Sergio Venturini1, Daniele Orso2,3, Francesco Cugini4, Massimo Crapis1, Sara Fossati1, Astrid Callegari1, Tommaso Pellis5, Maurizio Tonizzo6, Alessandro Grembiale6, Alessia Rosso6, Mario Tamburrini7, Natascia D'Andrea2,3, Luigi Vetrugno2,3, Tiziana Bove2,3. 1. Department of Infectious Diseases, ASFO Santa Maria degli Angeli Hospital of Pordenone, Pordenone, Italy. 2. Department of Medicine, University of Udine, Udine, Italy. 3. Department of Anesthesia and Intensive Care, ASUFC Santa Maria della Misericordia University Hospital of Udine, Udine, Italy. 4. Department of Emergency Medicine, ASUFC Hospital of San Daniele, Udine, Italy. 5. Department of Anesthesia and Intensive Care, ASFO Santa Maria degli Angeli Hospital of Pordenone, Pordenone, Italy. 6. Department of Internal Medicine, ASFO Santa Maria degli Angeli Hospital of Pordenone, Pordenone, Italy. 7. Department of Pneumology, ASFO Santa Maria degli Angeli Hospital of Pordenone, Pordenone, Italy.
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.
BACKGROUND: Early detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infectedpatients 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-19patients 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-19patients 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.
Authors: Sergio Venturini; Daniele Orso; Francesco Cugini; Francesco Martin; Cecilia Boccato; Laura De Santi; Elisa Pontoni; Silvia Tomasella; Fabrizio Nicotra; Alessandro Grembiale; Maurizio Tonizzo; Silvia Grazioli; Sara Fossati; Astrid Callegari; Giovanni Del Fabro; Massimo Crapis Journal: Infez Med Date: 2022-09-01
Authors: Rebecca De Lorenzo; Clara Sciorati; Giuseppe A Ramirez; Barbara Colombo; Nicola I Lorè; Annalisa Capobianco; Cristina Tresoldi; Daniela M Cirillo; Fabio Ciceri; Angelo Corti; Patrizia Rovere-Querini; Angelo A Manfredi Journal: PLoS One Date: 2022-04-25 Impact factor: 3.240