Katalog Plus
Bibliothek der Frankfurt UAS
Bald neuer Katalog: sichern Sie sich schon vorab Ihre persönlichen Merklisten im Nutzerkonto: Anleitung.
Dieses Ergebnis aus BASE kann Gästen nicht angezeigt werden.  Login für vollen Zugriff.

Classification and analysis of outcome predictors in non‐critically ill COVID‐19 patients

Title: Classification and analysis of outcome predictors in non‐critically ill COVID‐19 patients
Authors: Venturini, Sergio; Orso, Daniele; Cugini, Francesco; Crapis, Massimo; Fossati, Sara; Callegari, Astrid; Pellis, Tommaso; Tonizzo, Maurizio; Grembiale, Alessandro; Rosso, Alessia; Tamburrini, Mario; D'Andrea, Natascia; Vetrugno, Luigi; Bove, Tiziana
Source: Internal Medicine Journal ; volume 51, issue 4, page 506-514 ; ISSN 1444-0903 1445-5994
Publisher Information: Wiley
Publication Year: 2021
Collection: Wiley Online Library (Open Access Articles via Crossref)
Description: 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.
Document Type: article in journal/newspaper
Language: English
DOI: 10.1111/imj.15140
Availability: https://doi.org/10.1111/imj.15140; https://onlinelibrary.wiley.com/doi/pdf/10.1111/imj.15140; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/imj.15140
Rights: http://onlinelibrary.wiley.com/termsAndConditions#vor
Accession Number: edsbas.F6367E4F
Database: BASE