Idioma:

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

Boudou, M ; ÓhAiseadha, C ; Garvey, P ; O'Dwyer, J ; Hynds, P

Scientific reports, 2021-09, Vol.11 (1), p.18474-11, Article 18474 [Periódico revisado por pares]

England: Nature Publishing Group

Texto completo disponível

Citações Citado por

Enviar para

Título:
Modelling COVID-19 severity in the Republic of Ireland using patient co-morbidities, socioeconomic profile and geographic location, February to November 2020
Autor: Boudou, M ; ÓhAiseadha, C ; Garvey, P ; O'Dwyer, J ; Hynds, P
Assuntos: Adult ; Aged ; Aged, 80 and over ; Comorbidity ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; COVID-19 - mortality ; Decision making ; Evidence-Based Medicine ; Female ; Health surveillance ; Hospital Mortality ; Hospitalization ; Humans ; Incidence ; Infections ; Intensive Care Units ; Ireland - epidemiology ; Linear Models ; Male ; Middle Aged ; Obesity ; Obesity, Morbid - epidemiology ; Patients ; Population Surveillance ; Prognosis ; Regression analysis ; Retrospective Studies ; Risk factors ; Rural Population - statistics & numerical data ; Socioeconomic Factors ; Urban Population - statistics & numerical data ; Vaccination
É parte de: Scientific reports, 2021-09, Vol.11 (1), p.18474-11, Article 18474
Descrição: 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.
Editor: England: Nature Publishing Group
Idioma: Inglês

Links

View this record in MEDLINE/PubMed

Voltar para lista de resultados

Realização: Logos de Redes Sociais:

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

Boudou, M ; ÓhAiseadha, C ; Garvey, P ; O'Dwyer, J ; Hynds, P

England: Nature Publishing Group

Buscando em bases de dados remotas. Favor aguardar.