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1.
Clin Infect Dis ; 75(11): 1903-1911, 2022 Nov 30.
Article in English | MEDLINE | ID: covidwho-1806307

ABSTRACT

BACKGROUND: Multisystem inflammatory syndrome in adults (MIS-A) is a severe condition temporally associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. METHODS: In this retrospective cohort study, we applied the US Centers for Disease Control and Prevention (CDC) case definition to identify diagnosed and undiagnosed MIS-A cases among adults discharged during April 2020-January 2021 from 4 Atlanta, Georgia hospitals affiliated with a single medical center. Non-MIS-A coronavirus disease 2019 (COVID-19) hospitalizations were identified using International Classification of Diseases, Tenth Revision, Clinical Modification encounter code U07.1. We calculated the ratio of MIS-A to COVID-19 hospitalizations, compared demographic characteristics of the 2 cohorts, and described clinical characteristics of MIS-A patients. RESULTS: We identified 11 MIS-A cases, none of which were diagnosed by the treatment team, and 5755 COVID-19 hospitalizations (ratio 1:523). Compared with patients with COVID-19, patients with MIS-A were more likely to be younger than 50 years (72.7% vs 26.1%, P < .01) and to be non-Hispanic Black (81.8% vs 50.0%, P = .04). Ten patients with MIS-A (90.9%) had at least 1 underlying medical condition. Two MIS-A patients (18.2%) had a previous episode of laboratory-confirmed COVID-19, occurring 37 and 55 days prior to admission. All MIS-A patients developed left ventricular systolic dysfunction. None had documented mucocutaneous involvement. All required intensive care, all received systemic corticosteroids, 8 (72.7%) required mechanical ventilation, 2 (18.2%) required mechanical cardiovascular circulatory support, and none received intravenous immunoglobulin. Two (18.2%) died or were discharged to hospice. CONCLUSIONS: MIS-A is a severe but likely underrecognized complication of SARS-CoV-2 infection. Improved recognition of MIS-A is needed to quantify its burden and identify populations at highest risk.


Subject(s)
COVID-19 , Connective Tissue Diseases , Adult , Humans , Connective Tissue Diseases/drug therapy , Electronic Health Records , Immunoglobulins, Intravenous/therapeutic use , Retrospective Studies , SARS-CoV-2 , Systemic Inflammatory Response Syndrome/diagnosis , Systemic Inflammatory Response Syndrome/epidemiology
2.
Clin Infect Dis ; 73(11): e4141-e4151, 2021 12 06.
Article in English | MEDLINE | ID: covidwho-1561160

ABSTRACT

BACKGROUND: Coronavirus disease (COVID-19) can cause severe illness and death. Predictors of poor outcome collected on hospital admission may inform clinical and public health decisions. METHODS: We conducted a retrospective observational cohort investigation of 297 adults admitted to 8 academic and community hospitals in Georgia, United States, during March 2020. Using standardized medical record abstraction, we collected data on predictors including admission demographics, underlying medical conditions, outpatient antihypertensive medications, recorded symptoms, vital signs, radiographic findings, and laboratory values. We used random forest models to calculate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for predictors of invasive mechanical ventilation (IMV) and death. RESULTS: Compared with age <45 years, ages 65-74 years and ≥75 years were predictors of IMV (aORs, 3.12 [95% CI, 1.47-6.60] and 2.79 [95% CI, 1.23-6.33], respectively) and the strongest predictors for death (aORs, 12.92 [95% CI, 3.26-51.25] and 18.06 [95% CI, 4.43-73.63], respectively). Comorbidities associated with death (aORs, 2.4-3.8; P < .05) included end-stage renal disease, coronary artery disease, and neurologic disorders, but not pulmonary disease, immunocompromise, or hypertension. Prehospital use vs nonuse of angiotensin receptor blockers (aOR, 2.02 [95% CI, 1.03-3.96]) and dihydropyridine calcium channel blockers (aOR, 1.91 [95% CI, 1.03-3.55]) were associated with death. CONCLUSIONS: After adjustment for patient and clinical characteristics, older age was the strongest predictor of death, exceeding comorbidities, abnormal vital signs, and laboratory test abnormalities. That coronary artery disease, but not chronic lung disease, was associated with death among hospitalized patients warrants further investigation, as do associations between certain antihypertensive medications and death.


Subject(s)
COVID-19 , Aged , Hospitalization , Humans , Middle Aged , Respiration, Artificial , Retrospective Studies , Risk Factors , SARS-CoV-2 , United States
3.
Emerg Infect Dis ; 27(4): 1164-1168, 2021.
Article in English | MEDLINE | ID: covidwho-1146202

ABSTRACT

We compared the characteristics of hospitalized and nonhospitalized patients who had coronavirus disease in Atlanta, Georgia, USA. We found that risk for hospitalization increased with a patient's age and number of concurrent conditions. We also found a potential association between hospitalization and high hemoglobin A1c levels in persons with diabetes.


Subject(s)
COVID-19 , Diabetes Mellitus , Glycated Hemoglobin/analysis , Hospitalization/statistics & numerical data , Hypertension , Obesity , Patient Care Management , Age Factors , COVID-19/epidemiology , COVID-19/psychology , COVID-19/therapy , Diabetes Mellitus/blood , Diabetes Mellitus/epidemiology , Disease Progression , Female , Georgia/epidemiology , Humans , Hypertension/drug therapy , Hypertension/epidemiology , Male , Middle Aged , Multimorbidity , Obesity/diagnosis , Obesity/epidemiology , Patient Acceptance of Health Care , Patient Care Management/methods , Patient Care Management/standards , Patient Care Management/statistics & numerical data , Risk Assessment/methods , Risk Assessment/statistics & numerical data , Risk Factors , SARS-CoV-2
4.
Open Forum Infect Dis ; 8(1): ofaa596, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-960578

ABSTRACT

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.

5.
MMWR Morb Mortal Wkly Rep ; 69(18): 545-550, 2020 May 08.
Article in English | MEDLINE | ID: covidwho-142205

ABSTRACT

SARS-CoV-2, the novel coronavirus that causes coronavirus disease 2019 (COVID-19), was first detected in the United States during January 2020 (1). Since then, >980,000 cases have been reported in the United States, including >55,000 associated deaths as of April 28, 2020 (2). Detailed data on demographic characteristics, underlying medical conditions, and clinical outcomes for persons hospitalized with COVID-19 are needed to inform prevention strategies and community-specific intervention messages. For this report, CDC, the Georgia Department of Public Health, and eight Georgia hospitals (seven in metropolitan Atlanta and one in southern Georgia) summarized medical record-abstracted data for hospitalized adult patients with laboratory-confirmed* COVID-19 who were admitted during March 2020. Among 305 hospitalized patients with COVID-19, 61.6% were aged <65 years, 50.5% were female, and 83.2% with known race/ethnicity were non-Hispanic black (black). Over a quarter of patients (26.2%) did not have conditions thought to put them at higher risk for severe disease, including being aged ≥65 years. The proportion of hospitalized patients who were black was higher than expected based on overall hospital admissions. In an adjusted time-to-event analysis, black patients were not more likely than were nonblack patients to receive invasive mechanical ventilation† (IMV) or to die during hospitalization (hazard ratio [HR] = 0.63; 95% confidence interval [CI] = 0.35-1.13). Given the overrepresentation of black patients within this hospitalized cohort, it is important for public health officials to ensure that prevention activities prioritize communities and racial/ethnic groups most affected by COVID-19. Clinicians and public officials should be aware that all adults, regardless of underlying conditions or age, are at risk for serious illness from COVID-19.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/therapy , Pneumonia, Viral/epidemiology , Pneumonia, Viral/therapy , Adolescent , Adult , Black or African American/statistics & numerical data , Aged , Aged, 80 and over , COVID-19 , Cohort Studies , Comorbidity , Coronavirus Infections/ethnology , Georgia/epidemiology , Hospitalization/statistics & numerical data , Humans , Middle Aged , Pandemics , Pneumonia, Viral/ethnology , Risk Factors , Treatment Outcome , Young Adult
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