A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine

Salvatore, Maxwell; Gu, Tian; Mack, Jasmine A; Sankar, Swaraaj Prabhu; Patil, Snehal; Valley, Thomas S.; Singh, Karandeep; Nallamothu, Brahmajee K.; Kheterpal, Sachin; Lisabeth, Lynda D.; Fritsche, Lars G.; Mukherjee, Bhramar

doi:10.3390/jcm10071351

Cited by 27 publications

(40 citation statements)

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“…Leveraging EMR data from a regional medical center managing COVID-19, this study enriches the literature with a large COVID-19 positive patient cohort and a longer follow-up period to observe evolving patient outcomes. Differing outcomes associated with demographic characteristics such as age, sex, and race, which have become particularly salient [ 20 , 22 , 37 , 58 – 60 ], were corroborated in this study. Notably, after adjusting for all other risk factors, Black patients had lower healthcare utilization rates, but higher hospitalization and mortality rates.…”

Section: Discussionsupporting

confidence: 73%

“…The University of Michigan (UM) Health System, referred to as Michigan Medicine hereafter, is one of the primary regional centers managing the care of COVID-19 patients [ 36 , 37 ] and has created, maintained, and updated an electronic medical record (EMR) database for COVID-19 patients treated in its hospital system since the outbreak [ 38 ]. Access to this rich database enables us to conduct a comprehensive analysis of COVID-19 outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes

et al. 2021

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Background Understanding risk factors for short- and long-term COVID-19 outcomes have implications for current guidelines and practice. We study whether early identified risk factors for COVID-19 persist one year later and through varying disease progression trajectories. Methods This was a retrospective study of 6,731 COVID-19 patients presenting to Michigan Medicine between March 10, 2020 and March 10, 2021. We describe disease progression trajectories from diagnosis to potential hospital admission, discharge, readmission, or death. Outcomes pertained to all patients: rate of medical encounters, hospitalization-free survival, and overall survival, and hospitalized patients: discharge versus in-hospital death and readmission. Risk factors included patient age, sex, race, body mass index, and 29 comorbidity conditions. Results Younger, non-Black patients utilized healthcare resources at higher rates, while older, male, and Black patients had higher rates of hospitalization and mortality. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss anemia were risk factors for these outcomes. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss were associated with lower discharge and higher inpatient mortality rates. Conclusions This study found differences in healthcare utilization and adverse COVID-19 outcomes, as well as differing risk factors for short- and long-term outcomes throughout disease progression. These findings may inform providers in emergency departments or critical care settings of treatment priorities, empower healthcare stakeholders with effective disease management strategies, and aid health policy makers in optimizing allocations of medical resources.

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Section: Discussionsupporting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Leveraging EMR data from a regional medical center managing COVID-19, this study enriches the literature with a large COVID-19 positive patient cohort and a longer follow-up period to observe evolving patient outcomes. Differing outcomes associated with demographic characteristics such as age, sex, and race, which have become particularly salient, [20,22,37,[58][59][60] were corroborated in this study. Notably, after adjusting for all other risk factors, Black patients had lower healthcare utilization rates, but higher hospitalization and mortality rates.…”

Section: Discussionsupporting

confidence: 74%

Comprehensive Evaluation of COVID-19 Patient Short- and Long-term Outcomes: Disparities in Healthcare Utilization and Post-Hospitalization Outcomes

Salerno

Sun

Morris

et al. 2021

Preprint

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Background: Understanding risk factors for short- and long-term COVID-19 outcomes have implications for current guidelines and practice. We study whether early identified risk factors for COVID-19 persist one year later and through varying disease progression trajectories. Methods: This was a retrospective study of 6,731 COVID-19 patients presenting to Michigan Medicine between March 10, 2020 and March 10, 2021. We describe disease progression trajectories from diagnosis to potential hospital admission, discharge, readmission, or death. Outcomes pertained to all patients: rate of medical encounters, hospitalization-free survival, and overall survival, and hospitalized patients: discharge versus in-hospital death and readmission. Risk factors included patient age, sex, race, body mass index, and 29 comorbidity conditions. Results: Younger, non-Black patients utilized healthcare resources at higher rates, while older, male, and Black patients had higher rates of hospitalization and mortality. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss anemia were risk factors for these outcomes. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss were associated with lower discharge and higher inpatient mortality rates. Conclusions: This study found differences in healthcare utilization and adverse COVID-19 outcomes, as well as differing risk factors for short- and long-term outcomes throughout disease progression. These findings may inform providers in emergency departments or critical care settings of treatment priorities, empower healthcare stakeholders with effective disease management strategies, and aid health policy makers in optimizing allocations of medical resources.

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“…Despite the advantages of an AI approach like unsupervised ML to discover previously unrecognized patterns, as well as the large extent to which AI allows for pliable model development, we have found several challenges hindering the potential of AI to be fully realized in the context of our platform. While we believe that PheWAS offers an advance over other functionally similar ML approaches in its ability to work robustly across the healthcare data of diverse enterprises ( Hermann et al, 2021 ; Salvatore et al, 2021 ; Schneider et al, 2021 ) and its empowerment of holistic, high-throughput discovery through minimal model pre-conditioning, its results require manual interpretation.…”

Section: Limitations Of the Utility Of Ai Based On Our Drug Repurposing Experiencesmentioning

confidence: 99%

Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases

et al. 2021

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Repurposing is an increasingly attractive method within the field of drug development for its efficiency at identifying new therapeutic opportunities among approved drugs at greatly reduced cost and time of more traditional methods. Repurposing has generated significant interest in the realm of rare disease treatment as an innovative strategy for finding ways to manage these complex conditions. The selection of which agents should be tested in which conditions is currently informed by both human and machine discovery, yet the appropriate balance between these approaches, including the role of artificial intelligence (AI), remains a significant topic of discussion in drug discovery for rare diseases and other conditions. Our drug repurposing team at Vanderbilt University Medical Center synergizes machine learning techniques like phenome-wide association study—a powerful regression method for generating hypotheses about new indications for an approved drug—with the knowledge and creativity of scientific, legal, and clinical domain experts. While our computational approaches generate drug repurposing hits with a high probability of success in a clinical trial, human knowledge remains essential for the hypothesis creation, interpretation, “go-no go” decisions with which machines continue to struggle. Here, we reflect on our experience synergizing AI and human knowledge toward realizable patient outcomes, providing case studies from our portfolio that inform how we balance human knowledge and machine intelligence for drug repurposing in rare disease.

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A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine

Cited by 27 publications

References 33 publications

Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes

Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes

Comprehensive Evaluation of COVID-19 Patient Short- and Long-term Outcomes: Disparities in Healthcare Utilization and Post-Hospitalization Outcomes

Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases

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