Machine Learning-Based Prediction of In-Hospital Mortality in Severe COVID-19 Patients Using Hematological Markers

Background: The mortality rate is very high in patients with severe COVID-19. Nearly 32% of COVID-19 patients are critically ill, with mortality rates ranging from 8.1% to 33%. Early risk factor detection makes it easier to get the right care and estimate the prognosis. This study aimed to develop and validate a model to predict the risk of mortality based on hematological parameters at hospital admission in patients with severe COVID-19. Methods: The study retrospectively collected clinical data and laboratory test results from 396 and 112 patients with severe COVID-19 in two tertiary care hospitals as Cohort 1 and Cohort 2, respectively. Cohort 1 was to train the model. The LASSO method was used to screen features. The models built by nine machine learning algorithms were compared to screen the best algorithm and model. The model was visualized using nomogram, followed by trend analyses, and finally subgroup analyses. Cohort 2 was for external validation. Results: In Cohort 1, the model developed by the LR algorithm performed the best, with an AUC of 0.852 (95% CI: 0.750-0.953). Five features were included in the model, namely, D-dimer, platelets, neutrophil count, lymphocyte count, and activated partial thromboplastin time. The mode had higher diagnostic accuracy in patients with severe COVID-19 > 65 years of age (AUC = 0.814), slightly lower than in patients with severe COVID-19 ≤ 65 years of age (AUC = 0.875). The ability of the model to predict the occurrence of mortality was validated in Cohort 2 (AUC = 0.841). Conclusions: The risk prediction model for mortality for patients with severe COVID-19 was constructed by the LR algorithm using only hematological parameters in this study. The model contributes to the timely and accurate stratification and management of patients with severe COVID-19.