Predictive Machine Learning Models and Survival Analysis for COVID-19 Prognosis Based on Hematochemical Parameters

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed the anamnestic data and laboratory parameters of 303 patients diagnosed with COVID-19 who were admitted to the Polyclinic Hospital of Bari during the first phase of the COVID-19 global pandemic. After the pre-processing phase, we performed a survival analysis with Kaplan–Meier curves and Cox Regression, with the aim to discover the most unfavorable predictors. The target outcomes were mortality or admission to the intensive care unit (ICU). Different machine learning models were also compared to realize a robust classifier relying on a low number of strongly significant factors to estimate the risk of death or admission to ICU. From the survival analysis, it emerged that the most significant laboratory parameters for both outcomes was C-reactive protein min; HR=17.963 (95% CI 6.548–49.277, p < 0.001) for death, HR=1.789 (95% CI 1.000–3.200, p = 0.050) for admission to ICU. The second most important parameter was Erythrocytes max; HR=1.765 (95% CI 1.141–2.729, p < 0.05) for death, HR=1.481 (95% CI 0.895–2.452, p = 0.127) for admission to ICU. The best model for predicting the risk of death was the decision tree, which resulted in ROC-AUC of 89.66%, whereas the best model for predicting the admission to ICU was support vector machine, which had ROC-AUC of 95.07%. The hematochemical predictors identified in this study can be utilized as a strong prognostic signature to characterize the severity of the disease in COVID-19 patients.

Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19

“…Comparing our model with other decision tree models, it can be observed that our model has an accuracy similar to that of Migriño (81.5%) [ 28 ], but lower than that of Altini et al [ 29 ] and Naseem et al [ 30 ], which were both higher than 85%. It is noteworthy that our model specificity was low when compared to other research [ 29 , 30 ]. On the other hand, the model has a 89,16% F-score, which is higher than another machine learning model that predicted outcomes in patients with COVID-19 [ 28 ].…”

“…A machine learning approach, the decision tree model, was used to analyze the clinical data of hospitalized COVID-19 patients to establish an efficient prognosis [ 29 ]. In this study, we used the clinical, demographic, and blood chemistry parameters of the patients in order to predict two possible outcomes: discharged alive, or transferred to ICU or death, whichever was first.…”

Exploring Machine Learning Strategies in COVID-19 Prognostic Modelling: A Systematic Analysis of Diagnosis, Classification and Outcome Prediction

“…Early identification of patients at higher risk for superinfections or other complications, before significant increases in CRP and LDH levels, could facilitate more efficient treatment strategies and improve patient outcomes. [66]. These models, particularly ensemble models, have demonstrated high accuracy and AUC values in predicting patient outcomes, such as recovery, ICU admission, and death.…”

Research progress of artificial intelligence technology in COVID-19

“…Numerous researchers have employed diverse predictive indicators in their investigation of COVID-19 to evaluate the prognosis of the disease's ultimate clinical outcome. These predictive indicators comprise blood samples, electrocardiograms, imaging data (CT, XCR), respiratory parameters, clinical symptoms, and other relevant information [28][29][30][31][32][33][34][35] . The concluding indicators of the study were also varied, with a primary focus on the severity of the illness, the risk of mortality, the actual mortality rate, the length of hospitalization, and other relevant factors.…”