Gold standard evaluation of an automatic HAIs surveillance system

Hospital-acquired Infections (HAIs) surveillance, defined as the systematic collection of data related to a certain health event, is considered an essential dimension for a prevention HAI program to be effective. In recent years, new automated HAI surveillance methods have emerged with the wide adoption of electronic health records (EHR). Here we present the validation results against the gold standard of HAIs diagnosis of the InNoCBR system deployed in the Ourense University Hospital Complex (Spain). Acting as a totally autonomous system, InNoCBR achieves a HAI sensitivity of 70.83% and a specificity of 97.76%, with a positive predictive value of 77.24%. The kappa index for infection type classification is 0.67. Sensitivity varies depending on infection type, where bloodstream infection attains the best value (93.33%), whereas the respiratory infection could be improved the most (53.33%). Working as a semi-automatic system, InNoCBR reaches a high level of sensitivity (81.73%), specificity (99.47%), and a meritorious positive predictive value (94.33%).Xunta de Galicia | Ref. ED431C2018/55-GR

Effects of the COVID-19 Pandemic on Incidence and Epidemiology of Catheter-Related Bacteremia, Spain

We compared hospital-acquired catheter-related bacte-remia (CRB) episodes diagnosed at acute care hospitals in Catalonia, Spain, during the COVID-19 pandemic in 2020 with those detected during 2007-2019. We com-pared the annual observed and predicted CRB rates by using the negative binomial regression model and calcu-lated stratified annual root mean squared errors. A total of 10,030 episodes were diagnosed during 2007-2020. During 2020, the observed CRB incidence rate was 0.29/103 patient-days, whereas the predicted CRB rate was 0.14/103 patient-days. The root mean squared er-ror was 0.153. Thus, a substantial increase in hospital-acquired CRB cases was observed during the COVID-19 pandemic in 2020 compared with the rate predicted from 2007-2019. The incidence rate was expected to increase by 1.07 (95% CI 1-1.15) for every 1,000 COVID-19-re-lated hospital admissions. We recommend maintaining all CRB prevention efforts regardless of the coexistence of other challenges, such as the COVID-19 pandemic

Prevention and Reduction of Catheter-Associated Urinary Tract Infection

Abstract Problem: Fifteen to twenty-five percent of hospitalized patients receive indwelling urinary catheters (IUCs) during their hospital stay and are at high risk for catheter-associated urinary tract infections, according to the Centers for Disease Control and Prevention (CDC, 2015). Context: A CAUTI Prevention program was implemented in a community hospital surgical unit to address concerns of high rates of CAUTI. Interventions: Staff training/education, pre-CAUTI and post-CAUTI prevention survey, hand hygiene, bundle care audit, and staff competency for indwelling urinary catheter insertion were implemented to prevent and reduce CAUTI cases in the surgical unit. Measures: The Standardized Infection Ratio (SIR) formula, pre-CAUTI, and post-CAUTI prevention survey, and annual competency were the process measures used to evaluate the success of this project. Results: The 2 and 3 Surgical units had no CAUTI cases as of July 2023. Hand hygiene compliance rate and bundle care audit were improved. Conclusions: The quality improvement project reevaluated the organization’s current nurse-driven indwelling urinary catheter protocol and implemented several interventions to prevent and reduce CAUTI. The 5 P’s framework used to assess microsystems consists of purpose/policy, patients/participants, professionals, processes, and patterns (Nelson et al., 2007). PDSA cycle (Plan-Do-Study-Act) and Six Sigma were utilized to implement plan of care. Sustainability cycles were created to maintain the quality project plan of care. Keywords: quality improvement, CAUTI bundle care, catheter-associated urinary tract infections, healthcare-associated infections

Mathematical Models of Infection Prevention Programs in Hospital Settings

Hospitals play a vital role in providing for the healthcare needs of a community. Patients can develop hospital-acquired infections (HAIs) during their hospitalization due to exposure to foreign bacteria, viruses, and fungi. Infection prevention programs target and reduce HAIs, but implementing the infection prevention programs often comes with a cost. The goal of my research is to use mathematical models to quantify the impact of infection prevention programs on cases of HAIs and total healthcare costs. First, I use a Markov chain model to quantify how one infection prevention program reduces general HAIs in the hospital. Then, I calculate the impact of resistance by healthcare leaders to implement two infection prevention techniques on two HAIs in the hospital. I used ordinary differential equations to quantify the timing of initiation and termination of two infection prevention programs within a region divided into two components to understand how a community intervention and a localized intervention affect the peak number of infections in an epidemic. Finally, I used an agent-based model to quantify the impact of one specific infection prevention program on one HAI in one ward within the hospital. Overall, my research supports implementing the specific infection prevention programs examined to reduce the burden on healthcare systems and improve patient outcomes

Ten golden rules for optimal antibiotic use in hospital settings : the WARNING call to action

Publisher Copyright: © 2023. BioMed Central Ltd., part of Springer Nature.Antibiotics are recognized widely for their benefits when used appropriately. However, they are often used inappropriately despite the importance of responsible use within good clinical practice. Effective antibiotic treatment is an essential component of universal healthcare, and it is a global responsibility to ensure appropriate use. Currently, pharmaceutical companies have little incentive to develop new antibiotics due to scientific, regulatory, and financial barriers, further emphasizing the importance of appropriate antibiotic use. To address this issue, the Global Alliance for Infections in Surgery established an international multidisciplinary task force of 295 experts from 115 countries with different backgrounds. The task force developed a position statement called WARNING (Worldwide Antimicrobial Resistance National/International Network Group) aimed at raising awareness of antimicrobial resistance and improving antibiotic prescribing practices worldwide. The statement outlined is 10 axioms, or "golden rules," for the appropriate use of antibiotics that all healthcare workers should consistently adhere in clinical practice.Peer reviewe

Infectious hospital agents: A HAI spreading simulation framework

Infectious Hospital Agents (IHA) is an individual-based simulation framework that is able to model wide range of infection spreading scenarios in the hospital environment. The simulations are agent-based simulations driven by stochastic events, the evolution of the model is tracked in discrete time. Our aim was to build a general, customisable and extensible simulation environment for the domain of Hospital-Associated Infections (HAIs). The system is designed in Object Oriented fashion, and the implementation is in C++. In this paper, the authors describe the motivations and the background of the framework, sketch the conceptual framework, and present a demonstration example. © 2017, Budapest Tech Polytechnical Institution. All rights reserved

Ten golden rules for optimal antibiotic use in hospital settings:the WARNING call to action

Antibiotics are recognized widely for their benefits when used appropriately. However, they are often used inappropriately despite the importance of responsible use within good clinical practice. Effective antibiotic treatment is an essential component of universal healthcare, and it is a global responsibility to ensure appropriate use. Currently, pharmaceutical companies have little incentive to develop new antibiotics due to scientific, regulatory, and financial barriers, further emphasizing the importance of appropriate antibiotic use. To address this issue, the Global Alliance for Infections in Surgery established an international multidisciplinary task force of 295 experts from 115 countries with different backgrounds. The task force developed a position statement called WARNING (Worldwide Antimicrobial Resistance National/International Network Group) aimed at raising awareness of antimicrobial resistance and improving antibiotic prescribing practices worldwide. The statement outlined is 10 axioms, or "golden rules," for the appropriate use of antibiotics that all healthcare workers should consistently adhere in clinical practice.</p

A Novel Deep Learning, Camera, and Sensorbased System for Enforcing Hand Hygiene Compliance in Healthcare Facilities

Hospital-acquired infections are a major cause of death worldwide, and poor hand hygiene compliance is a primary reason for their spread. This paper proposes an artificial intelligence, microcontroller, and sensor-based system that monitors and improves staff hand hygiene compliance at various critical points in a hospital. The system uses a Convolutional Neural Network (CNN) to detect and track if staff have followed the WHO hand rub/hand wash guidelines at alcohol dispensers, hospital sinks, and patient beds. The system also uses RFID tags, vibration motors, LEDs, and a central server to identify staff, alert them of their cleaning requirements, monitor their cleaning activity, and report compliance data. We obtain an accuracy of 90.6% in classifying all steps of the WHO-stipulated hand wash/hand rub guidelines and a testing accuracy of 89.8% on Ivanovs et al.’s dataset. The system ensures that hospital staff stay compliant to all WHO hand hygiene guidelines, saving countless lives

Application of agent-based simulation to the modelling and management of hospital-acquired infections

Hospital-acquired infections (HAIs) are a big threat to the well-being of patients and place a heavy burden on hospital resources. The thesis provides the first attempt to apply agent-based simulation (ABS) to describe the transmission dynamics and evaluate the intervention policies of HAIs in general and Methicillin-resistant Staphylococcus aureus (MRSA) in particular. Based on the proposed taxonomy of potential methods for modelling HAIs, the relative advantages of ABS compared to other modelling methods are investigated. The comparison provides a theoretical justification to the use of ABS. The main methodological issues, including the representation of patient agents and the modelling of the transmission process, are discussed and a framework of applying ABS on HAI modelling is proposed. Guided by the framework, a MRSA model is built and validated using observed data from an empirical study. The model is more realistic and flexible than previous MRSA models and embeds intervention policies that have not been systematically studied such as the turnaround time and frequency of screening tests and the decolonisation treatment. Various interventions and influencing factors are systematically evaluated by formal experimental design methods including the fractional factorial design and the response surface design. The experimental results indicate that the use of rapid screening tests with shorter test turnaround time is the most effective policy to reduce MRSA transmission in the hospital setting. The introduction of admission and repeat screening is another effective policy; however, the effectiveness is not linear and may depend on patients’ lengths of stay. Providing more isolation facilities is also an effective policy but its effectiveness is significantly dependent on the efficacy of isolation. To demonstrate the potential and flexibility of ABS, the MRSA model is extended to include a competitive infection, to include multiple hospital units and HCW agents, and the wider community