Staphylococcus aureus screening and preoperative decolonisation with Mupirocin and Chlorhexidine to reduce the risk of surgical site infections in orthopaedic surgery: a pre-post study.

BACKGROUND: Nasal carriage of Staphylococcus aureus is a risk factor for surgical site infections (SSI) in orthopaedic surgery. The efficacy of decolonisation for S. aureus on reducing the risk of SSI is uncertain in this speciality. The objective was to evaluate the impact of a nasal screening strategy of S. aureus and targeted decolonisation on the risk of S. aureus SSI. METHODS: A retrospective pre-post and here-elsewhere study was conducted between January 2014 and June 2020 in 2 adult orthopaedic surgical sites (North and South) of a French university hospital. Decolonisation with Mupirocin and Chlorhexidine was conducted in S. aureus carriers starting February 2017 in the South site (intervention group). Scheduled surgical procedures for hip, knee arthroplasties, and osteosyntheses were included and monitored for one year. The rates of S. aureus SSI in the intervention group were compared to a historical control group (South site) and a North control group. The risk factors for S. aureus SSI were analysed by logistic regression. RESULTS: A total of 5,348 surgical procedures was included, 100 SSI of which 30 monomicrobial S. aureus SSI were identified. The preoperative screening result was available for 60% (1,382/2,305) of the intervention group patients. Among these screenings, 25.3% (349/1,382) were positive for S. aureus and the efficacy of the decolonisation was 91.6% (98/107). The rate of S. aureus SSI in the intervention group (0.3%, 7/2,305) was not significantly different from the historical control group (0.5%, 9/1926) but differed significantly from the North control group (1.3%, 14/1,117). After adjustment, the risk factors of S. aureus SSI occurrence were the body mass index (ORaper unit, 1.05; 95%CI, 1.0-1.1), the Charlson comorbidity index (ORaper point, 1.34; 95%CI, 1.0-1.8) and operative time (ORaper minute, 1.01; 95%CI, 1.00-1.02). Having benefited from S. aureus screening/decolonisation was a protective factor (ORa, 0.24; 95%CI, 0.08-0.73). CONCLUSIONS: Despite the low number of SSI, nasal screening and targeted decolonisation of S. aureus were associated with a reduction in S. aureus SSI.

Early picc-line infections in non-neutropenic patients are mainly due to E. coli suggesting that third-generation cephalosporin may be used as a first-line antibiotic therapy.

PURPOSE: To describe the rate of peripherally inserted central catheter (PICC) -associated bloodstream infections, and the pathogens involved. METHODS: We prospectively analyzed data collected from all adult patients with a PICC insertion in a hematology unit in a tertiary care center between January 1, 2017 and June 30, 2020. RESULTS: A total of 370 PICCs were inserted in 275 patients with hematological malignancies: 54 (15 %) confirmed cases of central-line associated bloodstream infection (CLABSI) were identified. Enterobacteria were the most frequent bacteria identified, involved in 35 % of CLABSIs. Group 1 enterobacteria bacteremia occurred a much shorter time after insertion (median time to CLABSI 16 days) than group 2 or group 3 enterobacteria (median time to CLABSI 64 days, p-value = 0.049). CONCLUSION: Among Gram-negative bacilli CLABSI among non-neutropenic patients, E. coli identification was the most frequent and occurred earlier after insertion, suggesting that third-generation cephalosporin may be used as a first-line antibiotic therapy for enterobacteria bacteremia among non-neutropenic patients.

Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016-2018.

PURPOSE: In-hospital health-related adverse events (HAEs) are a major concern for hospitals worldwide. In high-income countries, approximately 1 in 10 patients experience HAEs associated with their hospital stay. Estimating the risk of an HAE at the individual patient level as accurately as possible is one of the first steps towards improving patient outcomes. Risk assessment can enable healthcare providers to target resources to patients in greatest need through adaptations in processes and procedures. Electronic health data facilitates the application of machine-learning methods for risk analysis. We aim, first to reveal correlations between HAE occurrence and patients' characteristics and/or the procedures they undergo during their hospitalisation, and second, to build models that allow the early identification of patients at an elevated risk of HAE. PARTICIPANTS: 143 865 adult patients hospitalised at Grenoble Alpes University Hospital (France) between 1 January 2016 and 31 December 2018. FINDINGS TO DATE: In this set-up phase of the project, we describe the preconditions for big data analysis using machine-learning methods. We present an overview of the retrospective de-identified multisource data for a 2-year period extracted from the hospital's Clinical Data Warehouse, along with social determinants of health data from the National Institute of Statistics and Economic Studies, to be used in machine learning (artificial intelligence) training and validation. No supplementary information or evaluation on the part of medical staff will be required by the information system for risk assessment. FUTURE PLANS: We are using this data set to develop predictive models for several general HAEs including secondary intensive care admission, prolonged hospital stay, 7-day and 30-day re-hospitalisation, nosocomial bacterial infection, hospital-acquired venous thromboembolism, and in-hospital mortality.

Towards real-time monitoring of COVID-19 nosocomial clusters using SARS-CoV-2 genomes in a university hospital of the French Alps.

OBJECTIVES: Experience of Nextstrain [1,2] and its approach adapted to the local context encouraged us to carry out real-time monitoring of COVID-19 nosocomial clusters in our establishment, the Grenoble Alpes University Hospital. PATIENTS AND METHODS, RESULTS: Through identification from electronic health records of nosocomial pathways and clusters and calculation of genetic distances from sequenced samples of COVID-19 patients, we were able to identify potential nosocomial clusters in very close to real time with a significant time saving compared to classical epidemiological surveillance, and to better understand and characterize nosocomial clusters. CONCLUSION: Through early detection and characterization of clusters, we may prevent infection of our patients by further implementing the appropriate measures.

Effect of a multimodal strategy for prevention of nosocomial influenza: a retrospective study at Grenoble Alpes University Hospital from 2014 to 2019.

BACKGROUND: A multimodal strategy to prevent nosocomial influenza was implemented in 2015-2016 in Grenoble Alpes University Hospital. Three modalities were implemented in all units: promotion of vaccination among healthcare workers, epidemiologic surveillance and communication campaigns. Units receiving a high number of patients with influenza implemented 2 additional modalities: improvement of diagnosis capacities and systematic surgical mask use. The main objective was to assess the effectiveness of the strategy for reducing the risk of nosocomial influenza. METHODS: A study was conducted retrospectively investigating 5 epidemic seasons (2014-2015 to 2018-2019) including all patients hospitalized with a positive influenza test at Grenoble Alpes University Hospital. The weekly number of nosocomial influenza cases was analyzed by Poisson regression and incidence rate ratios (IRR) were estimated. RESULTS: A total of 1540 patients, resulting in 1559 stays, were included. There was no significant difference between the 5 influenza epidemic seasons in the units implementing only 3 measures. In the units implementing the 5 measures, there was a reduction of nosocomial influenza over the seasons when the strategy was implemented compared to the 2014-2015 epidemic season (IRR = 0.56, 95% CI = 0.23-1.34 in 2015-2016; IRR = 0.39, 95% CI = 0.19-0.81 in 2016-2017; IRR = 0.50, 95% CI = 0.24-1.03 in 2017-2018; IRR = 0.48, 95% CI = 0.23-0.97 in 2018-2019). CONCLUSIONS: Our data mainly suggested that the application of the strategy with 5 modalities, including systematic surgical mask use and rapid diagnosis, seemed to reduce by half the risk of nosocomial influenza. Further data, including medico-economic studies, are necessary to determine the opportunity of extending these measures at a larger scale.

Is it possible to hospitalize patients in multiple-bed room without increasing the risk of hospital-acquired influenza? Description of a pragmatic preventive strategy in a French university hospital.

BACKGROUND: Large inrush of patients through Emergency Department during influenza season can be dramatic. The purpose of this study was to evaluate the impact of an emergency preventive strategy, namely admission of patients with influenza in multiple-bed room with patients free from influenza, on the occurrence of hospital-acquired influenza (HAI). METHODS: When a patient with an influenza RT-PCR diagnosis was hospitalized in a multiple-bed room, the emergency preventive strategy was applied: selection of non-immunocompromised neighbor, implementation of physical barriers (rigid screen pulled between beds, surgical mask for healthcare workers and visitors), preemptive Oseltamivir therapy for the neighbor. RESULTS: From 29/11/2017 to 10/05/2018 a total of 464 hospitalized influenza patients were included; 318 were placed in multiple-bed room and 141 in single room. Emergency preventive strategy was correctly applied for 75.1% of patients in multiple-bed room. A total of 8 exposed neighbors matched HAI definition despite strategy. 7 were already exposed to the case before the set-up of the strategy. Only one case of documented transmission of influenza occurred after application of an incorrect emergency preventive strategy: preventive posology of Oseltamivir was not correct. CONCLUSIONS: These preliminary results suggest that the occurrence of HAI in multiple-bed rooms can be limited by the implementation of maximum precautions and urge us to promote further evaluation of the strategy. A detection bias should be considered without a systematic neighbors monitoring.

Description of an influenza outbreak in a French university hospital and risk factors of nosocomial influenza.

Our objective was to evaluate risk factors of nosocomial influenza (NI) in an university hospital during the 2015/2016 influenza season. All hospitalized patients with influenza-like illness associated with laboratory confirmation by polymerase chain reaction were included in a prospective observational study. We identified 44 cases (19%) of NI among the 233 cases of influenza: 38/178 (21%) in adults and 6/55 (11%) in children. Among adults, hospitalization in a double or multi-occupancy room was independently associated with NI (adjusted Odds Ratio, 3.42; 95% CI, 1.29-9.08; p = 0.013). The results of the study underline the importance of single room to prevent NI.