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

Background: Coronavirus Disease (COVID-19) causes burdens to the intensive care unit (ICU). Evidence-based planning and optimal allocation of the scarce ICU resources is urgently needed but remains unaddressed. Methods: Retrospective data from 733 in-patients with laboratory-confirmed COVD-19 in Wuhan, China, as of March 18, 2020. Demographic, clinical course and laboratory were collected and analyzed using machine learning and Least Absolute Shrinkage and Selection Operator (LASSO) to build the predictive models. Findings: First, a predictive model using an ensemble learning strategy and classifier voting mechanism was built to identify the ICU admission based on ten factors identified in 909 potential predictors. It yielded a sensitivity of 0.86 and specificity of 0.82. Second, a model built on ten significant variables predicted whether patients would die despite entering the ICU with accuracy and AUC of 92% and 98% using 3-fold cross-validation. Third, a sparse regression model was built to estimate the length of stay in ICU. It yielded that the average difference between the predicted and empirical time is less than one day. Lymphocyte absolute value appeared in all prediction tasks, thus it was a very noteworthy factor. Interpretation: We identified variables and tested the accuracy to predict the need for ICU admission, death despite ICU admission, and among survivors, length of ICU stay, before patients were admitted to ICU. Our predictions provided quantitative and objective evidence for the optimal planning and allocation of ICU resources. We revealed the most predictive variables to assist clinical workflow.Funding Statement: This work was supported by the grant from the National Natural Science Foundation of China (no.61771007).Declaration of Interests: All authors declare no competing interests.Ethics Approval Statement: This study was approved by the First Affiliated Hospital of Guangxi Medical University Hospital Ethics Committee, with the informed consent being waived.