scholarly journals Risk of Adverse Outcomes in Hospitalized Patients with Autoimmune Disease and COVID-19: A Matched Cohort Study from New York City

2020 ◽  
pp. jrheum.200989 ◽  
Author(s):  
Adam S. Faye ◽  
Kate E. Lee ◽  
Monika Laszkowska ◽  
Judith Kim ◽  
John William Blackett ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Cohort Study ◽  
Intensive Care ◽  
Autoimmune Disease ◽  
Intensive Care Unit Admission ◽  
Hospitalized Patients ◽  
Secondary Outcome ◽  
Hospital Death ◽  
Composite Outcome ◽  
Increased Risk

Objective To examine the impact of autoimmune disease on the composite outcome of intensive care unit admission, intubation, or death, from COVID-19 in hospitalized patients. Methods Retrospective cohort study of 186 patients hospitalized with COVID-19 between March 1st–April 15th, 2020 at NewYork-Presbyterian Hospital/Columbia University Irving Medical Center. The cohort included 62 patients with autoimmune disease and 124 age- and sex-matched controls. The primary outcome was a composite of intensive care unit admission, intubation, and death, with secondary outcome assessing time to in-hospital death. Baseline demographics, comorbidities, medications, vital signs, and laboratory values were collected. Conditional logistic regression and Cox proportional hazards regression were used to assess the association between autoimmune disease and clinical outcomes. Results Patients with autoimmune disease were more likely to have at least one comorbidity (25.8% vs. 12.9%, p=0.03), take chronic immunosuppressive medications (66.1% vs. 4.0%, p<0.01), and have had a solid organ transplant (16.1% vs. 1.6%, p<0.01). There were no significant differences in intensive care unit admission (14.2% vs. 19.4%, p=0.44), intubation (14.2% vs. 17.7%, p=0.62) or death (17.5% vs. 14.5%, p=0.77). On multivariable analysis, patients with autoimmune disease were not at an increased risk for a composite outcome of intensive care unit admission, intubation, or death (adjOR 0.79, 95%CI 0.37-1.67). On Cox regression, autoimmune disease was not associated with in-hospital mortality (adjHR 0.73, 95%CI 0.33-1.63). Conclusion Among patients hospitalized with COVID-19, individuals with autoimmune disease did not have an increased risk of a composite outcome of intensive care unit admission, intubation, or death.

scholarly journals RISK OF ADVERSE OUTCOMES IN HOSPITALIZED PATIENTS WITH AUTOIMMUNE DISEASE AND COVID-19: A MATCHED COHORT STUDY FROM NEW YORK CITY

2021 ◽  
Vol 27 (Supplement_1) ◽  
pp. S44-S44
Author(s):  
Adam Faye ◽  
Kate Lee ◽  
Monika Laszkowska ◽  
Judith Kim ◽  
John Blackett ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
New York ◽  
Cohort Study ◽  
Intensive Care ◽  
Autoimmune Disease ◽  
Intensive Care Unit Admission ◽  
Hospitalized Patients ◽  
Secondary Outcome ◽  
Composite Outcome ◽  
Increased Risk

Abstract Objective To examine the impact of autoimmune disease on the composite outcome of intensive care unit admission, intubation, or death, from COVID-19 in hospitalized patients. Methods Retrospective cohort study of 186 patients hospitalized with COVID-19 between March 1st–April 15th, 2020 at New York-Presbyterian Hospital/Columbia University Irving Medical Center. The cohort included 62 patients with autoimmune disease and 124 age- and sex- matched controls. The primary outcome was a composite of intensive care unit admission, intubation, and death, with secondary outcome assessing time to in-hospital death. Baseline demographics, comorbidities, medications, vital signs, and laboratory values were collected. Conditional logistic regression and Cox proportional hazards regression were used to assess the association between autoimmune disease and clinical outcomes. Results Patients with autoimmune disease were more likely to have at least one comorbidity (25.8% vs. 12.9%, p=0.03), take chronic immunosuppressive medications (66.1% vs. 4.0%, p&lt;0.01), and have had a solid organ transplant (16.1% vs. 1.6%, p&lt;0.01). There were no significant differences in intensive care unit admission (14.2% vs. 19.4%, p=0.44), intubation (14.2% vs. 17.7%, p=0.62) or death (17.5% vs. 14.5%, p=0.77). On multivariable analysis, patients with autoimmune disease were not at an increased risk for a composite outcome of intensive care unit admission, intubation, or death (adjOR 0.79, 95%CI 0.37–1.67). On Cox regression, autoimmune disease was not associated with in-hospital mortality (adjHR 0.73, 95%CI 0.33–1.63). Conclusion Among patients hospitalized with COVID-19, individuals with autoimmune disease did not have an increased risk of a composite outcome of intensive care unit admission, intubation, or death. Kaplan-Meier curve examining death, stratified by the presence or absence of autoimmune disease in all 186 patients, with 16 patients censored as of 4/29/2020

scholarly journals Thromboembolic Rates Are Similar Between Intensive Care Unit and Nonintensive Care Unit Hospitalized Patients With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): A Retrospective Cohort Study

2021 ◽  
Vol 27 ◽  
pp. 107602962110533
Author(s):  
Heidi Worth ◽  
Kasey Helmlinger ◽  
Renju Raj ◽  
Eric Heidel ◽  
Ronald Lands
Keyword(s):  
Intensive Care Unit ◽  
Cohort Study ◽  
Intensive Care ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Thromboembolic Event ◽  
Hospitalized Patients ◽  
Thromboembolic Events ◽  
Icu Patients ◽  
Increased Risk

High rates of thromboembolic events have been described in intensive care unit (ICU) patients. Data regarding thromboembolic events in all hospitalized patients has been less frequently reported, raising concerns that thromboembolic events in non-ICU may be underrecognized. In addition, optimal anticoagulation type and dose is still unsettled at this time. This is a retrospective cohort study of 159 hospitalized patients with coronavirus disease 2019 (COVID-19) pneumonia during a 9-month period to determine an association between the frequency of thromboembolic rates and hospitalized patients with COVID-19. Secondary outcomes sought to investigate association of thromboembolic events with relation to place of admission, risk factors, anticoagulation, mortality, hospital length of stay, and discharge disposition. Among the cohort of 159 hospitalized patients who met criteria, 16 (10%) were diagnosed with a thromboembolic event. There were a total of 18 thromboembolic events with 12 venous and 6 arterial. Admission to the ICU was not associated with a higher frequency of thromboembolic events compared with non-ICU patients (37.5% vs 62.5%), p = .71. Patients with a thromboembolic event had a significantly higher mortality compared with those with no thromboembolic event (37.5% vs 13.3%), p = .012. Patients hospitalized with COVID-19 have increased rates of thromboembolic events, both venous and arterial, which contribute to a significant increase in mortality. However, the frequency of thromboembolism in patients admitted to the ICU was similar to events in non-ICU patients. We hope to increase awareness of the increased risk of hypercoagulability in all hospitalized patients with COVID-19 including non-ICU patients.

scholarly journals Clinical course and severity outcome indicators among COVID 19 hospitalized patients in relation to comorbidities distribution Mexican cohort

2020 ◽  
Author(s):  
Genny Carrillo ◽  
Nina Mendez Dominguez ◽  
Kassandra D Santos Zaldivar ◽  
Andrea Rochel Perez ◽  
Mario Azuela Morales ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Respiratory Diseases ◽  
Risk Estimation ◽  
Clinical Manifestations ◽  
Case Fatality ◽  
Hospitalized Patients ◽  
Hospital Death ◽  
Outcome Indicators

Introduction: COVID-19 affected worldwide, causing to date, around 500,000 deaths. In Mexico, by April 29, the general case fatality was 6.52%, with 11.1% confirmed case mortality and hospital recovery rate around 72%. Once hospitalized, the odds for recovery and hospital death rates depend mainly on the patients' comorbidities and age. In Mexico, triage guidelines use algorithms and risk estimation tools for severity assessment and decision-making. The study's objective is to analyze the underlying conditions of patients hospitalized for COVID-19 in Mexico concerning four severity outcomes. Materials and Methods: Retrospective cohort based on registries of all laboratory-confirmed patients with the COVID-19 infection that required hospitalization in Mexico. Independent variables were comorbidities and clinical manifestations. Dependent variables were four possible severity outcomes: (a) pneumonia, (b) mechanical ventilation (c) intensive care unit, and (d) death; all of them were coded as binary Results: We included 69,334 hospitalizations of laboratory-confirmed and hospitalized patients to June 30, 2020. Patients were 55.29 years, and 62.61% were male. Hospital mortality among patients aged<15 was 9.11%, 51.99% of those aged >65 died. Male gender and increasing age predicted every severity outcome. Diabetes and hypertension predicted every severity outcome significantly. Obesity did not predict mortality, but CKD, respiratory diseases, cardiopathies were significant predictors. Conclusion: Obesity increased the risk for pneumonia, mechanical ventilation, and intensive care admittance, but it was not a predictor of in-hospital death. Patients with respiratory diseases were less prone to develop pneumonia, to receive mechanical ventilation and intensive care unit assistance, but they were at higher risk of in-hospital death.

Out-of-hours intensive care unit admission and 90-day mortality: a Danish nationwide cohort study

2018 ◽  
Vol 62 (7) ◽  
pp. 974-982 ◽  
Author(s):  
D. L. Buck ◽  
C. F. Christiansen ◽  
S. Christensen ◽  
M. H. Møller ◽  
Keyword(s):  
Intensive Care Unit ◽  
Cohort Study ◽  
Intensive Care ◽  
Intensive Care Unit Admission ◽  
Out Of Hours

scholarly journals Coronavirus disease 2019 outcomes among patients with rheumatic diseases 6 months into the pandemic

2020 ◽  
pp. annrheumdis-2020-219279
Author(s):  
Naomi Serling-Boyd ◽  
Kristin M D’Silva ◽  
Tiffany YT Hsu ◽  
Rachel Wallwork ◽  
Xiaoqing Fu ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Cohort Study ◽  
Intensive Care ◽  
Intensive Care Unit Admission ◽  
Rheumatic Diseases ◽  
Cox Regression ◽  
Diagnosis Date ◽  
Recent Cohort ◽  
Over Time

ObjectiveIn earlier studies, patients with rheumatic and musculoskeletal disease (RMD) who got infected with COVID-19 had a higher risk of mechanical ventilation than comparators. We sought to determine COVID-19 outcomes among patients with RMD 6 months into the pandemic.MethodsWe conducted a cohort study at Mass General Brigham in Boston, Massachusetts, of patients with RMD matched to up to five comparators by age, sex and COVID-19 diagnosis date (between 30 January 2020 and 16 July 2020) and followed until last encounter or 18 August 2020. COVID-19 outcomes were compared using Cox regression. Risk of mechanical ventilation was compared in an early versus a recent cohort of patients with RMD.ResultsWe identified 143 patients with RMD and with COVID-19 (mean age 60 years; 76% female individuals) and 688 comparators (mean age 59 years; 76% female individuals). There were no significantly higher adjusted risks of hospitalisation (HR: 0.87, 95% CI: 0.68–1.11), intensive care unit admission (HR: 1.27, 95% CI: 0.86–1.86), or mortality (HR: 1.02, 95% CI: 0.53–1.95) in patients with RMD versus comparators. There was a trend towards a higher risk of mechanical ventilation in the RMD cohort versus comparators, although not statistically significant (adjusted HR: 1.51, 95% CI: 0.93–2.44). There was a trend towards improvement in mechanical ventilation risk in the recent versus early RMD cohort (10% vs 19%, adjusted HR: 0.44, 95% CI: 0.17–1.12).ConclusionsPatients with RMD and comparators had similar risks of poor COVID-19 outcomes after adjusting for race, smoking and comorbidities. The higher risk of mechanical ventilation in the early RMD cohort was no longer detected in a recent cohort, suggesting improved management over time.

scholarly journals Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach

BMJ Open ◽  
2017 ◽  
Vol 7 (9) ◽  
pp. e017199 ◽  
Author(s):  
Thomas Desautels ◽  
Ritankar Das ◽  
Jacob Calvert ◽  
Monica Trivedi ◽  
Charlotte Summers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Tertiary Care Hospital ◽  
Tertiary Care ◽  
Value Added ◽  
Secondary Outcome ◽  
Hospital Death ◽  
Care Hospital ◽  
Icu Discharge

ObjectivesUnplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult and potentially increasing length of stay and mortality in some settings. Identifying patients who are likely to suffer unplanned ICU readmission could reduce the frequency of this adverse event.SettingA single academic, tertiary care hospital in the UK.ParticipantsA set of 3326 ICU episodes collected between October 2014 and August 2016. All records were of patients who visited an ICU at some point during their stay. We excluded patients who were ≤16 years of age; visited ICUs other than the general and neurosciences ICU; were missing crucial electronic patient record measurements; or had indeterminate ICU discharge outcomes or very early or extremely late discharge times. After exclusion, 2018 outcome-labelled episodes remained.Primary and secondary outcome measuresArea under the receiver operating characteristic curve (AUROC) for prediction of unplanned ICU readmission or in-hospital death within 48 hours of first ICU discharge.ResultsIn 10-fold cross-validation, an ensemble predictor was trained on data from both the target hospital and the Medical Information Mart for Intensive Care (MIMIC-III) database and tested on the target hospital’s data. This predictor discriminated between patients with the unplanned ICU readmission or death outcome and those without this outcome, attaining mean AUROC of 0.7095 (SE 0.0260), superior to the purpose-built Stability and Workload Index for Transfer (SWIFT) score (AUROC=0.6082, SE 0.0249; p=0.014, pairwise t-test).ConclusionsDespite the inherent difficulties, we demonstrate that a novel machine learning algorithm based on transfer learning could achieve good discrimination, over and above that of the treating clinicians or the value added by the SWIFT score. Accurate prediction of unplanned readmission could be used to target resources more efficiently.

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