Early prediction of carbapenem-resistant Gram-negative bacterial carriage in intensive care units using machine learning

Objectives: This study constructed a carbapenem-resistant Gram-negative bacteria (CR-GNB) carriage prediction model to predict the CR-GNB incidence in a week.

Methods: We used our database to select patients with complete CR-GNB screening records between the years 2015 and 2019 and constructed the model using multivariable logistic regression and three machine learning algorithms. Then we chose the optimal model and verified the accuracy by daily prediction and recorded the occurrence of CR-GNB in all intensive care unit patients admitted for 4 months.

Results: There were 1385 patients with positive CR-GNB cultures and 1535 negative patients in this study. Forty-five variables had statistically significant differences. We included 16 variables in the multivariable logistic regression model and built three machine learning models for all variables. In terms of accuracy and the area under the receiver operating characteristic (AUROC) curve, random forest was better than XGBoost and decision tree and better than a multivariable logistic regression model (accuracy: 84%>82%>81%>72%, AUROC: 0.91>0.89=0.89>0.78). In a 4-month prospective study, 74 cases were predicted to have positive CR-GNB culture within 7 days, 132 cases were predicted to be negative, 86 cases were positive, and 120 cases were negative, with an overall accuracy of 85.92% and AUROC of 92.02%.

Conclusion: Machine learning prediction models can predict the occurrence of CR-GNB colonisation or infection within a one-week period and can guide medical staff in real time to identify high-risk groups more accurately.