Machine Learning to Predict ICU Admission, ICU Mortality and Survivors' Length of Stay among COVID-19 Patients: Toward Optimal Allocation of ICU Resources

Dan, T. T.; Li, Y.; Zhu, Z. W.; Chen, X. J.; Quan, W. X.; Hu, Y.; Tao, G. H.; Zhu, L.; Zhu, J. J.; Jin, Y. Y.; Li, L. G.; Liang, C. K.; Wen, H. C.; Cai, H. M.; Dan, T.; Zhu, Z.; Chen, X.; Quan, W.; Tao, G.; Zhu, J.; Jin, Y.; Li, L.; Liang, C.; Wen, H.; Cai, H.

COVID-19 causes burdens to the ICU. Evidence-based planning and optimal allocation of the scarce ICU resources is urgently needed but remains unaddressed. This study aims to identify variables and test the accuracy to predict the need for ICU admission, death despite ICU care, and among survivors, length of ICU stay, before patients were admitted to ICU. Retrospective data from 733 in-patients confirmed with COVD-19 in Wuhan, China, as of March 18, 2020. Demographic, clinical and laboratory were collected and analyzed using machine learning to build the predictive models. The built machine learning model can accurately assess ICU admission, length of ICU stay, and mortality in COVID-19 patients toward optimal allocation of ICU resources. The prediction can be done by using the clinical data collected within 1-15 days before the actual ICU admission. Lymphocyte absolute value involved in all prediction tasks with a higher AUC.