학술논문
COVID-19 Clinical Phenotypes: Presentation and Temporal Progression of Disease in a Cohort of Hospitalized Adults in Georgia, United States
MAJOR ARTICLE
MAJOR ARTICLE
Document Type
Report
Author
Silva, Juliana F. da; Hernandez-Romieu, Alfonso C.; Browning, Sean D.; Bruce, Beau B.; Natarajan, Pavithra; Morris, Sapna B.; Gold, Jeremy A.W.; Fanfair, Robyn Neblett; Rogers-Brown, Jessica; Rossow, John; Szablewski, Christine M.; Oosmanally, Nadine; DAngelo, Melissa Tobin; Drenzek, Cherie; Murphy, David J.; Hollberg, Julie; Blum, James M.; Jansen, Robert; Wright, David W.; Sewell, William; Owens, Jack; Lefkove, Benjamin; Brown, Frank W.; Burton, Deron C.; Uyeki, Timothy M.; Patel, Priti R.; Jackson, Brendan R.; Wong, Karen K.
Source
Open Forum Infectious Diseases. January 2021, Vol. 8 Issue 1
Subject
Language
English
ISSN
2328-8957
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused more than 10 million infections and 239 590 deaths in the United States [1]. As the number of cases increases, the [...]
Background. The epidemiological features and outcomes of hospitalized adults with coronavirus disease 2019 (COVID-19) have been described; however, the temporal progression and medical complications of disease among hospitalized patients require further study. Detailed descriptions of the natural history of COVID-19 among hospitalized patients are paramount to optimize health care resource utilization, and the detection of different clinical phenotypes may allow tailored clinical management strategies. Methods. This was a retrospective cohort study of 305 adult patients hospitalized with COVID-19 in 8 academic and community hospitals. Patient characteristics included demographics, comorbidities, medication use, medical complications, intensive care utilization, and longitudinal vital sign and laboratory test values. We examined laboratory and vital sign trends by mortality status and length of stay. To identify clinical phenotypes, we calculated Gower's dissimilarity matrix between each patient's clinical characteristics and clustered similar patients using the partitioning around medoids algorithm. Results. One phenotype of 6 identified was characterized by high mortality (49%), older age, male sex, elevated inflammatory markers, high prevalence of cardiovascular disease, and shock. Patients with this severe phenotype had significantly elevated peak C-reactive protein creatinine, D-dimer, and white blood cell count and lower minimum lymphocyte count compared with other phenotypes (P < .01, all comparisons). Conclusions. Among a cohort of hospitalized adults, we identified a severe phenotype of COVID-19 based on the characteristics of its clinical course and poor prognosis. These findings need to be validated in other cohorts, as improved understanding of clinical phenotypes and risk factors for their development could help inform prognosis and tailored clinical management for COVID-19. Keywords. clinical phenotype; COVID-19; medical complications; mortality; multisystem inflammatory syndrome.
Background. The epidemiological features and outcomes of hospitalized adults with coronavirus disease 2019 (COVID-19) have been described; however, the temporal progression and medical complications of disease among hospitalized patients require further study. Detailed descriptions of the natural history of COVID-19 among hospitalized patients are paramount to optimize health care resource utilization, and the detection of different clinical phenotypes may allow tailored clinical management strategies. Methods. This was a retrospective cohort study of 305 adult patients hospitalized with COVID-19 in 8 academic and community hospitals. Patient characteristics included demographics, comorbidities, medication use, medical complications, intensive care utilization, and longitudinal vital sign and laboratory test values. We examined laboratory and vital sign trends by mortality status and length of stay. To identify clinical phenotypes, we calculated Gower's dissimilarity matrix between each patient's clinical characteristics and clustered similar patients using the partitioning around medoids algorithm. Results. One phenotype of 6 identified was characterized by high mortality (49%), older age, male sex, elevated inflammatory markers, high prevalence of cardiovascular disease, and shock. Patients with this severe phenotype had significantly elevated peak C-reactive protein creatinine, D-dimer, and white blood cell count and lower minimum lymphocyte count compared with other phenotypes (P < .01, all comparisons). Conclusions. Among a cohort of hospitalized adults, we identified a severe phenotype of COVID-19 based on the characteristics of its clinical course and poor prognosis. These findings need to be validated in other cohorts, as improved understanding of clinical phenotypes and risk factors for their development could help inform prognosis and tailored clinical management for COVID-19. Keywords. clinical phenotype; COVID-19; medical complications; mortality; multisystem inflammatory syndrome.