Early Prediction of the Carbapenem Resistance Gram-Negative Bacteria Carriage in Intensive Care Unit using Machine-Learning

Abstract Background: The prevention and control of carbapenem-resistance gram-negative bacteria (CR-GNB) is the difficulty and focus for clinicians in the intensive care unit (ICU). This study constructs a CR-GNB carriage prediction model in order to predict the CR-GNB incidence in the next week. Methods: We used our database to select patients with complete CR-GNB screening records and delete cases with positive culture at admission to ICU. We collected the data within a week before the cultures as the prediction data source, using the multivariate logistic regression and three machine learning algorithms to construct model. Then we choose the optimal model and verify the accuracy by daily predicted and recorded the occurrence of CR-GNB of all patients admitted for 4 months. Results: There are 1385 patients with positive CR-GNB cultures and 1535 negative patients in this study. Forty-five variables have statistically significant differences. We include the 17 variables in the multivariate logistic regression model and build three machine learning models for all variables. In terms of accuracy and the area under the receiver operating characteristic (AUROC) curve, the random forest is better than XGBoost, decision tree, and better than multivariate logistic regression model (accuracy: 84%>82%>81%>72%, AUROC: 0.9089>0.894≈0.8987>0.7845). In the 4-month prospective study, 81 cases were predicted to be positive in CR-GNB culture within 7 days, 146 cases were predicted to be negative, 86 cases were positive, and 120 cases were negative, with an overall accuracy of 84% and AUROC of 91.98%. Conclusions: Prediction models by machine learning can predict the occurrence of CR-GNB colonization or infection within a week period, and can real-time predict and guide medical staff to identify high-risk groups more accurately.