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

Troels Siggaard, Roc Reguant, Isabella F. Jørgensen, Amalie D. Haue, Mette Lademann, Alejandro Aguayo-Orozco, Jessica X. Hjaltelin, Anders Boeck Jensen, Karina Banasik and Søren Brunak ()
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Troels Siggaard: University of Copenhagen
Roc Reguant: University of Copenhagen
Isabella F. Jørgensen: University of Copenhagen
Amalie D. Haue: University of Copenhagen
Mette Lademann: University of Copenhagen
Alejandro Aguayo-Orozco: University of Copenhagen
Jessica X. Hjaltelin: University of Copenhagen
Anders Boeck Jensen: University of Copenhagen
Karina Banasik: University of Copenhagen
Søren Brunak: University of Copenhagen

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract 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).

Date: 2020
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DOI: 10.1038/s41467-020-18682-4

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