/116 (38%) without seizures (OR 1.81 (95% CI 1.01 to 3.23), p=0.044) (figure 2).

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Figure 2

Glasgow Outcome Scale at 6 months stratified by presence and nature of seizures.

Provisional model

Presenting with a seizure and GCS were co-linear, however, GCS was most strongly associated with seizures and likely also captures whether a patient has a history of seizures due to the postictal phase, so was retained in the model. The provisional model of seizures at any time included GCS on admission and probable aetiology of encephalitis (χ2=42.53, p<0.001) (online supplemental table 2). Consistent with the univariate analysis, autoantibody-associated (OR 11.99 (95% CI 2.09 to 68.86), p=0.017) and HSV encephalitis (3.58 (95% CI 1.06 to 12.12), p=0.096) were associated with seizures. Internal cross-validation demonstrated 68% sensitivity, 72% specificity, with a positive predictive value (PPV) of 62% and negative predictive value (NPV) of 77%, with overall accuracy 70%.

The model demonstrated good discrimination in Cohort 1 and when externally validated in Cohort 2, area under ROC (AUROC)=0.775 (95% CI 0.701 to 0.848) and 0.744 (95% CI 0.677 to 0.811) respectively (figure 3) and Hosmer-Lemeshow test equalled p=0.737 on the original data. Further evaluation of provisional model calibration is provided in online supplemental table 3 and online supplemental figure 1. The provisional model systematically overestimated risk in Cohort 2, but seizures were less commonly reported in Cohort 2 compared with Cohort 1, 84/233 (36%) and 121/203 (60%) patients, respectively.

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Figure 3

Model performance. Receiver operating characteristics (ROC) curve for seizure risk according to provisional seizure model in Cohort 1 (A), Area under ROC (AUROC)=0.775 (95% CI 0.701 to 0.848), and Cohort 2 (B), AUROC 0.744 (95% CI 0.6770.811). (C) ROC curve for inpatient seizure risk according to SEIZUre Risk in Encephalitis (SEIZURE) score in Cohort 1.

Inpatient seizure risk: SEIZURE score

A second binary logistic regression model was developed to identify predictors of inpatient seizures based on the information available on admission and then on these parameters combined with aetiology once established (Cohort 1) (table 2). The derived, points-based SEIZUre Risk in Encephalitis (SEIZURE) score stratified risk by decile and is designed to be applied by healthcare professionals when a patient of any age with suspected encephalitis is admitted to hospital with two weighted scoring systems for application prior to and following identification of the probable aetiology (figure 4). Internal cross-validation demonstrated 66% sensitivity, 72% specificity, PPV 69% and NPV 69%, with overall accuracy 69%. Patients in the highest risk category on admission had an OR 7.17 (95% CI 2.55 to 20.16) of seizures compared with those in the lowest risk categories and an OR of 15.51 (95% CI 5.60 to 42.96) once probable aetiology was established (table 3).

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Figure 4

SEIZUre Risk in Encephalitis (SEIZURE) score for stratifying inpatient seizure risk by decile. ADEM, acute disseminated encephalomyelitis.

Table 2

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Development of scoring system for inpatient seizure risk in encephalitis using binary logistic regression model based on pooled estimates from imputed data in Cohort 1

Table 3

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Performance of SEIZURE score by cut-off in complete data, Cohort 1

The admission SEIZURE score, including age, admission GCS and fever, showed good discrimination (AUROC=0.716 (95% CI 0.634 to 0.798)). Addition of probable aetiology, once known, slightly increased discrimination (AUROC 0.761 (95% CI 0.684 to 0.839), p<0.001) and Hosmer-Lemeshow for the complete model was p=0.285 on original data.

Discussion

Acute seizures affect many patients with encephalitis and are associated with increased need for intensive care support and a worse outcome, and moreover may further contribute to brain injury through excitotoxicity, host immune responses and raised intracranial pressure.5 12–15 However, there are currently no established tools to stratify patients as to their risk of seizures and status epilepticus. Without such risk stratification, it is currently not possible to identify which patients would be best managed in centres with adequate clinical facilities, such as those with intensive therapy units and continuous EEG monitoring, and also to identify whom might benefit from primary and secondary ASM prophylaxis.

Through our evaluation of two independent prospective multicentre cohort studies, we identified multiple factors associated with increased risk of seizures during the acute illness, particularly low GCS on admission, fever and an autoantibody-associated or HSV aetiology. Patients with seizures presented to hospital earlier, even in those who have not yet had their first seizure at the time of presentation. Models for seizure risk could be established which, despite requiring a small number of variables, were strongly predictive of acute seizures. Low GCS on admission was more strongly associated with inpatient seizures than whether the patient presented with a seizure history or not. The provisional score accurately determined seizure risk in the first cohort, but potentially overestimated seizure risk in the second cohort, perhaps reflecting under-documentation of seizures in this cohort as seizures were limited to those documented at presentation. The ‘SEIZURE score’ for inpatient seizures requires further external validation. Improved access to easy EEG-monitoring on a wider scale, or the establishment of novel biomarkers could enhance the accuracy of risk-stratification. In addition, the impact of ASM prescription as primary and secondary prophylaxis requires further evaluation.4 5

There are likely to be multiple structural and biochemical mechanisms underlying seizure risk in patients with acute encephalitis syndrome. For example, HSV encephalitis has a predisposition to affect epileptogenic areas within the frontotemporal region and in autoimmune encephalitis, the antibodies associated with neuronal cell-surface antigens, that are highly expressed in this region, are themselves often directly involved in the disease process.16–18 The differential disease mechanisms observed in specific aetiologies of encephalitis may influence seizure risk, however, there was inadequate power in this analysis to establish factors associated with seizures within aetiological subgroups. Certain clinical features were less common in patients with seizures, specifically, stiff neck, photophobia, lethargy and any focal deficit on neurological examination. This may in part be explained by aetiological distinctions, as focal neurological deficits were most frequently reported in encephalitis caused by Varicella zoster virus and ADEM, which were least strongly associated with seizures; and particularly the latter which is associated with subcortical white matter lesions as opposed to cortical inflammation which drives seizures.19 Many clinical features associated with seizures in our analysis are likely to be proxies for underlying mechanisms, rather than risk factors in themselves. Nevertheless, these features can inform future mechanistic studies, particularly through in vivo models of encephalitis.20

EEG abnormalities were strongly associated with clinical seizure activity and EEG also identified three cases of NCSE, which is increasingly recognised in encephalitis, particularly autoimmune aetiologies.21 22 Status epilepticus in the context of encephalitis is frequently refractory and has a poor outcome.23 24 Patients with seizures were less likely to have CSF pleocytosis or low CSF glucose. Given that lumbar puncture is contraindicated until patients are stabilised following a seizure, we hypothesise that this result could be related in part to delayed lumbar puncture, or also may reflect the increased proportion with autoimmune encephalitis in this group.19 These CSF parameters likely reflect aetiological distinctions rather than direct biomarkers of seizures. Nevertheless, these data sets did not provide sufficient granularity to determine the CSF white cell count relative to the time from/before a seizure and this requires further study. No relationship was observed between the presence of normal or abnormal CT or MRI findings and seizure risk. It may be that the imaging variables in this analysis were too crude as they were based on retrospective interpretation of clinically indicated scans performed at multiple sites. Volumetric analysis for research purposes of specific brain regions or structures would be more sensitive.6 25–27 The finding may additionally reflect the high incidence of seizures in those with autoimmune encephalitis, who often have normal or near-normal neuroimaging. A single-centre study of 94 patients in China found cortical or hippocampal abnormalities on neuroimaging independently predicted progression to super-refractory status epilepticus.28 Notably, the potential associations of seizures identified in our study; aetiology, GCS and younger age, were also reported in a single-centre study in Northern India, despite large differences in the cohort.26 The likelihood of a seizure being witnessed may confound associations, for example, younger children in community settings may be more likely to have a seizure witnessed.

Our study corroborates previous work demonstrating an association between seizure activity and poor outcome in encephalitis.14 29–31 Although seizures may be a proxy marker of severe disease, there are a number of mechanisms through which seizures could cause further brain damage. Seizures cause significant systemic metabolic and biochemical disturbance including hypoxia, hypoglycaemia, metabolic acidosis as well as direct central nervous system perturbations including glutaminergic activity, raised intracranial pressure and blood–brain barrier permeabilisation, as well as low CSF glucose and high CSF lactate.32 A study of 144 patients with Japanese encephalitis presenting to hospital in Vietnam, showed that patients with recent seizures had high CSF lactate:glucose ratios and high CSF opening pressures and that patients with opening pressure >25 cm were more likely to die.14 A more recent analysis of CSF biomarkers in HSV encephalitis indicated that acute inflammation may drive subsequent synaptic autoimmunity and proposed a trial of post-acute corticosteroids.33 In addition, several inflammatory markers have been associated with a lower GCS, increased oedema and a worse outcome in encephalitis, especially the interleukin-1 family relative to their endogenous antagonists.13 It remains unknown whether seizures intervene with the underlying encephalitic process.

Despite the high prevalence and prognostic importance of seizures, the most recent Cochrane review concluded that there is insufficient evidence to support or refute the routine use of antiepileptic drugs for the primary or secondary prevention of seizures in viral encephalitis.4 A recent randomised controlled trial of secondary prophylaxis for acute symptomatic seizures in children with encephalitis demonstrated that a 4-week course of ASM was comparable to 12 weeks in terms of the incidence of seizure recurrence.34 A rabbit model of HSV-1 encephalitis, untreated with aciclovir, showed that all animals that had a seizure became moribund or died, but that phenobarbital prevented seizures and significantly reduced mortality.35 Further questions remain regarding choice and duration of antiepileptic agents.6 Any intervention strategy would need to consider the high baseline risk of seizures in patients with encephalitis and the presence of subtle and subclinical seizures including NCSE.14 21 36

Our findings reflect two relatively large prospectively recruited cohorts but have limitations, principally due to the retrospective nature of seizure-focused analysis. The observational nature of the study has an intrinsic risk of confounding which we have attempted to address in both the analysis and interpretation of results but there may be a residual impact. Additionally, the data were collected from 2005 to 2016 and seizures were not the primary focus of the initial data collection, potentially resulting in missing data. The diagnosis and management of encephalitis may have changed over this time period, particularly the identification of autoantibodies. These factors are balanced by the substantial sample size for a relatively uncommon condition, the granularity of the UKHSA data and the enhanced diagnostic testing performed.

Conclusion

These finding indicate that patients with seizures during encephalitis present earlier, but despite this, have a worse outcome, suggesting there may be a window of opportunity for intervention that is currently not being exploited. This study provides a foundation for risk stratification of seizures in encephalitis on clinical grounds alone. Biomarkers and improved access to EEG-monitoring could enhance model accuracy and allow for the development of targeted interventions. The SEIZURE score can be used to aid the design of clinical trials of primary and secondary prophylaxis with ASM.