Seering, Melinda S

BACKGROUND: Improved cleaning in the anesthesia workspace is important for surgical site infection prevention. We sought to identify high-priority cleaning targets to guide post-induction surface disinfection and use of a triangular ultraviolet-C (UV-C) configuration involving placement of one emitter at the head and one on each side of the surgical bed. METHODS: The 20 sites most frequently touched by anesthesia practitioners, previously identified via video-based workflow analysis, were filtered to remove disposable (eg, stopcocks) and transient (eg, the patient bed) sites, leaving durable cleaning targets amenable to repeated surface disinfection and UV-C irradiation within and/or between cases. Inequality in the distribution of cleaning target touches was quantified using the Gini coefficient, and uncertainty was estimated using bootstrap resampling (10,000 iterations). We then assessed residual contamination after standard terminal cleaning across three operating rooms and 171 sites to evaluate the difficulty of cleaning high-touch surfaces and the efficacy of UV-C disinfection. Simulations using medical devices composed of materials representative of cleaning targets contaminated with high-level Clostridium difficile were conducted to evaluate generalizability to more resistant pathogens. RESULTS: There were 13 of 20 frequently touched sites that were considered durable cleaning targets. Touches were relatively equal among the 13 cleaning targets (Gini coefficient 0.16, 95% confidence interval [CI], 0.12–0.20). Cleaning targets included components of the anesthesia cart (first and second drawer handles); anesthesia machine (reservoir bag, circuit ventilator control, oxygen dial, vaporizer dial, adjustable pressure-limiting valve), monitoring equipment (screen buttons/knob, electrocardiogram leads); and room infrastructure (intravenous pole, operating room table and chair). Residual contamination after cleaning was identified at 4% (7/171) of sites, mostly (6/7) within the anesthesia workspace, involving the anesthesia cart (N = 2), machine (N = 3), and monitor (N = 1). Only one site outside of the anesthesia workspace, the circulating nurse’s mouse, had residual contamination. Triangular UV-C reduced mean (standard deviation [SD]) colony-forming units (CFU) from 60.47 (542.22) to 0.12 (1.08), achieving a 2.7-log reduction. Simulation with high-level C. difficile contamination demonstrated ≥3-log reductions on surfaces representative of high-touch sites, confirming applicability to resistant pathogens. CONCLUSIONS: Thirteen durable cleaning targets in the anesthesia workspace are largely equivalent in terms of risk of touch. They are amenable to UV-C augmentation of surface disinfection in the real-world clinical environment. These findings can guide targeted post-induction cleaning and use of triangular UV-C, particularly for surfaces with substantial contamination or resistant pathogens.

Background: Obstructive Sleep Apnea (OSA) is a common occurrence in the perioperative patient population but is often undiagnosed. Point-of-Care Ultrasound (POCUS) has emerged as a promising tool for perioperative assessment; however, its effectiveness in detecting the presence or severity of OSA needs to be evaluated. Objective: We assessed the ability of airway POCUS as a screening and severity evaluation tool for OSA by examining its correlation with STOP-BANG scores and the Apnea–Hypopnea Index (AHI). Design: Cross-sectional observational study. Setting: A single-center study in a tertiary care hospital between June 2020 to May 2021. Patients: Adult patients aged 18–65 with prior Polysomnography (PSG) for OSA workup were screened. Interventions: The participants completed the STOP-BANG questionnaire and subsequently underwent POCUS examinations, either pre- or post-surgery. Ten different POCUS views previously used for evaluating OSA were acquired in a predefined sequence, with subsequent measurements of airway parameters. Outcome measures: Generalized linear modeling was used to explore and assess the relationships between the measured parameters, STOP-BANG, and AHI scores (modeled continuously and categorized into risk levels of STOP-BANG and AHI). Results: A total of 260 patients were screened, of which 142 were enrolled and 127 completed the scanning studies. The median AHI was 16.71, while the STOP-BANG scores were mostly between 5 and 6, indicating a moderate-to-high OSA risk in the study population. Notably, only neck circumference was significantly associated with AHI severity (p = 0.012), whereas none of the other POCUS measures were. Among the POCUS measures, significant associations with STOP-BANG scores were observed for the Tongue Cross-Sectional Area (T-CSA) (p = 0.002), Retro-Palatal Diameter (RPD) (p = 0.034), Distance Between Lingual Arteries (DLA) (p = 0.034), and Geniohyoid Muscle Thickness (GMT) (p = 0.040). Conclusions: Neck circumference is a more reliable predictor of OSA severity (AHI) compared to other POCUS measurements. Many of the POCUS measures had a good correlation with the STOP-BANG scores, highlighting the utility of POCUS as a screening tool for OSA rather than as a severity evaluation tool.