Modelling COVID-19 severity in the Republic of Ireland using patient co-morbidities, socioeconomic profile and geographic location, February to November 2020

Abstract Understanding patient progression from symptomatic COVID-19 infection to a severe outcome represents an important tool for improved diagnoses, surveillance, and triage. A series of models have been developed and validated to elucidate hospitalization, admission to an intensive care unit (IC...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	M. Boudou, C. ÓhAiseadha, P. Garvey, J. O’Dwyer, P. Hynds
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f6b5b79539b24dbaaebee73aa2062cab
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:f6b5b79539b24dbaaebee73aa2062cab
record_format	dspace
spelling	oai:doaj.org-article:f6b5b79539b24dbaaebee73aa2062cab2021-12-02T18:33:47ZModelling COVID-19 severity in the Republic of Ireland using patient co-morbidities, socioeconomic profile and geographic location, February to November 202010.1038/s41598-021-98008-62045-2322https://doaj.org/article/f6b5b79539b24dbaaebee73aa2062cab2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98008-6https://doaj.org/toc/2045-2322Abstract Understanding patient progression from symptomatic COVID-19 infection to a severe outcome represents an important tool for improved diagnoses, surveillance, and triage. A series of models have been developed and validated to elucidate hospitalization, admission to an intensive care unit (ICU) and mortality in patients from the Republic of Ireland. This retrospective cohort study of patients with laboratory-confirmed symptomatic COVID-19 infection included data extracted from national COVID-19 surveillance forms (i.e., age, gender, underlying health conditions, occupation) and geographically-referenced potential predictors (i.e., urban/rural classification, socio-economic profile). Generalised linear models and recursive partitioning and regression trees were used to elucidate COVID-19 progression. The incidence of symptomatic infection over the study-period was 0.96% (n = 47,265), of whom 3781 (8%) required hospitalisation, 615 (1.3%) were admitted to ICU and 1326 (2.8%) died. Models demonstrated an increasingly efficacious fit for predicting hospitalization [AUC 0.816 (95% CI 0.809, 0.822)], admission to ICU [AUC 0.885 (95% CI 0.88 0.89)] and death [AUC of 0.955 (95% CI 0.951 0.959)]. Severe obesity (BMI ≥ 40) was identified as a risk factor across all prognostic models; severely obese patients were substantially more likely to receive ICU treatment [OR 19.630] or die [OR 10.802]. Rural living was associated with an increased risk of hospitalization (OR 1.200 (95% CI 1.143–1.261)]. Urban living was associated with ICU admission [OR 1.533 (95% CI 1.606–1.682)]. Models provide approaches for predicting COVID-19 prognoses, allowing for evidence-based decision-making pertaining to targeted non-pharmaceutical interventions, risk-based vaccination priorities and improved patient triage.M. BoudouC. ÓhAiseadhaP. GarveyJ. O’DwyerP. HyndsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q M. Boudou C. ÓhAiseadha P. Garvey J. O’Dwyer P. Hynds Modelling COVID-19 severity in the Republic of Ireland using patient co-morbidities, socioeconomic profile and geographic location, February to November 2020
description	Abstract Understanding patient progression from symptomatic COVID-19 infection to a severe outcome represents an important tool for improved diagnoses, surveillance, and triage. A series of models have been developed and validated to elucidate hospitalization, admission to an intensive care unit (ICU) and mortality in patients from the Republic of Ireland. This retrospective cohort study of patients with laboratory-confirmed symptomatic COVID-19 infection included data extracted from national COVID-19 surveillance forms (i.e., age, gender, underlying health conditions, occupation) and geographically-referenced potential predictors (i.e., urban/rural classification, socio-economic profile). Generalised linear models and recursive partitioning and regression trees were used to elucidate COVID-19 progression. The incidence of symptomatic infection over the study-period was 0.96% (n = 47,265), of whom 3781 (8%) required hospitalisation, 615 (1.3%) were admitted to ICU and 1326 (2.8%) died. Models demonstrated an increasingly efficacious fit for predicting hospitalization [AUC 0.816 (95% CI 0.809, 0.822)], admission to ICU [AUC 0.885 (95% CI 0.88 0.89)] and death [AUC of 0.955 (95% CI 0.951 0.959)]. Severe obesity (BMI ≥ 40) was identified as a risk factor across all prognostic models; severely obese patients were substantially more likely to receive ICU treatment [OR 19.630] or die [OR 10.802]. Rural living was associated with an increased risk of hospitalization (OR 1.200 (95% CI 1.143–1.261)]. Urban living was associated with ICU admission [OR 1.533 (95% CI 1.606–1.682)]. Models provide approaches for predicting COVID-19 prognoses, allowing for evidence-based decision-making pertaining to targeted non-pharmaceutical interventions, risk-based vaccination priorities and improved patient triage.
format	article
author	M. Boudou C. ÓhAiseadha P. Garvey J. O’Dwyer P. Hynds
author_facet	M. Boudou C. ÓhAiseadha P. Garvey J. O’Dwyer P. Hynds
author_sort	M. Boudou
title	Modelling COVID-19 severity in the Republic of Ireland using patient co-morbidities, socioeconomic profile and geographic location, February to November 2020
title_short	Modelling COVID-19 severity in the Republic of Ireland using patient co-morbidities, socioeconomic profile and geographic location, February to November 2020
title_full	Modelling COVID-19 severity in the Republic of Ireland using patient co-morbidities, socioeconomic profile and geographic location, February to November 2020
title_fullStr	Modelling COVID-19 severity in the Republic of Ireland using patient co-morbidities, socioeconomic profile and geographic location, February to November 2020
title_full_unstemmed	Modelling COVID-19 severity in the Republic of Ireland using patient co-morbidities, socioeconomic profile and geographic location, February to November 2020
title_sort	modelling covid-19 severity in the republic of ireland using patient co-morbidities, socioeconomic profile and geographic location, february to november 2020
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f6b5b79539b24dbaaebee73aa2062cab
work_keys_str_mv	AT mboudou modellingcovid19severityintherepublicofirelandusingpatientcomorbiditiessocioeconomicprofileandgeographiclocationfebruarytonovember2020 AT cohaiseadha modellingcovid19severityintherepublicofirelandusingpatientcomorbiditiessocioeconomicprofileandgeographiclocationfebruarytonovember2020 AT pgarvey modellingcovid19severityintherepublicofirelandusingpatientcomorbiditiessocioeconomicprofileandgeographiclocationfebruarytonovember2020 AT jodwyer modellingcovid19severityintherepublicofirelandusingpatientcomorbiditiessocioeconomicprofileandgeographiclocationfebruarytonovember2020 AT phynds modellingcovid19severityintherepublicofirelandusingpatientcomorbiditiessocioeconomicprofileandgeographiclocationfebruarytonovember2020
_version_	1718377955729604608

Modelling COVID-19 severity in the Republic of Ireland using patient co-morbidities, socioeconomic profile and geographic location, February to November 2020

Ejemplares similares