Additive value of computed tomography severity scores to predict lengths of stay in hospital and ICU for COVID-19 patients: a machine learning study

Additive value of CT-SS to predict COVID-19 hospital and ICU LOSs: a ML study

Abstract

Introduction: During the outbreak of COVID-19, most hospitals faced resource shortages due to the great surges in the influx of infected COVID-19 patients and demand exceeding capacities. Predicting the lengths of stay (LOS) of the patients can help to make proper resource-planning decisions. CT-SS accurately determines the disease severity and could be considered an appropriate prognostic factor to predict patients’ LOS.

Objective: In this study, we evaluate the additive value of CT-SS in the prediction of hospital and ICU LOSs of COVID-19 patients.

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 MLP model with an accuracy of 96.7%, sensitivity of 100.0%, precision of 93.8%, specificity of 93.4%, and AUC of around 99.4% had the best performance among the other three ML techniques. This algorithm with 95.3% sensitivity, 86.2% specificity, 90.8% accuracy, 87.3% precision, 91.2% F-Measure, and an AUC of 95.8% 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.