TY - JOUR
T1 - Heterogeneity in COVID-19 Patients at Multiple Levels of Granularity
T2 - From Biclusters to Clinical Interventions
AU - Bhavnani, Suresh K.
AU - Kummerfeld, Erich
AU - Zhang, Weibin
AU - Kuo, Yong Fang
AU - Garg, Nisha
AU - Visweswaran, Shyam
AU - Raji, Mukaila
AU - Radhakrishnan, Ravi
AU - Golvoko, Georgiy
AU - Hatch, Sandra
AU - Usher, Michael
AU - Melton-Meaux, Genevieve
AU - Tignanelli, Christopher
N1 - Publisher Copyright:
©2021 AMIA - All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85115280452&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115280452&partnerID=8YFLogxK
M3 - Article
C2 - 34457125
AN - SCOPUS:85115280452
SN - 1559-4076
VL - 2021
SP - 112
EP - 121
JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
ER -