In this study, we build the prediction model of CR-GNB carriages within a week at admission by machine learning. We analyze to further verify the accuracy of the model through a 4-month prospective and consecutive predictions.
With the worldwide spread of CR-GNB in ICU, clinicians invest a lot of times and resources in nosocomial prevention and control measures, including colonization supervision, contact isolation, hand hygiene, antibiotic control and so on2, 6. However, in the face of heavy clinical work, the implementation rate of these measures has been criticized. The normalization of nosocomial prevention and control measures is not only the compliance of medical staff but also the problem of cost and benefits. Control the source of infection, cut off the route of transmission, and protect the vulnerable population are three classical pathways. Current research has found more and more dormant sources of infection, including fiberoptic bronchoscope, ICU flume17, 18. The route of transmission also has more possibility of analysis with the assistance of next-generation sequencing19. Many pieces of researches focus on Protecting susceptible people because of its simplicity and effectiveness5, 20, 21. Some studies pay attention to the identification of high-risk factors, which are determined by building models21–24. Thomas and his colleagues predicted the infection of MDR by multivariate logical regression model by the public database and found that the main risk factors of MDR infection were the high use of antibiotics previously, the site and degree of infection in the previous three months25. The researchers included a total of 120,000 samples, but there were a few variables and the models could only vaguely indicate which patients were likely to develop into the infections. Katherine and his teams used a multiple logical regression model and a decision tree model to assess the risk of extended Spectyum-β lactamase (ESBLs) bacteremia through the data of 1288 cases of gram-negative bacteremia. A total of 14 variables were included. The prediction effect of the decision tree and multivariate logistic regression model for predicting ESBL infection was similar21. However, the research sample size and variables are limited, and the incoordination between the number of positive cases (15%) and that of control cases (85%) restricted the performance of machine learning. Wang et al used 512 patients to predict MDR carriages and found that males, high CRP levels, and high Pitt scores were high-risk predictors, and a line chart was used to predict the occurrence of MDR24. In our study, invasive procedures include endotracheal intubation, intravenous intubation, drainage tube, and the hospital residence history over the past month are high-risk factors for CR-GNB carriages, which have been identified in other studies9, 22. According to the history of residence in other hospitals, our center adopts preemptive isolation and active surveillance which can reduce the incidence of carbapenem-resistant Enterobacteriaceae (CRE) 26. However, these kinds of literatures only provide information on which patients may develop MDR colonization or infection, but the exact time is unknown. As a result, the prevention and control of nosocomial infection measures are faced with the problems of when to implement and when to remove, as well as the cost-effectiveness, clinical burnout. Therefore, our center developed a CR-GNB prediction model in a week for ICU patients, which aims to carry out more targeted prevention including single isolation in advance, special management, and other measures. At present, this study has completed the verification of clinical applicability, and the subsequent clinical randomized controlled trial will be conducted to verify its clinical practicability.
We set one week as the forecast period in several aspects. First of all, too short or too long prediction periods will affect the performance of the prediction model. Secondly, ICU hospitalization as a high-risk factor for CR-GNB, the longer the ICU hospital stay, the higher the incidence of CR-GNB. According to the average hospitalization days, about 6 days in our center, most of the patients with hospitalization in a week are postoperative patients who are not the beneficiaries of this study. Lastly, according to the pre-experimental results, the peak time of CR-GNB carriages in our center was 5 days, then decreased slowly. One week as the prediction node can balance and the comparability in the trial group and the control group. Besides, sputum samples were still dominant in the colonization and infection of CR-GNB in this study, which was related to the types of diseases, including severe craniocerebral trauma and community-acquired pneumonia, which required long-term mechanical ventilatio. and the proportion of. These patients sent sputum samples for examination are higher than that of other parts and stay longer than the general postoperative patient trial. Therefore they are more likely to be included and caused bias in the study.
This study uses the central database, which has both advantages and disadvantages. On the one hand, the local database has with the diversified and integrated variables, which is difficult to achieve in the public database. The central database has been updated in real-time and provides the feasibility for continuous prediction. Also, the real-time updated database can continuously carry out iterative learning for the model to incorporate data and keep pace with the times. On the other hand, the public database has more data and complete diseases than the single-center database, based by stable models and multi-center researches. However, the problem is also very prominent. The variables are fixed and may simplify the number of variables to ensure the integrity, followed by a slow update and unable to achieve timely prediction. The number adopted in this study is limited, and as single-center research, follow-up promotion will take a long time.