A data-driven medication score predicts 10-year mortality among aging adults View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-09-25

AUTHORS

Paavo Häppölä, Aki S. Havulinna, Tõnis Tasa, Nina J. Mars, Markus Perola, Mikko Kallela, Lili Milani, Seppo Koskinen, Veikko Salomaa, Benjamin M. Neale, Aarno Palotie, Mark Daly, Samuli Ripatti

ABSTRACT

Health differences among the elderly and the role of medical treatments are topical issues in aging societies. We demonstrate the use of modern statistical learning methods to develop a data-driven health measure based on 21 years of pharmacy purchase and mortality data of 12,047 aging individuals. The resulting score was validated with 33,616 individuals from two fully independent datasets and it is strongly associated with all-cause mortality (HR 1.18 per point increase in score; 95% CI 1.14–1.22; p = 2.25e−16). When combined with Charlson comorbidity index, individuals with elevated medication score and comorbidity index had over six times higher risk (HR 6.30; 95% CI 3.84–10.3; AUC = 0.802) compared to individuals with a protective score profile. Alone, the medication score performs similarly to the Charlson comorbidity index and is associated with polygenic risk for coronary heart disease and type 2 diabetes. More... »

PAGES

15760

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-020-72045-z

DOI

http://dx.doi.org/10.1038/s41598-020-72045-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1131168739

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/32978407


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