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Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients

Author

Listed:
  • Anders Boeck Jensen

    (Center for Biological Sequence Analysis, Technical University of Denmark
    NNF Center for Protein Research, University of Copenhagen)

  • Pope L. Moseley

    (NNF Center for Protein Research, University of Copenhagen
    University of New Mexico, MSC10 5550, 1 University of New Mexico)

  • Tudor I. Oprea

    (Center for Biological Sequence Analysis, Technical University of Denmark
    University of New Mexico, MSC10 5550, 1 University of New Mexico
    University of Gothenburg)

  • Sabrina Gade Ellesøe

    (NNF Center for Protein Research, University of Copenhagen)

  • Robert Eriksson

    (Center for Biological Sequence Analysis, Technical University of Denmark
    NNF Center for Protein Research, University of Copenhagen)

  • Henriette Schmock

    (Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Copenhagen University Hospital)

  • Peter Bjødstrup Jensen

    (NNF Center for Protein Research, University of Copenhagen)

  • Lars Juhl Jensen

    (NNF Center for Protein Research, University of Copenhagen)

  • Søren Brunak

    (Center for Biological Sequence Analysis, Technical University of Denmark
    NNF Center for Protein Research, University of Copenhagen)

Abstract

A key prerequisite for precision medicine is the estimation of disease progression from the current patient state. Disease correlations and temporal disease progression (trajectories) have mainly been analysed with focus on a small number of diseases or using large-scale approaches without time consideration, exceeding a few years. So far, no large-scale studies have focused on defining a comprehensive set of disease trajectories. Here we present a discovery-driven analysis of temporal disease progression patterns using data from an electronic health registry covering the whole population of Denmark. We use the entire spectrum of diseases and convert 14.9 years of registry data on 6.2 million patients into 1,171 significant trajectories. We group these into patterns centred on a small number of key diagnoses such as chronic obstructive pulmonary disease (COPD) and gout, which are central to disease progression and hence important to diagnose early to mitigate the risk of adverse outcomes. We suggest such trajectory analyses may be useful for predicting and preventing future diseases of individual patients.

Suggested Citation

  • Anders Boeck Jensen & Pope L. Moseley & Tudor I. Oprea & Sabrina Gade Ellesøe & Robert Eriksson & Henriette Schmock & Peter Bjødstrup Jensen & Lars Juhl Jensen & Søren Brunak, 2014. "Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients," Nature Communications, Nature, vol. 5(1), pages 1-10, September.
  • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms5022
    DOI: 10.1038/ncomms5022
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    Cited by:

    1. David Westergaard & Frederik Hytting Jørgensen & Jens Waaben & Alexander Wolfgang Jung & Mette Lademann & Thomas Folkmann Hansen & Jolien Cremers & Sisse Rye Ostrowski & Ole Birger Vesterager Pedersen, 2024. "Uncovering the heritable components of multimorbidities and disease trajectories using a nationwide cohort," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Andreas Höhn & Anna Oksuzyan & Rune Lindahl-Jacobsen & Kaare Christensen & Rosie Seaman, 2021. "Gender differences in time to first hospital admission at age 60 in Denmark, 1995–2014," European Journal of Ageing, Springer, vol. 18(4), pages 443-451, December.
    3. Xin Han & Qing Shen & Can Hou & Huazhen Yang & Wenwen Chen & Yu Zeng & Yuanyuan Qu & Chen Suo & Weimin Ye & Fang Fang & Unnur A. Valdimarsdóttir & Huan Song, 2024. "Disease clusters subsequent to anxiety and stress-related disorders and their genetic determinants," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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