New tool shows main highways of disease development

New tool developed by CPR enables researchers to explore disease patterns from 7.2 million patients spanning 25 years. The tool is freely available for the scientific community and requires no prerequisite bioinformatics or medical informatics skills.

As people get older they often jump from disease to disease and carry the burden of more chronic diseases at once. But is there a system in the way diseases follow each other? Danish researchers have for the past six years developed a comprehensive tool, the Danish Disease Trajectory Browser, that utilizes 25 years of public health data from Danish patients to explore what they call the main highways of disease development.

“A lot of research focus is on investigating one disease at a time. We try to add a time perspective and look at multiple diseases following each other to discover where are the most common trajectories – what are the disease highways that we as people encounter,” says professor Søren Brunak from the Novo Nordisk Foundation Center for Protein Research at University of Copenhagen.

To illustrate the use of the tool the research group looked at data for Down Syndrome patients and showed, as expected, that these patients in general are diagnosed with Alzheimer’s Disease at an earlier age that others. Other frequent diseases are displayed as well.

The Danish Disease Trajectory Browser is published in Nature Communications.

Disease trajectory that shows Down Syndrome patients in general are diagnosed with Alzheimer’s Disease at an earlier age that others. Credit: : University of Copenhagen The Faculty of Health and Medical Sciences

Making health data accessible for research

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In general, there is a barrier for working with health data in research. Both in terms of getting approval from authorities to handle patient data and the fact that researchers need specific technical skills to extract meaningful information from the data.

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“We wanted to make an easily accessible tool for researchers and health professionals where they don’t necessarily need to know all the details. The statistical summary data on disease to disease jumps in the tool are not person-sensitive. We compute statistics over many patients and have boiled it down to data points that visualize how often patients with one disease get a specific other disease at a later point. So we are focusing on the sequence of diseases,” says Søren Brunak.

The Danish Disease Trajectory Browser is freely available for the scientific community and uses WHO’s disease codes. Even though there are regional differences in disease patterns the tool is highly relevant in an international context to compare i.e. how fast diseases progress in different countries.

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Disease trajectories can help in personalized medicine

For Søren Brunak the tool has a great potential in personalized medicine.

“In personalized medicine a part of the job is to divide patients into subgroups that will benefit most from a specific treatment. By knowing the disease trajectories you can create subgroups of patients not just by their current disease, but based on their previous conditions and expected future conditions as well. In that way you find different subgroups of patients that may need different treatment strategies,” Søren Brunak explains.

Currently the Disease Trajectory Browser contains data from 1994 to 2018 and will continuously be updated with new data.


University of Copenhagen The Faculty of Health and Medical Sciences

Journal Reference:

Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients

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We present the Danish Disease Trajectory Browser (DTB), a tool for exploring almost 25 years of data from the Danish National Patient Register. In the dataset comprising 7.2 million patients and 122 million admissions, users can identify diagnosis pairs with statistically significant directionality and combine them to linear disease trajectories.

Users can search for one or more disease codes (ICD-10 classification) and explore disease progression patterns via an array of functionalities. For example, a set of linear trajectories can be merged into a disease trajectory network displaying the entire multimorbidity spectrum of a disease in a single connected graph.

Using data from the Danish Register for Causes of Death mortality is also included. The tool is disease-agnostic across both rare and common diseases and is showcased by exploring multimorbidity in Down syndrome (ICD-10 code Q90) and hypertension (ICD-10 code I10). Finally, we show how search results can be customized and exported from the browser in a format of choice (i.e. JSON, PNG, JPEG and CSV).

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