AUTHOR=Hong Wandong , Zhou Xiaoying , Jin Shengchun , Lu Yajing , Pan Jingyi , Lin Qingyi , Yang Shaopeng , Xu Tingting , Basharat Zarrin , Zippi Maddalena , Fiorino Sirio , Tsukanov Vladislav , Stock Simon , Grottesi Alfonso , Chen Qin , Pan Jingye 

TITLE=A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile

JOURNAL=Frontiers in Cellular and Infection Microbiology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2022.819267

DOI=10.3389/fcimb.2022.819267

ISSN=2235-2988

ABSTRACT=Background & Aims:  The aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients. 
Methods: Clinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Outcomes were followed up until Mar 12, 2020. A logistic regression function (LR model), Random Forest, and XGBoost models were developed. The performance of these models was measured by area under receiver operating characteristic curve (AUC) analysis. 
Results: Univariate analysis revealed that there was difference between critically and non-critically ill patients with respect to levels of interleukin-6, interleukin-10, T-cells, CD4+ T, and CD8+ T cells. Interleukin-10 with an AUC of 0.86 was most useful predictor of critically ill patients with COVID-19 pneumonia. Ten variables (respiratory rate, neutrophil counts, aspartate transaminase, albumin, serum procalcitonin, D-dimer and B-type natriuretic peptide, CD4+ T cells, interleukin-6 and interleukin-10) were used as candidate predictors for LR model, Random Forest (RF) and XGBoost model application. The coefficients from LR model were utilized to build a nomogram. RF and XGBoost methods suggested that Interleukin-10 and interleukin-6 were the most important variables for severity of illness prediction. The mean AUC for LR, RF, and XGBoost model was 0.0.91, 0.89, and 0.93 respectively (in two-fold cross-validation).  Individualized prediction by XGBoost model was explained by local interpretable model-agnostic explanations (LIME) plot.
Conclusions: XGBoost exhibited the highest discriminatory performance for prediction of critically ill patients with COVID-19 pneumonia. It is inferred that the nomogram and visualized interpretation with LIME plot could be useful in the clinical setting. Additionally, interleukin-10 could serve as a useful predictor of critically ill patients with COVID-19 pneumonia.