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Nat Commun. 2014 Jun 24;5:4022. doi: 10.1038/ncomms5022.

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

Author information

1
1] Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet, Building 208, DK-2800 Kgs. Lyngby, Denmark [2] NNF Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, DK-2200 Copenhagen, Denmark.
2
1] NNF Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, DK-2200 Copenhagen, Denmark [2] Department of Internal Medicine, University of New Mexico, MSC10 5550, 1 University of New Mexico, Albuquerque, New Mexico 87131, USA.
3
1] Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet, Building 208, DK-2800 Kgs. Lyngby, Denmark [2] Department of Internal Medicine, University of New Mexico, MSC10 5550, 1 University of New Mexico, Albuquerque, New Mexico 87131, USA [3] Department of Rheumatology and Inflammation Research, University of Gothenburg, Box 480, SE-40530 Gothenburg, Sweden.
4
NNF Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, DK-2200 Copenhagen, Denmark.
5
Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Copenhagen University Hospital, Boserupvej 2, DK-4000 Roskilde, Denmark.

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.

PMID:
24959948
PMCID:
PMC4090719
DOI:
10.1038/ncomms5022
[Indexed for MEDLINE]
Free PMC Article

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