Daniele Rosadini

Time effectiveness of Ultraviolet C light (UVC) emitted by Light Emitting Diodes (LEDs) in reducing stethoscope contamination

Today it is well demonstrated that stethoscopes can be as contaminated as hands, which are a recognized source of Health-Care Associated Infections (HCAIs). Ultraviolet C (UVC) light has proven disinfection capacity and the innovative UVC technology of Light Emitting Diode (LED) shows several potential benefits. To verify whether the use of UVC LEDs is effective and reliable in stethoscope membrane disinfection after prolonged use, a pre-post intervention study was conducted. A total of 1668 five-minute cycles were performed on two UVC LEDs to simulate their use; thereafter, their disinfection capacity was tested on stethoscope membranes used on a previously auscultated volunteer. Then, a further 1249 cycles were run and finally the LEDs were tested to assess performance in reducing experimental contamination by Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli on the stethoscope membrane. Baseline volunteer contamination identified 104 Colony Forming Units (CFUs) while treated Petri dishes had 12 and 15 CFUs (p < 0.001). Statistically significant differences (p < 0.001) were also found relating to the reduction of specific bacteria: in particular, after treatment no CFU were observed for S. aureus and E. coli. UVC LEDs demonstrated the capacity to maintain high levels of disinfection after more than 240 h of use and they were effective against common microorganisms that are causative agents of HCAIs.

Risk adjusted mortality after hip replacement surgery. A retrospective study

INTRODUCTION Hip replacement (HR) operations are increasing. Short term mortality is an indicator of quality; few studies include risk adjustment models to predict HR outcomes. We evaluated in-hospital and 30-day mortality in hospitalized patients for HR and compared the performance of two risk adjustment algorithms. MATERIALS AND METHODS A retrospective cohort study on hospital discharge records of patients undergoing HR from 2000 to 2005 in Tuscany Region, Italy, applied All-Patient Refined Diagnosis Related Groups (APR-DRG) and Elixhauser Index (EI) risk adjustment models to predict outcomes. Logistic regression was used to analyse the performance of the two models; C statistic (C) was used to define their discriminating ability. RESULTS 25 850 hospital discharge records were studied. In-hospital and 30-day crude mortality were 1.3% and 3%, respectively. Female gender was a significant (p < 0.001) protective factor under both models and had the following Odds Ratios (OR): 0.64 for in-hospital and 0.51 for 30-day mortality using APR-DRG and 0.55 and 0.48, respectively, with EI. Among EI comorbidities, heart failure and liver disease were associated with in-hospital (OR 9.29 and 5.60; p < 0.001) and 30-day (OR 6.36 and 3.26; p < 0.001) mortality. Increasing age and APR-DRG risk class were predictive of all the outcomes. Discriminating ability for in-hospital and 30-day mortality was reasonable with EI (C 0.79 and 0.68) and good with APR-DRG (C 0.86 and 0.82). CONCLUSIONS Our study found that gender, age, EI comorbidities and APR-DRG risk of death are predictive factors of in-hospital and 30-day mortality outcomes in patients undergoing HR. At least one risk adjustment algorithm should always be implemented in patient management.