Background: Nosocomial infections caused by carbapenemase-producing Enterobacterieceae (CPE) constitute a major global health concern and are associated with increased morbidity and mortality. Rectal colonization with CPE is a risk factor for bacterial translocation leading to subsequent endogenous CPE infections. This prospective observational study was aimed to investigate the prevalence and epidemiology of rectal colonization of CPE, the carbapenemase genotypes, and to identify the independent risk factors for the acquisition of CPE colonization in high-risk patients from ICU and HSCT wards in a university hospital in China. Methods: In a prospective cohort study, 150 fecal samples from rectal swabs were consecutively obtained for inpatients from the ICU and HSCT wards from November 2018 to May 2019, and screening test for CPE was conducted by using home-made trypsin soybean broth (TSB) selective media and MacConkey agar. Antimicrobial susceptibility was determined by the broth microdilution method and carbapenemase genes were characterized by both the GeneXpert Carba-R and PCR for blaKPC, blaNDM, blaIMP, blaVIM and blaOXA. In order to further investigate the risk factors and clinical outcomes of CPE colonization, a prospective case-control study was also performed.Results: 26 suspected CPE strains, including 17 Klebsiella pneumoniae, 6 Escherichia coli, 1 Citrobacter freundii, 1 Enterobacter Kobe, and 1 Raoultella ornithinolytica, were identified in 25 non-duplicated fecal samples from 25 patients, with a carriage rate of 16.67% (25/150). Through GeneXpert Carba-R and subsequent PCR and sequencing, all the suspected CPE isolates were identified to be positive for the carbapenemase genes, of which 17 were blaKPC-carriers, and another 9 were blaNDM-producers. Multivariate analysis indicated that urinary system diseases, operation of bronchoscopy, and combined use of antibiotics were independent risk factors for acquiring CPE colonization in high-risk patients from the ICU and HSCT wards. Conclusions: This study revealed a high prevalence of rectal CPE colonization in high-risk patients from ICU and HSCT wards, and a predominant colonization of the KPC-producing K. pneumoniae strains. Stricter infection control measures are urgently needed to limit the dissemination of CPE strains, especially in patients who were afflicted by urinary system diseases, have underwent bronchoscopy, and were previously exposed to combined antibiotic use.

Rapid and early identification of pathogens is critical to guide antibiotic therapy. Raman spectroscopy as a noninvasive diagnostic technique provides rapid and accurate detection of pathogens. Raman spectrum of single cells serves as the “fingerprint” of the cell, revealing its metabolic characteristics. Rapid identification of pathogens can be achieved by combining Raman spectroscopy and deep learning. Traditional classification techniques frequently require lots of data for training, which is time costing to collect Raman spectra. For trace samples and strains that are difficult to culture, it is difficult to provide an accurate classification model. In order to reduce the number of samples collected and improve the accuracy of the classification model, a new pathogen detection method integrating Raman spectroscopy, variational auto‐encoder (VAE), and long short‐term memory network (LSTM) is proposed in this paper. We collect the Raman signals of pathogens and input them to VAE for training. VAE will generate a large number of Raman spectral data that cannot be distinguished from the real spectrum, and the signal‐to‐noise ratio is higher than that of the real spectrum. These spectra are input into the LSTM together with the real spectrum for training, and a good classification model is obtained. The results of the experiments reveal that this method not only improves the average accuracy of pathogen classification to 96.9% but also reduces the number of Raman spectra collected from 1000 to 200. With this technology, the number of Raman spectra collected can be greatly reduced, so that strains that are difficult to culture or trace can be rapidly identified.