About: Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study

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About: Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study
Creator	Li, Jing Liu, Bin Wang, Xiang Xie, Yuanliang Nicastri, Emanuele Li, Jiawei Ding, Yi Louis, Renaud Albarello, Fabrizio Schininà, Vincenzo Sun, Wenwu Wang, ; Liu, ; D'abramo, Alessandra Yang, ; Barisione, Emanuela Moutschen, Michel Yan, ; Yang, Pei Yan, Chenggong Barisione, Giovanni Du, Dan Frix, Anne-Noelle Guiot, Julien Halilaj, Iva Lambin, Philippe Lovinfosse, Pierre Moutschen, ; Occhipinti, Mariaelena Rao, Xiangang Woodruff, Henry Wu, Guangyao Wu, Jianlin Zhao, Shengchao
Source	Medline; PMC
abstract	BACKGROUND: The outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. OBJECTIVE: To develop and validate machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. METHOD: 725 patients were used to train and validate the model including a retrospective cohort of 299 hospitalised COVID-19 patients at Wuhan, China, from December 23, 2019, to February 13, 2020, and five cohorts with 426 patients from eight centers in China, Italy, and Belgium, from February 20, 2020, to March 21, 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion-matrix. RESULTS: The median age was 50.0 years and 137 (45.8%) were men in the retrospective cohort. The median age was 62.0 years and 236 (55.4%) were men in five cohorts. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.89, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 57.5% to 88.0%, all of which performed better than the pneumonia severity index. The cut-off values of the low, medium, and high-risk probabilities were 0.21 and 0.80. The online-calculators can be found at www.covid19risk.ai. CONCLUSION: The machine-learning model, nomogram, and online-calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission.
has issue date	2020-07-02(xsd:dateTime)
bibo:doi	10.1183/13993003.01104-2020
bibo:pmid	32616597
has license	cc-by-nc
sha1sum (hex)	928604ddad0ace09760461c7acbb807e546dbf94
schema:url	https://doi.org/10.1183/13993003.01104-2020
resource representing a document's title	Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study
has PubMed Central identifier	PMC7331655
has PubMed identifier	32616597
schema:publication	Eur Respir J
resource representing a document's body	covid:928604ddad0ace09760461c7acbb807e546dbf94#body_text
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