ABSTRACTChest computed tomography (CT) is widely used for the management of Coronavirus disease 2019 (COVID-19) 49 pneumonia because of its availability and rapidity1–3. The standard of reference for confirming COVID-19 relies on microbiological tests but these tests might not be available in an emergency setting and their results are not immediately available, contrary to CT. In addition to its role for early diagnosis, CT has a prognostic role by allowing visually evaluating the extent of COVID-19 lung abnormalities4, 5. The objective of this study is to address prediction of short-term outcomes, especially need for mechanical ventilation. In this multi-centric study, we propose an end-to-end artificial intelligence solution for automatic quantification and prognosis assessment by combining automatic CT delineation of lung disease meeting expert’s performance and data-driven identification of biomarkers for its prognosis. AI-driven combination of variables with CT-based biomarkers offers perspectives for optimal patient management given the shortage of intensive care beds and ventilators6, 7.
UTILITY OF THE SPPB FOR SHORT-TERM OUTCOME PREDICTION OF MULTIMORBID GERIATRIC REHABILITANTS
