tetano
Editor, Senior Moderator
Intern Med J
. 2021 Apr 9.
doi: 10.1111/imj.15140. Online ahead of print.
Classification and analysis of outcome predictors in non-critically ill COVID-19 patients
Sergio Venturini[SUP] 1 [/SUP], Daniele Orso[SUP] 2 3 [/SUP], Francesco Cugini[SUP] 4 [/SUP], Massimo Crapis[SUP] 1 [/SUP], Sara Fossati[SUP] 1 [/SUP], Astrid Callegari[SUP] 1 [/SUP], Tommaso Pellis[SUP] 5 [/SUP], Maurizio Tonizzo[SUP] 6 [/SUP], Alessandro Grembiale[SUP] 6 [/SUP], Alessia Rosso[SUP] 6 [/SUP], Mario Tamburrini[SUP] 7 [/SUP], Natascia D'Andrea[SUP] 2 3 [/SUP], Luigi Vetrugno[SUP] 2 3 [/SUP], Tiziana Bove[SUP] 2 3 [/SUP]
Affiliations
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.
Keywords: COVID-19; machine learning; non-critically ill; prediction.
. 2021 Apr 9.
doi: 10.1111/imj.15140. Online ahead of print.
Classification and analysis of outcome predictors in non-critically ill COVID-19 patients
Sergio Venturini[SUP] 1 [/SUP], Daniele Orso[SUP] 2 3 [/SUP], Francesco Cugini[SUP] 4 [/SUP], Massimo Crapis[SUP] 1 [/SUP], Sara Fossati[SUP] 1 [/SUP], Astrid Callegari[SUP] 1 [/SUP], Tommaso Pellis[SUP] 5 [/SUP], Maurizio Tonizzo[SUP] 6 [/SUP], Alessandro Grembiale[SUP] 6 [/SUP], Alessia Rosso[SUP] 6 [/SUP], Mario Tamburrini[SUP] 7 [/SUP], Natascia D'Andrea[SUP] 2 3 [/SUP], Luigi Vetrugno[SUP] 2 3 [/SUP], Tiziana Bove[SUP] 2 3 [/SUP]
Affiliations
- PMID: 33835685
- DOI: 10.1111/imj.15140
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.
Keywords: COVID-19; machine learning; non-critically ill; prediction.