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Using Objective Electrocardiographic Data To Better Predict Mortality In Patients Hospitalized With COVID-19
BACKGROUND: The impact of SARS-CoV-2 infection on intrinsic myocardial conduction continues to be an area of focus amongst the medical community. Our objective was to investigate if specific myocardial conduction abnormalities were independently associated with mortality in patients hospitalized wit...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10090089/ http://dx.doi.org/10.1016/j.cardfail.2022.10.238 |
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author | Malik, Bobby Burns, Katherine Villasmil, Ricardo Walo, Richard Baez-Rodriguez, Karla Wiese-Rometsch, Wilhelmine Whiteside, Zachary |
author_facet | Malik, Bobby Burns, Katherine Villasmil, Ricardo Walo, Richard Baez-Rodriguez, Karla Wiese-Rometsch, Wilhelmine Whiteside, Zachary |
author_sort | Malik, Bobby |
collection | PubMed |
description | BACKGROUND: The impact of SARS-CoV-2 infection on intrinsic myocardial conduction continues to be an area of focus amongst the medical community. Our objective was to investigate if specific myocardial conduction abnormalities were independently associated with mortality in patients hospitalized with COVID 19. METHODS: Under IRB exemption, the electronic medical records of COVID-19 patients (N=3840) undergoing index hospitalization were reviewed to extract presentation ECG conduction data, demographics, and laboratory results (within 8h). This patient cohort was then separated into two groups based on mortality vs. no mortality (N=520). Logistical regression was used to test association of ECG conduction intervals with mortality. A subgroup analysis of 651 patients who underwent at least 1 ECG in the 12 months prior to their COVID hospitalization were analyzed to detect statistically significant differences in conduction intervals pre and post SARS-CoV-2 infection. RESULTS: According to our nominal logistic fit for hospital mortality, Heart Rate (HR) >100 (p=0.0007; LW 4.14), QRS duration > 120 ms (p=0.0053; LW 2.27), and QTc prolongation (defined as QTc > 450ms in males; QTc > 460ms in females) (p=0.0089; LW 2.04) were independently associated with higher risk of mortality. LogWorth (LW) calculations were included in an effort to estimate the proportional effect each variable has on overall mortality. LW > 2 were shown to be statistically significant with p< 0.05 with HR > 100 (LW 4.14) having the highest proportional effect on mortality followed by QRSd (LW 2.27) then QTc prolongation (LW 2.04). PR interval> 200ms (p=0.30) and QRS axis (p=0.15) were not associated with higher risk of mortality. Our subgroup analysis of the 651 patients mentioned above yielded no statistically significant differences in conduction intervals pre & post SARS-CoV-2 infection. CONCLUSIONS: : Amongst our patient cohort, HR > 100, QRSd > 120ms, and QTc prolongation (QTc > 450 in males; QTc > 460 in females) were each independently associated with higher risk of mortality in patients hospitalized with COVID 19. Subgroup analysis of 651 patients showed no statistically significant differences in conduction intervals pre and post SARS-CoV-2 infection. These findings support the use of objective ECG data in risk stratifying patients hospitalized with COVID 19. |
format | Online Article Text |
id | pubmed-10090089 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Published by Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100900892023-04-12 Using Objective Electrocardiographic Data To Better Predict Mortality In Patients Hospitalized With COVID-19 Malik, Bobby Burns, Katherine Villasmil, Ricardo Walo, Richard Baez-Rodriguez, Karla Wiese-Rometsch, Wilhelmine Whiteside, Zachary J Card Fail 218 BACKGROUND: The impact of SARS-CoV-2 infection on intrinsic myocardial conduction continues to be an area of focus amongst the medical community. Our objective was to investigate if specific myocardial conduction abnormalities were independently associated with mortality in patients hospitalized with COVID 19. METHODS: Under IRB exemption, the electronic medical records of COVID-19 patients (N=3840) undergoing index hospitalization were reviewed to extract presentation ECG conduction data, demographics, and laboratory results (within 8h). This patient cohort was then separated into two groups based on mortality vs. no mortality (N=520). Logistical regression was used to test association of ECG conduction intervals with mortality. A subgroup analysis of 651 patients who underwent at least 1 ECG in the 12 months prior to their COVID hospitalization were analyzed to detect statistically significant differences in conduction intervals pre and post SARS-CoV-2 infection. RESULTS: According to our nominal logistic fit for hospital mortality, Heart Rate (HR) >100 (p=0.0007; LW 4.14), QRS duration > 120 ms (p=0.0053; LW 2.27), and QTc prolongation (defined as QTc > 450ms in males; QTc > 460ms in females) (p=0.0089; LW 2.04) were independently associated with higher risk of mortality. LogWorth (LW) calculations were included in an effort to estimate the proportional effect each variable has on overall mortality. LW > 2 were shown to be statistically significant with p< 0.05 with HR > 100 (LW 4.14) having the highest proportional effect on mortality followed by QRSd (LW 2.27) then QTc prolongation (LW 2.04). PR interval> 200ms (p=0.30) and QRS axis (p=0.15) were not associated with higher risk of mortality. Our subgroup analysis of the 651 patients mentioned above yielded no statistically significant differences in conduction intervals pre & post SARS-CoV-2 infection. CONCLUSIONS: : Amongst our patient cohort, HR > 100, QRSd > 120ms, and QTc prolongation (QTc > 450 in males; QTc > 460 in females) were each independently associated with higher risk of mortality in patients hospitalized with COVID 19. Subgroup analysis of 651 patients showed no statistically significant differences in conduction intervals pre and post SARS-CoV-2 infection. These findings support the use of objective ECG data in risk stratifying patients hospitalized with COVID 19. Published by Elsevier Inc. 2023-04 2023-04-12 /pmc/articles/PMC10090089/ http://dx.doi.org/10.1016/j.cardfail.2022.10.238 Text en Copyright © 2022 Published by Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | 218 Malik, Bobby Burns, Katherine Villasmil, Ricardo Walo, Richard Baez-Rodriguez, Karla Wiese-Rometsch, Wilhelmine Whiteside, Zachary Using Objective Electrocardiographic Data To Better Predict Mortality In Patients Hospitalized With COVID-19 |
title | Using Objective Electrocardiographic Data To Better Predict Mortality In Patients Hospitalized With COVID-19 |
title_full | Using Objective Electrocardiographic Data To Better Predict Mortality In Patients Hospitalized With COVID-19 |
title_fullStr | Using Objective Electrocardiographic Data To Better Predict Mortality In Patients Hospitalized With COVID-19 |
title_full_unstemmed | Using Objective Electrocardiographic Data To Better Predict Mortality In Patients Hospitalized With COVID-19 |
title_short | Using Objective Electrocardiographic Data To Better Predict Mortality In Patients Hospitalized With COVID-19 |
title_sort | using objective electrocardiographic data to better predict mortality in patients hospitalized with covid-19 |
topic | 218 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10090089/ http://dx.doi.org/10.1016/j.cardfail.2022.10.238 |
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