IntroductionSince 2012, the European Centre for Disease Prevention and Control (ECDC) promotes a point prevalence survey (PPS) of HAIs in European acute care hospitals. Through a retrospective ...analysis of 2012, 2015 and 2017 PPS of HAIs performed in a tertiary academic hospital in Italy, we developed a model to predict the risk of HAI. MethodsFollowing ECDC protocol we surveyed 1382 patients across three years. Bivariate logistic regression analyses were conducted to assess the relationship between HAI and several variables. Those statistically significant were included in a stepwise multiple regression model. The goodness of fit of the latter model was assessed with the Hosmer-Lemeshow test, ultimately constructing a probability curve to estimate the risk of developing HAIs. ResultsThree variables resulted statistically significant in the stepwise logistic regression model: length of stay (OR 1.03; 95% CI: 1.02-1.05), devices breaking the skin (i.e. peripheral or central vascular catheter, OR 4.38; 95% CI: 1.52-12.63), urinary catheter (OR 4.71; 95% CI: 2.78-7.98). ConclusionPPSs are a convenient and reliable source of data to develop HAIs prediction models. The differences found between our results and previously published studies suggest the need of developing hospital-specific databases and predictive models for HAIs.
Skin Cleansing with chlorhexidine gluconate (CHG) is an evidence-based practice to prevent hospital acquired infections (HAI) by reducing colonization of skin organisms. The patient population in an ...oncology ICU is at an increased risk of developing a HAI due to their immunocompromised and critically ill state. During HAI event reviews, CHG skin cleansing was identified as one of the top trending opportunities in the ICU. The ICU team subsequently established a series of interventions to address compliance and prevent further HAIs. CHG is effective for up to 24 hours, so consistent application of the product is required for continuous skin protection. The ICU and Infection Prevention (IP) team prioritized CHG skin cleansing compliance due to the high-risk patient population. Together, a CHG compliance goal of 90% was predetermined. IP historically shared ICU's CHG skin cleansing compliance on a monthly cadence, with compliance averaging between 60-70%. The ICU leadership team collaborated with frontline staff to understand the current workflow, drill-down on opportunities, and address the barriers identified. Based on the feedback, the team implemented a dedicated CHG skin cleansing time that was feasible for staff workflow, prioritized refusal escalation, rebranded terminology to avoid patient confusion, provided patients and family members with educational materials, and delegated CHG skin cleansing to patient care assistants (PCAs). This was rolled out as the first PCA quality improvement project and there was complete buy-in from the ancillary staff. Additionally, ICU leaders worked with IP to increase the CHG data sharing to weekly instead of monthly, resulting in increased awareness and improved progress tracking. This allowed for timely staff education and improved accountability. ICU set a CHG skin cleansing goal of 90% in an effort to improve compliance and decrease HAIs. The PCA CHG skin cleansing project started on July 1, 2021 and resulted in a significant sustained improvement of CHG skin cleansing compliance ranging between 84-92%. With the increase, there has been an improvement in the following Nurse Sensitive Indicators: 100 days without a Central Line Blood Stream Infections, outperformance of the Hospital Acquired Pressure Injury NDNQI benchmark for the last 4 quarters, and outperformance of the Catheter Associated Urinary Tract Infection NDNQI benchmark for 3 of the last 4 quarters. This collaboration is being replicated in other unit throughout the organization.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, OILJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
Summary
Carbapenem is an important therapy for serious hospital‐acquired infections and for the care of patients affected by multidrug‐resistant organisms, specifically Acinetobacter baumannii; ...however, with the global increase of carbapenem‐resistant A. baumannii, this pathogen has significantly threatened public health. Thus, there is a pressing need to better understand this pathogen in order to develop novel treatments and control strategies for dealing with A. baumannii. In this review, we discuss an overview of carbapenem, including its discovery, development, classification and biological characteristics, and its importance in hospital medicine especially in critical care units. We also describe the peculiarity of bacterial pathogen, A. baumannii, including its commonly reported virulence factors, environmental persistence and carbapenem resistance mechanisms. In closing, we discuss various control strategies for overcoming carbapenem resistance in hospitals and for limiting outbreaks. With the appearance of strains that resist carbapenem, the aim of this review is to highlight the importance of understanding this increasingly problematic healthcare‐associated pathogen that creates significant concern in the field of nosocomial infections and overall public health.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Introduction: Our hospital has a C. diff Infection (CDI) Stool Collection Algorithm (figure 1) in place to promote effective testing. In the previous year, data showed that there was an increase in ...stool samples collected more than 24 hours after order was placed. This delay in collection is likely due to lack of watery bowel movements since the order was placed. Any positive results from these orders are likely false positive. This leads to unwarranted treatment and inaccurate labeling of a hospital acquired infection if positive more than 48 hours after admission. Methods: CDI Stool Collection Algorithm was posted at all nursing stations and educational sessions were held. Message explaining the collection algorithm including a request to reevaluate the necessity of the order was sent to providers of patients with a C. diff order uncollected for more than 24 hours. Results: Data was retrieved from the hospital database for the time period of this study and the corresponding time period last year for comparison. Last year, a total of 896 C. diff tests were ordered. This year, a total of 688 C. diff tests were ordered. This decrease is statistically significant with a p value of 0.002. Therefore, the interventions of implementing the collection algorithm led to more appropriate ordering (figure 3). Last year, the total number of C. diff orders collected after 24 hours was 84 and after the intervention this year, it was 34. The reduction was statistically significant with a p value of 0.001. This could be a reflection of the educational messaging system. (figure 2). The total number of positive results occurring from orders collected more than 24 hours from placement of order was 7 last year and it was 1 this year. There was no statistical difference between these numbers (p value = 0.15). The percentage was however reduced in half (8 to 4% of total results). Hospital acquired infection total number and rate were unchanged. Conclusion: This intervention may have led to more appropriate testing for CDIs through implementation of the CDI Algorithm and educational sessions. This was demonstrated in an overall decrease in orders. A reduction in collection of orders pending for 24 hours or more was achieved; through educational message system if deemed clinically appropriate. Despite this reduction, positive results from these collections were not reduced statistically and may not have led to improvement of unwarranted treatment of carrier patients.
PDF
