PT - JOURNAL ARTICLE AU - Guangyao Wu AU - Pei Yang AU - Yuanliang Xie AU - Henry C. Woodruff AU - Xiangang Rao AU - Julien Guiot AU - Anne-Noelle Frix AU - Renaud Louis AU - Michel Moutschen AU - Jiawei Li AU - Jing Li AU - Chenggong Yan AU - Dan Du AU - Shengchao Zhao AU - Yi Ding AU - Bin Liu AU - Wenwu Sun AU - Fabrizio Albarello AU - Alessandra D'Abramo AU - Vincenzo Schininà AU - Emanuele Nicastri AU - Mariaelena Occhipinti AU - Giovanni Barisione AU - Emanuela Barisione AU - Iva Halilaj AU - Pierre Lovinfosse AU - Xiang Wang AU - Jianlin Wu AU - Philippe Lambin TI - Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study AID - 10.1183/13993003.01104-2020 DP - 2020 Jan 01 TA - European Respiratory Journal PG - 2001104 4099 - http://erj.ersjournals.com/content/early/2020/06/25/13993003.01104-2020.short 4100 - http://erj.ersjournals.com/content/early/2020/06/25/13993003.01104-2020.full 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.