Effet d'une stratégie multimodale pour la prévention de la grippe nosocomiale

A multimodal strategy to prevent nosocomial influenza was implemented in 2015-2016 in our institution. 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 face mask use. The main objective was to assess the effectiveness of the strategy for reducing the risk of nosocomial influenza. A retrospective study was conducted over 5 epidemic seasons (2014-2015 to 2018-2019) including all adult patients hospitalized with a positive influenza virological 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. A total of 1555 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 significant reduction of nosocomial influenza over the last 3 epidemic seasons compared to the 2014-2015 epidemic season (IRR=0.39, 95%CI=0.19–0.81 in 2016-2017; IRR=0.49, 95%CI=0.24–0.99 in 2017-2018; IRR=0.48, 95%CI=0.23–0.97 in 2018-2019). Our data mainly suggested that the application of the strategy with 5 modalities, including systematic face 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.Une stratégie multimodale de prévention de la grippe nosocomiale a été mise en place dans notre établissement en 2015-2016. Trois modalités concernaient toutes les unités : promotion de la vaccination pour les professionnels de santé, surveillance épidémiologique et communication. Les unités accueillant un grand nombre de patients grippés ont mis en place 2 autres modalités : amélioration des capacités diagnostiques et port du masque systématique. L’objectif principal était d’évaluer l’efficacité de la stratégie pour diminuer la survenue de la grippe nosocomiale. Une étude rétrospective portant sur 5 saisons épidémiques (2014-2015 à 2018-2019) et incluant tous les patients adultes hospitalisés avec un test virologique positif pour la grippe a été réalisée au CHU Grenoble Alpes. Le nombre hebdomadaire de cas nosocomiaux de grippe a été analysé par un modèle de Poisson ; des ratios d’incidence (RI) ont été estimés. Au total, 1555 hospitalisations ont été incluses. Il n’y avait pas de différence significative entre les 5 saisons épidémiques pour les unités ayant mis en place 3 modalités. Dans les unités ayant instauré 5 modalités, il y avait une réduction significative du risque de grippe nosocomiale sur les 3 dernières saisons épidémiques comparées à la saison 2014-2015 (RI=0.39, IC95%=0.19–0.81 en 2016-2017 ; RI=0.49, IC95%=0.24–0.99 en 2017-2018 ; RI=0.48, IC95%=0.23–0.97 en 2018-2019). L’application de la stratégie à 5 modalités, incluant port de masque systématique et diagnostic rapide, permettrait de réduire de moitié le risque de grippe nosocomiale. Des données médico-économiques sont nécessaires avant d’envisager l’application de ces mesures à plus large échelle

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