The impact of the COVID-19 pandemic on nosocomial infections: a retrospective analysis in a tertiary maternal and child healthcare hospital.

Objective: The constant changes in the control strategies of the Corona Virus Disease 2019 (COVID-19) pandemic have greatly affected the prevention and control of nosocomial infections (NIs). This study assessed the impact of these control strategies on the surveillance of NIs in a regional maternity hospital during the COVID-19 pandemic. Methods: This retrospective study compared the observation indicators of nosocomial infections and their changing trends in the hospital before and during the COVID-19 pandemic. Results: In total, 2,56,092 patients were admitted to the hospital during the study. During the COVID-19 pandemic, the main drug-resistant bacteria in hospitals were Escherichia coli, Streptococcus agalactiae, Staphylococcus aureus, Klebsiella pneumoniae, and Enterococcus faecalis. The detection rate of S. agalactiae increased annually, while that of E. faecalis remained the same. The detection rate of multidrug-resistant bacteria decreased during the pandemic (16.86 vs. 11.42%), especially that of CRKP (carbapenem-resistant Klebsiella pneumoniae 13.14 vs. 4.39, P < 0.001). The incidence of nosocomial infections in the pediatric surgery department decreased significantly (OR: 2.031, 95% CI: 1.405-2.934, P < 0.001). Regarding the source of infection, a significant reduction was observed in respiratory infections, followed by gastrointestinal infections. In the routine monitoring of the ICU, the incidence of central line-associated bloodstream infection (CLABSI) decreased significantly (9.4/1,000 catheter days vs. 2.2/1,000 catheter days, P < 0.001). Conclusion: The incidence of nosocomial infections was lower than that before the COVID-19 pandemic. The prevention and control measures for the COVID-19 pandemic have reduced the number of nosocomial infections, especially respiratory, gastrointestinal, and catheter-related infections.

HFCF-Net: A hybrid-feature cross fusion network for COVID-19 lesion segmentation from CT volumetric images.

BACKGROUND: The coronavirus disease 2019 (COVID-19) spreads rapidly across the globe, seriously threatening the health of people all over the world. To reduce the diagnostic pressure of front-line doctors, an accurate and automatic lesion segmentation method is highly desirable in clinic practice. PURPOSE: Many proposed two-dimensional (2D) methods for sliced-based lesion segmentation cannot take full advantage of spatial information in the three-dimensional (3D) volume data, resulting in limited segmentation performance. Three-dimensional methods can utilize the spatial information but suffer from long training time and slow convergence speed. To solve these problems, we propose an end-to-end hybrid-feature cross fusion network (HFCF-Net) to fuse the 2D and 3D features at three scales for the accurate segmentation of COVID-19 lesions. METHODS: The proposed HFCF-Net incorporates 2D and 3D subnets to extract features within and between slices effectively. Then the cross fusion module is designed to bridge 2D and 3D decoders at the same scale to fuse both types of features. The module consists of three cross fusion blocks, each of which contains a prior fusion path and a context fusion path to jointly learn better lesion representations. The former aims to explicitly provide the 3D subnet with lesion-related prior knowledge, and the latter utilizes the 3D context information as the attention guidance of the 2D subnet, which promotes the precise segmentation of the lesion regions. Furthermore, we explore an imbalance-robust adaptive learning loss function that includes image-level loss and pixel-level loss to tackle the problems caused by the apparent imbalance between the proportions of the lesion and non-lesion voxels, providing a learning strategy to dynamically adjust the learning focus between 2D and 3D branches during the training process for effective supervision. RESULT: Extensive experiments conducted on a publicly available dataset demonstrate that the proposed segmentation network significantly outperforms some state-of-the-art methods for the COVID-19 lesion segmentation, yielding a Dice similarity coefficient of 74.85%. The visual comparison of segmentation performance also proves the superiority of the proposed network in segmenting different-sized lesions. CONCLUSIONS: In this paper, we propose a novel HFCF-Net for rapid and accurate COVID-19 lesion segmentation from chest computed tomography volume data. It innovatively fuses hybrid features in a cross manner for lesion segmentation, aiming to utilize the advantages of 2D and 3D subnets to complement each other for enhancing the segmentation performance. Benefitting from the cross fusion mechanism, the proposed HFCF-Net can segment the lesions more accurately with the knowledge acquired from both subnets.