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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...

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Autores principales: Hamid, Jemila S, Meaney, Christopher, Crowcroft, Natasha S, Granerod, Julia, Beyene, Joseph
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3022837/
https://www.ncbi.nlm.nih.gov/pubmed/21192831
http://dx.doi.org/10.1186/1471-2334-10-364
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author Hamid, Jemila S
Meaney, Christopher
Crowcroft, Natasha S
Granerod, Julia
Beyene, Joseph
author_facet Hamid, Jemila S
Meaney, Christopher
Crowcroft, Natasha S
Granerod, Julia
Beyene, Joseph
author_sort Hamid, Jemila S
collection PubMed
description 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.
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spelling pubmed-30228372011-01-20 Cluster analysis for identifying sub-groups and selecting potential discriminatory variables in human encephalitis Hamid, Jemila S Meaney, Christopher Crowcroft, Natasha S Granerod, Julia Beyene, Joseph BMC Infect Dis Research Article 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. BioMed Central 2010-12-31 /pmc/articles/PMC3022837/ /pubmed/21192831 http://dx.doi.org/10.1186/1471-2334-10-364 Text en Copyright ©2010 Hamid et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Hamid, Jemila S
Meaney, Christopher
Crowcroft, Natasha S
Granerod, Julia
Beyene, Joseph
Cluster analysis for identifying sub-groups and selecting potential discriminatory variables in human encephalitis
title Cluster analysis for identifying sub-groups and selecting potential discriminatory variables in human encephalitis
title_full Cluster analysis for identifying sub-groups and selecting potential discriminatory variables in human encephalitis
title_fullStr Cluster analysis for identifying sub-groups and selecting potential discriminatory variables in human encephalitis
title_full_unstemmed Cluster analysis for identifying sub-groups and selecting potential discriminatory variables in human encephalitis
title_short Cluster analysis for identifying sub-groups and selecting potential discriminatory variables in human encephalitis
title_sort cluster analysis for identifying sub-groups and selecting potential discriminatory variables in human encephalitis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3022837/
https://www.ncbi.nlm.nih.gov/pubmed/21192831
http://dx.doi.org/10.1186/1471-2334-10-364
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