Cargando…

Predictors of clinical decompensation in patients presenting with COVID-19 in an urban hospital health system

INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a pandemic which has infected more than 128 million people and led to over 2.8 million deaths worldwide. Although the introduction of efficacious vaccines has led to overall declines in the incidence of SARS-C...

Descripción completa

Detalles Bibliográficos
Autores principales: Krepostman, N, Collins, M, Merchant, K, De Sirkar, S, Chan, L, Allen, S, Newman, J, Patel, D, Fareed, J, Berg, S, Darki, A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767592/
http://dx.doi.org/10.1093/eurheartj/ehab724.2473
_version_ 1784634767595012096
author Krepostman, N
Collins, M
Merchant, K
De Sirkar, S
Chan, L
Allen, S
Newman, J
Patel, D
Fareed, J
Berg, S
Darki, A
author_facet Krepostman, N
Collins, M
Merchant, K
De Sirkar, S
Chan, L
Allen, S
Newman, J
Patel, D
Fareed, J
Berg, S
Darki, A
author_sort Krepostman, N
collection PubMed
description INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a pandemic which has infected more than 128 million people and led to over 2.8 million deaths worldwide. Although the introduction of efficacious vaccines has led to overall declines in the incidence of SARS-CoV-2 infection, there has been a recent increase in infections once more due to the appearance of mutant strains with higher virulence. It therefore remains vital to identify predictors of poor outcomes in this patient population. PURPOSE: The objective of our study was to identify predictors of prolonged hospitalization, intensive care unit (ICU) admission, intubation, and death in patients infected with SARS-CoV-2. METHODS: We conducted a retrospective analysis of all patients hospitalized with SARS-CoV-2 at our health system that includes one tertiary care center and two community hospitals located in the Chicago metropolitan area. The main outcome was a composite endpoint of hospitalization >28 days, ICU admission, intubation, and death. Explanatory variables associated with the primary outcome in the bivariate analysis (p<0.05) were included in the multivariable logistic regression model. Statistical analysis was performed using IBM SPSS 25.0. RESULTS: Between March 1, 2020 and May 31, 2020, 1029 patients hospitalized with SARS-CoV-2 were included in our analysis. Of these patients, 379 met the composite endpoint. Baseline demographics are described in Table 1. Of note, our cohort consisted of a predominantly minority patient population including 47% Hispanic, 17% African American, 16% Caucasian, and 16% other. In bivariate analysis, age, hypertension, tobacco and alcohol abuse, obesity, coronary artery disease, arrhythmias, valvular heart disease, dyslipidemia, hypertension, stroke, diabetes, documented thrombosis, troponin, CRP, ESR, ferritin, LDH, BNP, D-dimer >5x the upper limit of normal, lactate, and right ventricular outflow tract velocity time integral <9.5 were significant. After multivariable adjustment, explanatory variables associated with the composite endpoint included troponin (OR 2.36; 95% CI 1.08–5.17, p 0.03), D-dimer (OR 1.5; 95% CI 1.23–1.98, p<0.01, lactate (OR 1.58; 95% CI 1.28–1.95, p<0.01), and documented thrombosis (OR 3.56; 95% CI 1.30–8.70, p<.05). Race was not a predictor of poor outcomes in the bivariate or multivariate analysis (Table 2). CONCLUSIONS: In a large urban cohort with a predominantly minority population, we identified several clinical predictors of poor outcomes. Of note, race was not a predictor of the primary endpoint in this study. While recent literature has demonstrated worse outcomes among racial minorities infected with SARS-CoV-2, our data suggests these variations are related to social determinants of health rather than biologic causes. FUNDING ACKNOWLEDGEMENT: Type of funding sources: Public hospital(s). Main funding source(s): Loyola University Medical Center
format Online
Article
Text
id pubmed-8767592
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-87675922022-01-20 Predictors of clinical decompensation in patients presenting with COVID-19 in an urban hospital health system Krepostman, N Collins, M Merchant, K De Sirkar, S Chan, L Allen, S Newman, J Patel, D Fareed, J Berg, S Darki, A Eur Heart J Abstract Supplement INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a pandemic which has infected more than 128 million people and led to over 2.8 million deaths worldwide. Although the introduction of efficacious vaccines has led to overall declines in the incidence of SARS-CoV-2 infection, there has been a recent increase in infections once more due to the appearance of mutant strains with higher virulence. It therefore remains vital to identify predictors of poor outcomes in this patient population. PURPOSE: The objective of our study was to identify predictors of prolonged hospitalization, intensive care unit (ICU) admission, intubation, and death in patients infected with SARS-CoV-2. METHODS: We conducted a retrospective analysis of all patients hospitalized with SARS-CoV-2 at our health system that includes one tertiary care center and two community hospitals located in the Chicago metropolitan area. The main outcome was a composite endpoint of hospitalization >28 days, ICU admission, intubation, and death. Explanatory variables associated with the primary outcome in the bivariate analysis (p<0.05) were included in the multivariable logistic regression model. Statistical analysis was performed using IBM SPSS 25.0. RESULTS: Between March 1, 2020 and May 31, 2020, 1029 patients hospitalized with SARS-CoV-2 were included in our analysis. Of these patients, 379 met the composite endpoint. Baseline demographics are described in Table 1. Of note, our cohort consisted of a predominantly minority patient population including 47% Hispanic, 17% African American, 16% Caucasian, and 16% other. In bivariate analysis, age, hypertension, tobacco and alcohol abuse, obesity, coronary artery disease, arrhythmias, valvular heart disease, dyslipidemia, hypertension, stroke, diabetes, documented thrombosis, troponin, CRP, ESR, ferritin, LDH, BNP, D-dimer >5x the upper limit of normal, lactate, and right ventricular outflow tract velocity time integral <9.5 were significant. After multivariable adjustment, explanatory variables associated with the composite endpoint included troponin (OR 2.36; 95% CI 1.08–5.17, p 0.03), D-dimer (OR 1.5; 95% CI 1.23–1.98, p<0.01, lactate (OR 1.58; 95% CI 1.28–1.95, p<0.01), and documented thrombosis (OR 3.56; 95% CI 1.30–8.70, p<.05). Race was not a predictor of poor outcomes in the bivariate or multivariate analysis (Table 2). CONCLUSIONS: In a large urban cohort with a predominantly minority population, we identified several clinical predictors of poor outcomes. Of note, race was not a predictor of the primary endpoint in this study. While recent literature has demonstrated worse outcomes among racial minorities infected with SARS-CoV-2, our data suggests these variations are related to social determinants of health rather than biologic causes. FUNDING ACKNOWLEDGEMENT: Type of funding sources: Public hospital(s). Main funding source(s): Loyola University Medical Center Oxford University Press 2021-10-14 /pmc/articles/PMC8767592/ http://dx.doi.org/10.1093/eurheartj/ehab724.2473 Text en Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2021. For permissions, please email: journals.permissions@oup.com. https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_modelThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
spellingShingle Abstract Supplement
Krepostman, N
Collins, M
Merchant, K
De Sirkar, S
Chan, L
Allen, S
Newman, J
Patel, D
Fareed, J
Berg, S
Darki, A
Predictors of clinical decompensation in patients presenting with COVID-19 in an urban hospital health system
title Predictors of clinical decompensation in patients presenting with COVID-19 in an urban hospital health system
title_full Predictors of clinical decompensation in patients presenting with COVID-19 in an urban hospital health system
title_fullStr Predictors of clinical decompensation in patients presenting with COVID-19 in an urban hospital health system
title_full_unstemmed Predictors of clinical decompensation in patients presenting with COVID-19 in an urban hospital health system
title_short Predictors of clinical decompensation in patients presenting with COVID-19 in an urban hospital health system
title_sort predictors of clinical decompensation in patients presenting with covid-19 in an urban hospital health system
topic Abstract Supplement
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767592/
http://dx.doi.org/10.1093/eurheartj/ehab724.2473
work_keys_str_mv AT krepostmann predictorsofclinicaldecompensationinpatientspresentingwithcovid19inanurbanhospitalhealthsystem
AT collinsm predictorsofclinicaldecompensationinpatientspresentingwithcovid19inanurbanhospitalhealthsystem
AT merchantk predictorsofclinicaldecompensationinpatientspresentingwithcovid19inanurbanhospitalhealthsystem
AT desirkars predictorsofclinicaldecompensationinpatientspresentingwithcovid19inanurbanhospitalhealthsystem
AT chanl predictorsofclinicaldecompensationinpatientspresentingwithcovid19inanurbanhospitalhealthsystem
AT allens predictorsofclinicaldecompensationinpatientspresentingwithcovid19inanurbanhospitalhealthsystem
AT newmanj predictorsofclinicaldecompensationinpatientspresentingwithcovid19inanurbanhospitalhealthsystem
AT pateld predictorsofclinicaldecompensationinpatientspresentingwithcovid19inanurbanhospitalhealthsystem
AT fareedj predictorsofclinicaldecompensationinpatientspresentingwithcovid19inanurbanhospitalhealthsystem
AT bergs predictorsofclinicaldecompensationinpatientspresentingwithcovid19inanurbanhospitalhealthsystem
AT darkia predictorsofclinicaldecompensationinpatientspresentingwithcovid19inanurbanhospitalhealthsystem