Article ; Online: Heterogeneity in COVID-19 Patients at Multiple Levels of Granularity: From Biclusters to Clinical Interventions.
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
2021 Volume 2021, Page(s) 112–121
Abstract: Several studies have shown that COVID-19 patients with prior comorbidities have a higher risk ... interpretation of heterogeneity at three levels of granularity (cohort, subgroup, and patient ... heterogeneity in the comorbidity profiles of COVID-19 inpatients, based on electronic health records from 12 ...
| Abstract | Several studies have shown that COVID-19 patients with prior comorbidities have a higher risk for adverse outcomes, resulting in a disproportionate impact on older adults and minorities that fit that profile. However, although there is considerable heterogeneity in the comorbidity profiles of these populations, not much is known about how prior comorbidities co-occur to form COVID-19 patient subgroups, and their implications for targeted care. Here we used bipartite networks to quantitatively and visually analyze heterogeneity in the comorbidity profiles of COVID-19 inpatients, based on electronic health records from 12 hospitals and 60 clinics in the greater Minneapolis region. This approach enabled the analysis and interpretation of heterogeneity at three levels of granularity (cohort, subgroup, and patient), each of which enabled clinicians to rapidly translate the results into the design of clinical interventions. We discuss future extensions of the multigranular heterogeneity framework, and conclude by exploring how the framework could be used to analyze other biomedical phenomena including symptom clusters and molecular phenotypes, with the goal of accelerating translation to targeted clinical care. |
|---|---|
| MeSH term(s) | Aged ; COVID-19 ; Cohort Studies ; Comorbidity ; Humans ; Phenotype ; SARS-CoV-2 |
| Language | English |
| Publishing date | 2021-05-17 |
| Publishing country | United States |
| Document type | Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 2676378-3 |
| ISSN | 2153-4063 ; 2153-4063 |
| ISSN (online) | 2153-4063 |
| ISSN | 2153-4063 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
More links
Kategorien
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.