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Background: High hand hygiene (HH) workload is a commonly cited barrier to optimal HH performance. The objective of this study was to assess trends of HH workload as defined by HH opportunities (HHO) and performance rates over different timescales using automated HH monitoring system data., Methods: This multiyear retrospective observational study was conducted in 58 inpatient units located in 10 North American hospitals. HHO and HH rates were analyzed by time series mixed effects general additive model., Results: Median HH rates peaked at 50.0 between 6 and 7 AM with a trough of 38.2 at 5 PM. HHO over hours in a day were the highest at 184 per hospital unit per hour at 10 AM with a trough of 49.0 between 2 and 3 AM. Median rates for day and night shifts were 40.8 and 45.5, respectively (P = .078). Weekend day shift had the lowest median rate (39.4) compared with any other 12-hour shift (P < .1018). The median rates and HHO varied little across days in a week and months., Conclusions: HH workload and performance rates were negatively correlated and changed drastically over hours in a day. Hospitals should consider HH workload in the development and timely delivery of improvement interventions., (Copyright © 2024 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)

Thorium-227 was separated from its progeny and standardized for activity by the triple-to-double coincidence ratio (TDCR) method of liquid scintillation counting. Confirmatory liquid scintillation-based measurements were made using efficiency tracing with 3 H and live-timed anticoincidence counting (LTAC). The separation time and the efficiency of the separation were confirmed by gamma-ray spectrometry. Calibrations for reentrant pressurized ionization chambers, including commercial radionuclide calibrators, and a well-type NaI(Tl) detector are discussed., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:All authors reports financial support and equipment, drugs, or supplies were provided by Bayer, AS. Denis E. Bergeron is Editor-in-Chief of Applied Radiation and Isotopes. Brian E. Zimmerman is former Editor-in-Chief and a Consulting Editor for Applied Radiation and Isotopes., (Published by Elsevier Ltd.)

Objective: To determine how engagement of the hospital and/or vendor with performance improvement strategies combined with an automated hand hygiene monitoring system (AHHMS) influence hand hygiene (HH) performance rates., Design: Prospective, before-and-after, controlled observational study., Setting: The study was conducted in 58 adult and pediatric inpatient units located in 10 hospitals., Methods: HH performance rates were estimated using an AHHMS. Rates were expressed as the number of soap and alcohol-based hand rub portions dispensed divided by the number of room entries and exits. Each hospital self-assigned to one of the following intervention groups: AHHMS alone (control group), AHHMS plus clinician-based vendor support (vendor-only group), AHHMS plus hospital-led unit-based initiatives (hospital-only group), or AHHMS plus clinician-based vendor support and hospital-led unit-based initiatives (vendor-plus-hospital group). Each hospital unit produced 1–2 months of baseline HH performance data immediately after AHHMS installation before implementing initiatives., Results: Hospital units in the vendor-plus-hospital group had a statistically significant increase of at least 46% in HH performance compared with units in the other 3 groups ( P ≤ .006). Units in the hospital only group achieved a 1.3% increase in HH performance compared with units that had AHHMS alone ( P = .950). Units with AHHMS plus other initiatives each had a larger change in HH performance rates over their baseline than those in the AHHMS-alone group ( P < 0.001)., Conclusions: AHHMS combined with clinician-based vendor support and hospital-led unit-based initiatives resulted in the greatest improvements in HH performance. These results illustrate the value of a collaborative partnership between the hospital and the AHHMS vendor.

Background: Little is known about the prevalence of obstructive sleep apnea-hypopnea syndrome (OSAHS) in morbidly obese patients and whether such patients show peculiar clinical findings that may make it easier to suspect and diagnose OSAHS., Objectives: To investigate prevalence of OSAHS in patients with morbid obesity and find a simple structured model for predicting the results of polysomnography., Methods: The study enrolled a group of 101 consecutive inpatients (33 males, age range 20-80 years) with a body mass index > or =40, whose symptoms of OSAHS were not known, and a validation group of 45 patients., Results: Habitual snoring, nocturnal apneas or awakening as well as diurnal sleepiness were frequent findings (90.1, 40.6, 50.5 and 61.4%, respectively). Chronic obstructive pulmonary disease, hypertension, diabetes and myocardial ischemia were also frequently associated (22.8, 56.4, 30.7 and 6.9%, respectively). OSAHS was found in 61 (60.4%) patients, in 33.7% it was of severe degree. A multivariate logistic regression model allowed to select the independent predictors of OSAHS: age, male sex, diurnal sleepiness and the value of minimum nocturnal saturation. Sensitivity of 97%, specificity of 77% as well as positive and negative predictive values of 87% and 95%, respectively, were obtained; similar results were found in the validation group. When the best obtainable cutoff on the receiver operating characteristic curve is below 40%, the instrumental diagnosis might be excluded in as many as 33% of cases, since they are not affected by OSAHS or have OSAHS of mild degree., Conclusions: OSAHS is present in almost two thirds of morbidly obese patients. By applying the prediction model we propose, one may calculate the probability of a morbidly obese patient of being affected by OSAHS.