Classification and analysis of outcome predictors in non-critically ill COVID-19 patients.
Intern Med J
; 51(4): 506-514, 2021 04.
Article
em En
| MEDLINE
| ID: mdl-33835685
ABSTRACT
BACKGROUND:
Early detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients who could develop a severe form of COVID-19 must be considered of great importance to carry out adequate care and optimise the use of limited resources.AIMS:
To use several machine learning classification models to analyse a series of non-critically ill COVID-19 patients admitted to a general medicine ward to verify if any clinical variables recorded could predict the clinical outcome.METHODS:
We retrospectively analysed non-critically ill patients with COVID-19 admitted to the general ward of the hospital in Pordenone from 1 March 2020 to 30 April 2020. Patients' characteristics were compared based on clinical outcomes. Through several machine learning classification models, some predictors for clinical outcome were detected.RESULTS:
In the considered period, we analysed 176 consecutive patients admitted 119 (67.6%) were discharged, 35 (19.9%) dead and 22 (12.5%) were transferred to intensive care unit. The most accurate models were a random forest model (M2) and a conditional inference tree model (M5) (accuracy = 0.79; 95% confidence interval 0.64-0.90, for both). For M2, glomerular filtration rate and creatinine were the most accurate predictors for the outcome, followed by age and fraction-inspired oxygen. For M5, serum sodium, body temperature and arterial pressure of oxygen and inspiratory fraction of oxygen ratio were the most reliable predictors.CONCLUSIONS:
In non-critically ill COVID-19 patients admitted to a medical ward, glomerular filtration rate, creatinine and serum sodium were promising predictors for the clinical outcome. Some factors not determined by COVID-19, such as age or dementia, influence clinical outcomes.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
COVID-19
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
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Screening_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article