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Mapping each pre-existing condition’s association to short-term and long-term COVID-19 complications

Authors :
A. J. Venkatakrishnan
Colin Pawlowski
David Zemmour
Travis Hughes
Akash Anand
Gabriela Berner
Nikhil Kayal
Arjun Puranik
Ian Conrad
Sairam Bade
Rakesh Barve
Purushottam Sinha
John C. O‘Horo
Andrew D. Badley
John Halamka
Venky Soundararajan
Source :
npj Digital Medicine, Vol 4, Iss 1, Pp 1-11 (2021)
Publication Year :
2021
Publisher :
Nature Portfolio, 2021.

Abstract

Abstract Understanding the relationships between pre-existing conditions and complications of COVID-19 infection is critical to identifying which patients will develop severe disease. Here, we leverage ~1.1 million clinical notes from 1803 hospitalized COVID-19 patients and deep neural network models to characterize associations between 21 pre-existing conditions and the development of 20 complications (e.g. respiratory, cardiovascular, renal, and hematologic) of COVID-19 infection throughout the course of infection (i.e. 0–30 days, 31–60 days, and 61–90 days). Pleural effusion was the most frequent complication of early COVID-19 infection (89/1803 patients, 4.9%) followed by cardiac arrhythmia (45/1803 patients, 2.5%). Notably, hypertension was the most significant risk factor associated with 10 different complications including acute respiratory distress syndrome, cardiac arrhythmia, and anemia. The onset of new complications after 30 days is rare and most commonly involves pleural effusion (31–60 days: 11 patients, 61–90 days: 9 patients). Lastly, comparing the rates of complications with a propensity-matched COVID-negative hospitalized population confirmed the importance of hypertension as a risk factor for early-onset complications. Overall, the associations between pre-COVID conditions and COVID-associated complications presented here may form the basis for the development of risk assessment scores to guide clinical care pathways.

Details

Language :
English
ISSN :
23986352
Volume :
4
Issue :
1
Database :
Directory of Open Access Journals
Journal :
npj Digital Medicine
Publication Type :
Academic Journal
Accession number :
edsdoj.5d3be4022eee45a2a9e665e11457f771
Document Type :
article
Full Text :
https://doi.org/10.1038/s41746-021-00484-7