The epidemiology of acute encephalitis.

Encephalitis means inflammation of the brain matter. Despite being a rare condition, encephalitis is of public health importance worldwide because it has high morbidity and mortality. Yet, many details about its epidemiology have yet to be elucidated. This review attempts to summarise what is known about the epidemiology of the infective causes of encephalitis and is based on a literature search of the Medline archives. Infection is the most common cause identified, with viruses being the most important known aetiological agents. Incidence varies between studies but is generally between 3.5 and 7.4 per 100,000 patient-years. Encephalitis affects peoples of all ages; however, incidence is higher in the paediatric population. Although both sexes are affected, most studies have shown a slight predominance in males. Encephalitis occurs worldwide; some aetiologies have a global distribution (herpesviruses) while others are geographically restricted (arboviruses). Although definite epidemiological trends are evident, it is difficult to make generalisations as few population-based studies exist, most cases are not reported to health authorities, and many possible pathogens are implicated but in most cases a cause is never found. A better understanding of the epidemiology of this devastating disease will pave the way for better prevention and control strategies

Potential risk factors associated with human encephalitis: application of canonical correlation analysis

<p>Abstract</p> <p>Background</p> <p>Infection of the CNS is considered to be the major cause of encephalitis and more than 100 different pathogens have been recognized as causative agents. Despite being identified worldwide as an important public health concern, studies on encephalitis are very few and often focus on particular types (with respect to causative agents) of encephalitis (e.g. West Nile, Japanese, etc.). Moreover, a number of other infectious and non-infectious conditions present with similar symptoms, and distinguishing encephalitis from other disguising conditions continues to a challenging task.</p> <p>Methods</p> <p>We used canonical correlation analysis (CCA) to assess associations between set of exposure variable and set of symptom and diagnostic variables in human encephalitis. Data consists of 208 confirmed cases of encephalitis from a prospective multicenter study conducted in the United Kingdom. We used a covariance matrix based on Gini's measure of similarity and used permutation based approaches to test significance of canonical variates.</p> <p>Results</p> <p>Results show that weak pair-wise correlation exists between the risk factor (exposure and demographic) and symptom/laboratory variables. However, the first canonical variate from CCA revealed strong multivariate correlation (ρ = 0.71, se = 0.03, p = 0.013) between the two sets. We found a moderate correlation (ρ = 0.54, se = 0.02) between the variables in the second canonical variate, however, the value is not statistically significant (p = 0.68). Our results also show that a very small amount of the variation in the symptom sets is explained by the exposure variables. This indicates that host factors, rather than environmental factors might be important towards understanding the etiology of encephalitis and facilitate early diagnosis and treatment of encephalitis patients.</p> <p>Conclusions</p> <p>There is no standard laboratory diagnostic strategy for investigation of encephalitis and even experienced physicians are often uncertain about the cause, appropriate therapy and prognosis of encephalitis. Exploration of human encephalitis data using advanced multivariate statistical modelling approaches that can capture the inherent complexity in the data is, therefore, crucial in understanding the causes of human encephalitis. Moreover, application of multivariate exploratory techniques will generate clinically important hypotheses and offer useful insight into the number and nature of variables worthy of further consideration in a confirmatory statistical analysis.</p

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

ObjectiveIn patients with encephalitis, the development of acute symptomatic seizures is highly variable, but when present is associated with a worse outcome. We aimed to determine the factors associated with seizures in encephalitis and develop a clinical prediction model.MethodsWe analysed 203 patients from 24 English hospitals (2005–2008) (Cohort 1). Outcome measures were seizures prior to and during admission, inpatient seizures and status epilepticus. A binary logistic regression risk model was converted to a clinical score and independently validated on an additional 233 patients from 31 UK hospitals (2013–2016) (Cohort 2).ResultsIn Cohort 1, 121 (60%) patients had a seizure including 103 (51%) with inpatient seizures. Admission Glasgow Coma Scale (GCS) ≤8/15 was predictive of subsequent inpatient seizures (OR (95% CI) 5.55 (2.10 to 14.64), p&lt;0.001), including in those without a history of prior seizures at presentation (OR 6.57 (95% CI 1.37 to 31.5), p=0.025).A clinical model of overall seizure risk identified admission GCS along with aetiology (autoantibody-associated OR 11.99 (95% CI 2.09 to 68.86) and Herpes simplex virus 3.58 (95% CI 1.06 to 12.12)) (area under receiver operating characteristics curve (AUROC) =0.75 (95% CI 0.701 to 0.848), p&lt;0.001). The same model was externally validated in Cohort 2 (AUROC=0.744 (95% CI 0.677 to 0.811), p&lt;0.001). A clinical scoring system for stratifying inpatient seizure risk by decile demonstrated good discrimination using variables available on admission; age, GCS and fever (AUROC=0.716 (95% CI 0.634 to 0.798), p&lt;0.001) and once probable aetiology established (AUROC=0.761 (95% CI 0.6840.839), p&lt;0.001).ConclusionAge, GCS, fever and aetiology can effectively stratify acute seizure risk in patients with encephalitis. These findings can support the development of targeted interventions and aid clinical trial design for antiseizure medication prophylaxis.</jats:sec

Cluster analysis for identifying sub-groups and selecting potential discriminatory variables in human encephalitis

Abstract Background Encephalitis is an acute clinical syndrome of the central nervous system (CNS), often associated with fatal outcome or permanent damage, including cognitive and behavioural impairment, affective disorders and epileptic seizures. Infection of the central nervous system is considered to be a major cause of encephalitis and more than 100 different pathogens have been recognized as causative agents. However, a large proportion of cases have unknown disease etiology. Methods We perform hierarchical cluster analysis on a multicenter England encephalitis data set with the aim of identifying sub-groups in human encephalitis. We use the simple matching similarity measure which is appropriate for binary data sets and performed variable selection using cluster heatmaps. We also use heatmaps to visually assess underlying patterns in the data, identify the main clinical and laboratory features and identify potential risk factors associated with encephalitis. Results Our results identified fever, personality and behavioural change, headache and lethargy as the main characteristics of encephalitis. Diagnostic variables such as brain scan and measurements from cerebrospinal fluids are also identified as main indicators of encephalitis. Our analysis revealed six major clusters in the England encephalitis data set. However, marked within-cluster heterogeneity is observed in some of the big clusters indicating possible sub-groups. Overall, the results show that patients are clustered according to symptom and diagnostic variables rather than causal agents. Exposure variables such as recent infection, sick person contact and animal contact have been identified as potential risk factors. Conclusions It is in general assumed and is a common practice to group encephalitis cases according to disease etiology. However, our results indicate that patients are clustered with respect to mainly symptom and diagnostic variables rather than causal agents. These similarities and/or differences with respect to symptom and diagnostic measurements might be attributed to host factors. The idea that characteristics of the host may be more important than the pathogen is also consistent with the observation that for some causes, such as herpes simplex virus (HSV), encephalitis is a rare outcome of a common infection.</p

Potential risk factors associated with human encephalitis: application of canonical correlation analysis

Abstract Background Infection of the CNS is considered to be the major cause of encephalitis and more than 100 different pathogens have been recognized as causative agents. Despite being identified worldwide as an important public health concern, studies on encephalitis are very few and often focus on particular types (with respect to causative agents) of encephalitis (e.g. West Nile, Japanese, etc.). Moreover, a number of other infectious and non-infectious conditions present with similar symptoms, and distinguishing encephalitis from other disguising conditions continues to a challenging task. Methods We used canonical correlation analysis (CCA) to assess associations between set of exposure variable and set of symptom and diagnostic variables in human encephalitis. Data consists of 208 confirmed cases of encephalitis from a prospective multicenter study conducted in the United Kingdom. We used a covariance matrix based on Gini's measure of similarity and used permutation based approaches to test significance of canonical variates. Results Results show that weak pair-wise correlation exists between the risk factor (exposure and demographic) and symptom/laboratory variables. However, the first canonical variate from CCA revealed strong multivariate correlation (ρ = 0.71, se = 0.03, p = 0.013) between the two sets. We found a moderate correlation (ρ = 0.54, se = 0.02) between the variables in the second canonical variate, however, the value is not statistically significant (p = 0.68). Our results also show that a very small amount of the variation in the symptom sets is explained by the exposure variables. This indicates that host factors, rather than environmental factors might be important towards understanding the etiology of encephalitis and facilitate early diagnosis and treatment of encephalitis patients. Conclusions There is no standard laboratory diagnostic strategy for investigation of encephalitis and even experienced physicians are often uncertain about the cause, appropriate therapy and prognosis of encephalitis. Exploration of human encephalitis data using advanced multivariate statistical modelling approaches that can capture the inherent complexity in the data is, therefore, crucial in understanding the causes of human encephalitis. Moreover, application of multivariate exploratory techniques will generate clinically important hypotheses and offer useful insight into the number and nature of variables worthy of further consideration in a confirmatory statistical analysis

Cluster analysis for identifying sub-groups and selecting potential discriminatory variables in human encephalitis

Abstract Background Encephalitis is an acute clinical syndrome of the central nervous system (CNS), often associated with fatal outcome or permanent damage, including cognitive and behavioural impairment, affective disorders and epileptic seizures. Infection of the central nervous system is considered to be a major cause of encephalitis and more than 100 different pathogens have been recognized as causative agents. However, a large proportion of cases have unknown disease etiology. Methods We perform hierarchical cluster analysis on a multicenter England encephalitis data set with the aim of identifying sub-groups in human encephalitis. We use the simple matching similarity measure which is appropriate for binary data sets and performed variable selection using cluster heatmaps. We also use heatmaps to visually assess underlying patterns in the data, identify the main clinical and laboratory features and identify potential risk factors associated with encephalitis. Results Our results identified fever, personality and behavioural change, headache and lethargy as the main characteristics of encephalitis. Diagnostic variables such as brain scan and measurements from cerebrospinal fluids are also identified as main indicators of encephalitis. Our analysis revealed six major clusters in the England encephalitis data set. However, marked within-cluster heterogeneity is observed in some of the big clusters indicating possible sub-groups. Overall, the results show that patients are clustered according to symptom and diagnostic variables rather than causal agents. Exposure variables such as recent infection, sick person contact and animal contact have been identified as potential risk factors. Conclusions It is in general assumed and is a common practice to group encephalitis cases according to disease etiology. However, our results indicate that patients are clustered with respect to mainly symptom and diagnostic variables rather than causal agents. These similarities and/or differences with respect to symptom and diagnostic measurements might be attributed to host factors. The idea that characteristics of the host may be more important than the pathogen is also consistent with the observation that for some causes, such as herpes simplex virus (HSV), encephalitis is a rare outcome of a common infection

Cluster analysis for identifying sub-groups and selecting potential discriminatory variables in human encephalitis

BACKGROUND: Encephalitis is an acute clinical syndrome of the central nervous system (CNS), often associated with fatal outcome or permanent damage, including cognitive and behavioural impairment, affective disorders and epileptic seizures. Infection of the central nervous system is considered to be a major cause of encephalitis and more than 100 different pathogens have been recognized as causative agents. However, a large proportion of cases have unknown disease etiology. METHODS: We perform hierarchical cluster analysis on a multicenter England encephalitis data set with the aim of identifying sub-groups in human encephalitis. We use the simple matching similarity measure which is appropriate for binary data sets and performed variable selection using cluster heatmaps. We also use heatmaps to visually assess underlying patterns in the data, identify the main clinical and laboratory features and identify potential risk factors associated with encephalitis. RESULTS: Our results identified fever, personality and behavioural change, headache and lethargy as the main characteristics of encephalitis. Diagnostic variables such as brain scan and measurements from cerebrospinal fluids are also identified as main indicators of encephalitis. Our analysis revealed six major clusters in the England encephalitis data set. However, marked within-cluster heterogeneity is observed in some of the big clusters indicating possible sub-groups. Overall, the results show that patients are clustered according to symptom and diagnostic variables rather than causal agents. Exposure variables such as recent infection, sick person contact and animal contact have been identified as potential risk factors. CONCLUSIONS: It is in general assumed and is a common practice to group encephalitis cases according to disease etiology. However, our results indicate that patients are clustered with respect to mainly symptom and diagnostic variables rather than causal agents. These similarities and/or differences with respect to symptom and diagnostic measurements might be attributed to host factors. The idea that characteristics of the host may be more important than the pathogen is also consistent with the observation that for some causes, such as herpes simplex virus (HSV), encephalitis is a rare outcome of a common infection

New Estimates of Incidence of Encephalitis in England

Encephalitis causes high rates of illness and death, yet its epidemiology remains poorly understood. To improve incidence estimates in England and inform priority setting and treatment and prevention strategies, we used hospitalization data to estimate incidence of infectious and noninfectious encephalitis during 2005–2009. Hospitalization data were linked to a dataset of extensively investigated cases of encephalitis from a prospective study, and capture–recapture models were applied. Incidence was estimated from unlinked hospitalization data as 4.32 cases/100,000 population/year. Capture–recapture models gave a best estimate of encephalitis incidence of 5.23 cases/100,000/year, although the models ’ indicated incidence could be as high as 8.66 cases/100,000/year. This analysis indicates that the incidence of encephalitis in England is considerably higher than previously estimated. Therefore, encephalitis should be a greater priority for clinicians, researchers, and public health officials. Encephalitis is associated with severe illness, appreciable mortality rates, and high health care costs (1), but its epidemiology remains poorly understood (2). The sole previous incidence estimate for encephalitis in England of 1.5 cases per 100,000 population per year was for viral encephalitis only and was based on hospitalization data from 1989–1998 (3). Incidence should be understood; as an increasing number of viruses have been found to cause encephalitis in humans, more cases might be found among the high proportion of cases of unknown etiology (2,4–6). Climate change and increasing international travel raise the possibility of wider geographic spread of microbes, which may have important public health implications. Clarifying incidence is also important clinically with th

Characteristic Cytokine and Chemokine Profiles in Encephalitis of Infectious, Immune-Mediated, and Unknown Aetiology

Submitted by Sandra Infurna ([email protected]) on 2016-12-06T11:32:11Z No. of bitstreams: 1 david_brown_etal_IOC_2016.pdf: 2706291 bytes, checksum: c3230faf693f175a2c6f1cdcfaf5dd88 (MD5)Approved for entry into archive by Sandra Infurna ([email protected]) on 2016-12-06T11:49:23Z (GMT) No. of bitstreams: 1 david_brown_etal_IOC_2016.pdf: 2706291 bytes, checksum: c3230faf693f175a2c6f1cdcfaf5dd88 (MD5)Made available in DSpace on 2016-12-06T11:49:24Z (GMT). No. of bitstreams: 1 david_brown_etal_IOC_2016.pdf: 2706291 bytes, checksum: c3230faf693f175a2c6f1cdcfaf5dd88 (MD5) Previous issue date: 2016The Walton Centre NHS Foundation Trust. Liverpool, United Kingdom / University of Liverpool. The Institute of Infection and Global Health. Liverpool, United Kingdom / University of Liverpool. NIHR Health Protection Research Unit in Emerging and Zoonotic Infections. Liverpool, United Kingdom.University of Liverpool. The Institute of Infection and Global Health. Liverpool, United Kingdom / University of Liverpool. NIHR Health Protection Research Unit in Emerging and Zoonotic Infections. Liverpool, United Kingdom / Alder Hey Children’s NHS Foundation Trust,. Liverpool, United Kingdom.Public Health England. London, United Kingdom.Public Health England. London, United Kingdom / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Vírus Respiratório e Sarampo. Rio de Janeiro, RJ, Brasil.Chelsea and Westminster NHS Foundation Trust. London, United Kingdom.Public Health England. Vaccine Evaluation Unit. Manchester, United Kingdom.The Walton Centre NHS Foundation Trust. Liverpool, United Kingdom / University of Liverpool. The Institute of Infection and Global Health. Liverpool, United Kingdom / University of Liverpool. NIHR Health Protection Research Unit in Emerging and Zoonotic Infections. Liverpool, United Kingdom.Background Encephalitis is parenchymal brain inflammation due to infectious or immune-mediated processes. However, in 15–60% the cause remains unknown. This study aimed to determine if the cytokine/chemokine-mediated host response can distinguish infectious from immunemediated cases, and whether this may give a clue to aetiology in those of unknown cause. Methods We measured 38 mediators in serum and cerebrospinal fluid (CSF) of patients from the Health Protection Agency Encephalitis Study. Of serum from 78 patients, 38 had infectious, 20 immune-mediated, and 20 unknown aetiology. Of CSF from 37 patients, 20 had infectious, nine immune-mediated and eight unknown aetiology. Results Heat-map analysis of CSF mediator interactions was different for infectious and immunemediated cases, and that of the unknown aetiology group was similar to the infectious pattern. Higher myeloperoxidase (MPO) concentrations were found in infectious than immunemediated cases, in serum and CSF (p = 0.01 and p = 0.006). Serum MPO was also higher in unknown than immune-mediated cases (p = 0.03). Multivariate analysis selected serum MPO; classifying 31 (91%) as infectious (p = 0.008) and 17 (85%) as unknown (p = 0.009) as opposed to immune-mediated. CSF data also selected MPO classifying 11 (85%) as infectious as opposed to immune-mediated (p = 0.036). CSF neutrophils were detected in eight (62%) infective and one (14%) immune-mediated cases (p = 0.004); CSF MPO correlated with neutrophils (p<0.0001).Conclusions Mediator profiles of infectious aetiology differed from immune-mediated encephalitis; and those of unknown cause were similar to infectious cases, raising the hypothesis of a possible undiagnosed infectious cause. Particularly, neutrophils and MPO merit further investigation