Additive Value of Computed Tomography Severity Scores to Predict Lengths of Stay in Hospital and ICU for Covid-19 Patients: A Machine Learning Study

Introduction: During the outbreak of COVID-19, most hospitals faced resource shortages due to the greatsurges in the influx of infected COVID-19 patients and demand exceeding capacities. Predicting the lengths ofstay (LOS) of the patients can help to make proper resource-planning decisions. CT-SS accurately determinesthe disease severity and could be considered an appropriate prognostic factor to predict patients’ LOS.In this study, we evaluate the additive value of CT-SS in the prediction of hospital and ICU LOSs of COVID-19patients. Methods: This single-center study retrospectively reviewed a hospital-based COVID-19 registry database from 6854 cases of suspected COVID-19. Four well-known ML classification models including kNN, MLP, SVM, and C4.5 decision tree algorithms were used to predict hospital and ICU LOSs of COVID-19 patients. The confusion matrix-based performance measures were used to evaluate the classification performances of the ML algorithms. Results: For predicting hospital LOS, the kNN model with an accuracy of 77.1%, sensitivity of 100.0%, precision of 68.6%, specificity of 54.2%, and AUC of around 99.4% had the best performance among the other three ML techniques. This algorithm with 94.4% sensitivity, 74.6% specificity, 84.5% accuracy, 78.8% precision, 85.9% F-Measure, and an AUC of 95.3% had also the best performance for predicting ICU LOS of the patients. Conclusion: The performances of the ML predictive models for predicting hospital and ICU LOSs of COVID-19 patients were improved when CT-SS data was integrated into the input dataset.