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Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study

OBJECTIVES: To develop a disease stratification model for COVID-19 that updates according to changes in a patient’s condition while in hospital to facilitate patient management and resource allocation. DESIGN: In this retrospective cohort study, we adopted a landmarking approach to dynamic predictio...

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Autores principales: Wiegand, Martin, Cowan, Sarah L, Waddington, Claire S, Halsall, David J, Keevil, Victoria L, Tom, Brian D M, Taylor, Vince, Gkrania-Klotsas, Effrossyni, Preller, Jacobus, Goudie, Robert J B
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445230/
https://www.ncbi.nlm.nih.gov/pubmed/36691139
http://dx.doi.org/10.1136/bmjopen-2021-060026
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author Wiegand, Martin
Cowan, Sarah L
Waddington, Claire S
Halsall, David J
Keevil, Victoria L
Tom, Brian D M
Taylor, Vince
Gkrania-Klotsas, Effrossyni
Preller, Jacobus
Goudie, Robert J B
author_facet Wiegand, Martin
Cowan, Sarah L
Waddington, Claire S
Halsall, David J
Keevil, Victoria L
Tom, Brian D M
Taylor, Vince
Gkrania-Klotsas, Effrossyni
Preller, Jacobus
Goudie, Robert J B
author_sort Wiegand, Martin
collection PubMed
description OBJECTIVES: To develop a disease stratification model for COVID-19 that updates according to changes in a patient’s condition while in hospital to facilitate patient management and resource allocation. DESIGN: In this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression. SETTING: All data used in this study were obtained from a single UK teaching hospital. PARTICIPANTS: We developed the model using 473 consecutive patients with COVID-19 presenting to a UK hospital between 1 March 2020 and 12 September 2020; and temporally validated using data on 1119 patients presenting between 13 September 2020 and 17 March 2021. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome is all-cause in-hospital mortality within 48 hours of the prediction time. We accounted for the competing risks of discharge from hospital alive and transfer to a tertiary intensive care unit for extracorporeal membrane oxygenation. RESULTS: Our final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, oxygen saturation/fractional inspired oxygen ratio, white cell count, presence of acidosis (pH <7.35) and interleukin-6. Internal validation achieved an area under the receiver operating characteristic (AUROC) of 0.90 (95% CI 0.87 to 0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83 to 0.88). CONCLUSIONS: Our model incorporates both static risk factors (eg, age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient’s clinical condition. On successful external validation, the model has the potential to be a powerful clinical risk assessment tool. TRIAL REGISTRATION: The study is registered as ‘researchregistry5464’ on the Research Registry (www.researchregistry.com).
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spelling pubmed-94452302022-09-06 Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study Wiegand, Martin Cowan, Sarah L Waddington, Claire S Halsall, David J Keevil, Victoria L Tom, Brian D M Taylor, Vince Gkrania-Klotsas, Effrossyni Preller, Jacobus Goudie, Robert J B BMJ Open Epidemiology OBJECTIVES: To develop a disease stratification model for COVID-19 that updates according to changes in a patient’s condition while in hospital to facilitate patient management and resource allocation. DESIGN: In this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression. SETTING: All data used in this study were obtained from a single UK teaching hospital. PARTICIPANTS: We developed the model using 473 consecutive patients with COVID-19 presenting to a UK hospital between 1 March 2020 and 12 September 2020; and temporally validated using data on 1119 patients presenting between 13 September 2020 and 17 March 2021. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome is all-cause in-hospital mortality within 48 hours of the prediction time. We accounted for the competing risks of discharge from hospital alive and transfer to a tertiary intensive care unit for extracorporeal membrane oxygenation. RESULTS: Our final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, oxygen saturation/fractional inspired oxygen ratio, white cell count, presence of acidosis (pH <7.35) and interleukin-6. Internal validation achieved an area under the receiver operating characteristic (AUROC) of 0.90 (95% CI 0.87 to 0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83 to 0.88). CONCLUSIONS: Our model incorporates both static risk factors (eg, age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient’s clinical condition. On successful external validation, the model has the potential to be a powerful clinical risk assessment tool. TRIAL REGISTRATION: The study is registered as ‘researchregistry5464’ on the Research Registry (www.researchregistry.com). BMJ Publishing Group 2022-09-05 /pmc/articles/PMC9445230/ /pubmed/36691139 http://dx.doi.org/10.1136/bmjopen-2021-060026 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Epidemiology
Wiegand, Martin
Cowan, Sarah L
Waddington, Claire S
Halsall, David J
Keevil, Victoria L
Tom, Brian D M
Taylor, Vince
Gkrania-Klotsas, Effrossyni
Preller, Jacobus
Goudie, Robert J B
Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study
title Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study
title_full Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study
title_fullStr Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study
title_full_unstemmed Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study
title_short Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study
title_sort development and validation of a dynamic 48-hour in-hospital mortality risk stratification for covid-19 in a uk teaching hospital: a retrospective cohort study
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445230/
https://www.ncbi.nlm.nih.gov/pubmed/36691139
http://dx.doi.org/10.1136/bmjopen-2021-060026
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