Eczema (also known as atopic dermatitis or atopic eczema) affects 20% of children and up to 10% of adults (5.8 million people in England) and prevalence is increasing globally. Eczema is a challenging disease characterised by itch, sleeplessness, discomfort, stress and stigma for sufferers and incurs significant costs. Eczema is a heterogeneous disorder with different presentations and disease courses, suggestive of the existence of different eczema subtypes. Current divisions into allergic (atopic) and non-allergic eczema are too simplistic. This study will investigate if it is possible to discover these disease subtypes in eczema using an approach called cluster analysis. This method groups patients (in this instance patients with eczema) into groups based on individual patient clinical characteristics (such as other diseases and socioeconomic status or demographic information). Patients in the same group (called a cluster) are more similar between them than with patients in other groups. Cluster analysis has been used before for eczema, but on a much smaller scale, with smaller patient datasets. Furthermore, the accuracy of different clustering techniques (e.g. partitional and hierarchical) has not been investigated comprehensively and our research will aim to systematically compare and assess them.
Eczema (also known as atopic dermatitis or atopic eczema) affects 20% of children and up to 10% of adults (5.8 million people in England) and prevalence is increasing globally. Eczema is a challenging disease characterised by itch, sleeplessness, discomfort, stress and stigma for sufferers and incurs significant costs. Eczema is a very heterogeneous diseases, with groups of clinical, pathophysiological and demographic characteristics called phenotypes.). Currently, there is not much known about the relationship between pathological features, clinical patterns of disease and response to treatment.
In this study, we will use an approach named clustering analysis to try to identify eczema phenotypes in CPRD. Clustering is an exploratory approach in which can be used to identify subtypes in diseases. Cluster analysis to identify eczema phenotypes has been used in smaller studies. However, cluster analysis of a larger patient database could lead to the improved identification and characterisation of eczema phenotypes which would otherwise be grouped into broader disease aggregates.
Sinead Langan - Chief Investigator - London School of Hygiene & Tropical Medicine ( LSHTM )
Sinead Langan - Corresponding Applicant - London School of Hygiene & Tropical Medicine ( LSHTM )
Amy Mulick - Collaborator - London School of Hygiene & Tropical Medicine ( LSHTM )
Liam Smeeth - Collaborator - London School of Hygiene & Tropical Medicine ( LSHTM )
Maria Pikoula - Collaborator - University College London ( UCL )
Spiros Denaxas - Collaborator - University College London ( UCL )
HES Admitted Patient Care;HES Outpatient;ONS Death Registration Data;Patient Level Index of Multiple Deprivation