RT Journal Article
SR Electronic
T1 Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study
JF European Respiratory Journal
JO Eur Respir J
FD European Respiratory Society
SP 2001104
DO 10.1183/13993003.01104-2020
A1 Guangyao Wu
A1 Pei Yang
A1 Yuanliang Xie
A1 Henry C. Woodruff
A1 Xiangang Rao
A1 Julien Guiot
A1 Anne-Noelle Frix
A1 Renaud Louis
A1 Michel Moutschen
A1 Jiawei Li
A1 Jing Li
A1 Chenggong Yan
A1 Dan Du
A1 Shengchao Zhao
A1 Yi Ding
A1 Bin Liu
A1 Wenwu Sun
A1 Fabrizio Albarello
A1 Alessandra D'Abramo
A1 Vincenzo Schininà
A1 Emanuele Nicastri
A1 Mariaelena Occhipinti
A1 Giovanni Barisione
A1 Emanuela Barisione
A1 Iva Halilaj
A1 Pierre Lovinfosse
A1 Xiang Wang
A1 Jianlin Wu
A1 Philippe Lambin
YR 2020
UL http://erj.ersjournals.com/content/early/2020/06/25/13993003.01104-2020.abstract
AB 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.An internationally validated model, nomogram, and online- calculator for severity risk assessment and triage of COVID-19 patients at hospital admission.