Jensen Anders Boeck, Moseley Pope L, Oprea Tudor I, Ellesøe Sabrina Gade, Eriksson Robert, Schmock Henriette, Jensen Peter Bjødstrup, Jensen Lars Juhl, Brunak Søren
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.
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.
Nat Commun. 2014 Jun 24;5:4022. doi: 10.1038/ncomms5022.
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.
精准医学的一个关键前提是根据患者当前状态估计疾病进展。疾病相关性和疾病的时间进展（轨迹）主要是在关注少数疾病的情况下进行分析的，或者是使用不考虑时间因素的大规模方法进行分析的，这种时间跨度超过了数年。到目前为止，还没有大规模研究专注于定义一套全面的疾病轨迹。在此，我们利用丹麦全体人口的电子健康登记数据，对疾病时间进展模式进行了探索性分析。我们涵盖了所有疾病种类，并将620万患者14.9年的登记数据转化为1171条有意义的轨迹。我们将这些轨迹归纳为以少数关键诊断为中心的模式，如慢性阻塞性肺疾病（COPD）和痛风，这些对于疾病进展至关重要，因此早期诊断对于降低不良后果风险很重要。我们认为这种轨迹分析可能有助于预测和预防个体患者未来的疾病。
