• Guangyao Wu, Pei Yang, Yuanliang Xie, Henry C Woodruff, Xiangang Rao, Julien Guiot, Anne-Noelle Frix, Renaud Louis, Michel Moutschen, Jiawei Li, Jing Li, Chenggong Yan, Dan Du, Shengchao Zhao, Yi Ding, Bin Liu, Wenwu Sun, Fabrizio Albarello, Alessandra D'Abramo, Vincenzo Schininà, Emanuele Nicastri, Mariaelena Occhipinti, Giovanni Barisione, Emanuela Barisione, Iva Halilaj, Pierre Lovinfosse, Xiang Wang, Jianlin Wu, and Philippe Lambin.
• The D-Lab, Dept of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Center+, Maastricht, The Netherlands g.wu@maastrichtuniversity.nl.
• Eur. Respir. J. 2020 Aug 1; 56 (2).

BackgroundThe outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality.ObjectiveTo develop and validate a machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission.Method725 patients were used to train and validate the model. This included a retrospective cohort from Wuhan, China of 299 hospitalised COVID-19 patients from 23 December 2019 to 13 February 2020, and five cohorts with 426 patients from eight centres in China, Italy and Belgium from 20 February 2020 to 21 March 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.ResultsIn the retrospective cohort, the median age was 50 years and 137 (45.8%) were male. In the five test cohorts, the median age was 62 years and 236 (55.4%) were male. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.93, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 55.0% to 88.0%, most 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.ConclusionThe 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.Copyright ©ERS 2020.

42 keywords...

hide…