Poster Abstracts • OFID 2020:7 (Suppl 1) • S251
MD Joanna S Cavalier, PhD Benjamin Goldstein, MD Cara L O'brien, MD, MMCi Armando Bedoya
Session: P-12. COVID-19 Complications, Co-infections, and Clinical Outcomes Background: The novel coronavirus disease (COVID-19) results in severe illness in a significant proportion of patients, necessitating a way to discern which patients will become critically ill and which will not. In one large case series, 5.0% of patients required an intensive care unit (ICU) and 1.4% died. Several models have been developed to assess decompensating patients. However, research examining their applicability to COVID-19 patients is limited. An accurate predictive model for patients at risk of decompensation is critical for health systems to optimally triage emergencies, care for patients, and allocate resources. Methods: An early warning score (EWS) algorithm created within a large academic medical center, with methodology previously described, was applied to COVID-19 patients admitted to this institution. 122 COVID-19 patients were included. A decompensation event was defined as inpatient mortality or an unanticipated transfer to an ICU from an intermediate medical ward. The EWS was calculated at 12-hour and 24-hour intervals. Results: Of 122 patients admitted with COVID-19, 28 had a decompensation event, yielding an event rate of 23.0%. 8 patients died, 13 transferred to the ICU, and 6 both transferred to the ICU and died. Decompensation within 12 and 24 hours were predicted with areas under the curve (AUC) of 0.850 and 0.817, respectively. Using a three-tiered risk model, use of the customized EWS score for patients identified as high risk of decompensation had a positive predictive value of 44.4% and 11.1% and specificity of 99.3% and 99.6% and 12-and 24-hour intervals. Amongst medium-risk patients, the score had a specificity of 85.0% and 85.4%, respectively. Conclusion: This EWS allows for prediction of decompensation, defined as transfer to an ICU or death, in COVID-19 patients with excellent specificity and a high positive predictive value. Clinically, implementation of this score can help to identify patients before they decompensate in order to triage at time of presentation and allocate step-down beds, ICU beds, and treatments such as remdesivir.
Poster Abstracts • OFID 2020:7 (Suppl 1) • S253 pts admitted to our southeast U.S. HS had significant comorbidities, most commonly obesity, HTN, and diabetes. Additionally, AA comprised a disproportionate share (72%) of our cohort compared to the general population of our state (30%), those tested in our region (32.9%), and those found to be positive for . In-hospital mortality was 19.3% and intubation, particularly if delayed, was associated with death as were several complications, most notably arrhythmia, ARDS, and renal failure with HD. Disclosures: All Authors: No reported disclosures
Characteristics and Outcomes of COVID-19 Patients with Fungal Infections Erica Herc, MD 1 ; Nicholas F. Yared, MD 2 ; Adam Kudirka, MD 1 ; Geehan Suleyman, MD 1 ; 1 Henry Ford Hospital, Detroit, MI; 2 Henry Ford Health System, Detroit, Michigan
Session: P-12. COVID-19 Complications, Co-infections, and Clinical Outcomes Background: There is concern that patients with coronavirus disease 2019 (COVID-19) are at risk of developing secondary bacterial and fungal infections; however, data on the clinical characteristics and outcomes of COVID-19 patients with fungal infections are limited. We evaluated the risk factors and mortality of hospitalized COVID-19 patients with fungal infections. Methods: This was a retrospective chart review of 51 patients with fungal infections at an 877-bed teaching hospital in Detroit, Michigan from March through May 2020. Demographic data, comorbidities,..
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'abstract': '<jats:title>Abstract</jats:title>\n'
' <jats:sec>\n'
' <jats:title>Background</jats:title>\n'
' <jats:p>COVID-19, first described in Wuhan, China, is now a global '
'pandemic. We describe a cohort of patients (pts) admitted to our academic health system (HS) '
'in the southeast, where demographics and comorbidities differ significantly from other '
'regions in the U.S.</jats:p>\n'
' </jats:sec>\n'
' <jats:sec>\n'
' <jats:title>Methods</jats:title>\n'
' <jats:p>This was a retrospective review of 161 consecutive pts admitted '
'with COVID-19 from 3/12/20 to 6/1/20. We assessed demographics, comorbidities, presenting '
'symptoms, treatments and outcomes and compared pts who died during hospitalization to those '
'who survived to discharge (EpiInfo 7.2, Atlanta, GA).</jats:p>\n'
' </jats:sec>\n'
' <jats:sec>\n'
' <jats:title>Results</jats:title>\n'
' <jats:p>Mean age was 60.5 years, 51.6% were female, 72% African American '
'(AA) and 69.6% admitted from home. 54.5% had a BMI &gt;30, 72% had HTN, 47.2% diabetes, '
'and 33.6% COPD or asthma. The majority (68.8%) presented with fever (&gt;38.0) and '
'required supplemental oxygen within 8 hours of admission (63.4%). Cough (65.6%), dyspnea '
'(57.5%), myalgias (30.6%) and diarrhea (23.8%) were also common. 40.4% received '
'hydroxychloroquine, 23.6% steroids and 19.9% convalescent plasma. 42.9% required ICU care, '
'27.3% were intubated, and 19.3% died. Characteristics associated with death included older '
'age, male sex, HTN, ESRD on HD, and cancer. Symptoms associated with death included absence '
'of cough, absence of myalgias, previous admission for COVID-19, tachypnea, need for '
'supplemental oxygen, elevated BUN and creatinine, and elevated ferritin. Interventions '
'associated with death included use of steroids, receipt of ICU care, intubation, delay to '
'intubation, and use of vasopressors or inotropes. Complications associated with death '
'included development of a new arrhythmia, bacteremia, pneumonia, ARDS, thrombosis, and new '
'renal failure requiring HD (Table).</jats:p>\n'
' <jats:p>Table 1. Patient Characteristics by Death</jats:p>\n'
' <jats:p />\n'
' <jats:p>Table 2. Patient Characteristics by Death</jats:p>\n'
' <jats:p />\n'
' <jats:p>Table 3. Patient Characteristics by Death</jats:p>\n'
' <jats:p />\n'
' </jats:sec>\n'
' <jats:sec>\n'
' <jats:title>Conclusion</jats:title>\n'
' <jats:p>COVID-19 pts admitted to our southeast U.S. HS had significant '
'comorbidities, most commonly obesity, HTN, and diabetes. Additionally, AA comprised a '
'disproportionate share (72%) of our cohort compared to the general population of our state '
'(30%), those tested in our region (32.9%), and those found to be positive for COVID-19 '
'(35.8%). In-hospital mortality was 19.3% and intubation, particularly if delayed, was '
'associated with death as were several complications, most notably arrhythmia, ARDS, and renal '
'failure with HD.</jats:p>\n'
' </jats:sec>\n'
' <jats:sec>\n'
' <jats:title>Disclosures</jats:title>\n'
' <jats:p>All Authors: No reported disclosures</jats:p>\n'
' </jats:sec>',
'DOI': '10.1093/ofid/ofaa439.560',
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'title': '365. Characteristics and outcomes of COVID-19 patients admitted to a regional health system in '
'the southeast',
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'author': [ { 'given': 'Charles',
'family': 'Teixeira',
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{ 'given': 'Henry',
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{ 'given': 'Deeksha',
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'affiliation': [ { 'name': 'Medical University of South Carolina, Charleston, South '
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{ 'given': 'Jessica',
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'affiliation': [ { 'name': 'Medical University of South Carolina, Charleston, South '
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{ 'given': 'Scott R',
'family': 'Curry',
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'affiliation': [ { 'name': 'Medical University of South Carolina, Charleston, South '
'Carolina'}]},
{ 'given': 'Cassandra',
'family': 'Salgado',
'sequence': 'additional',
'affiliation': [ { 'name': 'Medical University of South Carolina, Charleston, South '
'Carolina'}]}],
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'relation': {},
'ISSN': ['2328-8957'],
'subject': ['Infectious Diseases', 'Oncology'],
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'published': {'date-parts': [[2020, 10, 1]]}}
