Nationwide prediction of type 2 diabetes comorbidities
Thomas Alexander Gerds,
Tune H Pers
Posted 14 Jun 2019
bioRxiv DOI: 10.1101/664722 (published DOI: 10.1038/s41598-020-58601-7)
Posted 14 Jun 2019
Identification of individuals at risk of developing disease comorbidities represents an important task in tackling the growing personal and societal burdens associated with chronic diseases. We employed machine learning techniques to investigate to what extent data from longitudinal, nationwide Danish health registers can be used to predict individuals at high risk of developing type 2 diabetes (T2D) comorbidities. Based on register data spanning hospitalizations, drug prescriptions and contacts with primary health contractors from >200,000 individuals newly diagnosed with T2D, we used logistic regression-, random forest- and gradient boosting models to predict five-year risk of heart failure (HF), myocardial infarction (MI), stroke (ST), cardiovascular disease (CVD) and chronic kidney disease (CKD). For HF, MI, CVD, and CKD, register-based models outperformed a reference model leveraging canonical individual characteristics by achieving an area under the receiver operating characteristic curve improvements of 0.06, 0.03, 0.06, and 0.07, respectively. The top 1,000 patients predicted to be at highest risk exhibited observed incidence ratios exceeding 4.99, 3.52, 2.92 and 4.71, respectively. In summary, prediction of T2D comorbidities utilizing Danish registers led to consistent albeit modest performance improvements over reference models, suggesting that register data could be leveraged to systematically identify individuals at risk of developing disease comorbidities.
- Downloaded 338 times
- Download rankings, all-time:
- Site-wide: 86,819
- In bioinformatics: 7,789
- Year to date:
- Site-wide: 117,933
- Since beginning of last month:
- Site-wide: 138,672
Downloads over time
Distribution of downloads per paper, site-wide
- 27 Nov 2020: The website and API now include results pulled from medRxiv as well as bioRxiv.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!