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Patient-centric characterization of multimorbidity trajectories in patients with severe mental illnesses: A temporal bipartite network modeling approach.
Wang, Tao; Bendayan, Rebecca; Msosa, Yamiko; Pritchard, Megan; Roberts, Angus; Stewart, Robert; Dobson, Richard.
Afiliação
  • Wang T; Department of Biostatistics and Health Informatics, King's College London, Denmark Hill, London SE5 8AF, United Kingdom. Electronic address: tao.wang@kcl.ac.uk.
  • Bendayan R; Department of Biostatistics and Health Informatics, King's College London, Denmark Hill, London SE5 8AF, United Kingdom; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ
  • Msosa Y; Department of Biostatistics and Health Informatics, King's College London, Denmark Hill, London SE5 8AF, United Kingdom.
  • Pritchard M; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom.
  • Roberts A; Department of Biostatistics and Health Informatics, King's College London, Denmark Hill, London SE5 8AF, United Kingdom; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ
  • Stewart R; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom; Department of Psychological Medicine, King's College London, Denmark Hill, London SE5 8AF, United Kingd
  • Dobson R; Department of Biostatistics and Health Informatics, King's College London, Denmark Hill, London SE5 8AF, United Kingdom; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ
J Biomed Inform ; 127: 104010, 2022 03.
Article em En | MEDLINE | ID: mdl-35151869
Multimorbidity is a major factor contributing to increased mortality among people with severe mental illnesses (SMI). Previous studies either focus on estimating prevalence of a disease in a population without considering relationships between diseases or ignore heterogeneity of individual patients in examining disease progression by looking merely at aggregates across a whole cohort. Here, we present a temporal bipartite network model to jointly represent detailed information on both individual patients and diseases, which allows us to systematically characterize disease trajectories from both patient and disease centric perspectives. We apply this approach to a large set of longitudinal diagnostic records for patients with SMI collected through a data linkage between electronic health records from a large UK mental health hospital and English national hospital administrative database. We find that the resulting diagnosis networks show disassortative mixing by degree, suggesting that patients affected by a small number of diseases tend to suffer from prevalent diseases. Factors that determine the network structures include an individual's age, gender and ethnicity. Our analysis on network evolution further shows that patients and diseases become more interconnected over the illness duration of SMI, which is largely driven by the process that patients with similar attributes tend to suffer from the same conditions. Our analytic approach provides a guide for future patient-centric research on multimorbidity trajectories and contributes to achieving precision medicine.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Multimorbidade / Transtornos Mentais Tipo de estudo: Diagnostic_studies / Prevalence_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Multimorbidade / Transtornos Mentais Tipo de estudo: Diagnostic_studies / Prevalence_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article