An assessment of the impact of recommended anesthesia work area cleaning procedures on intraoperative SARS-CoV-2 contamination, a case-series analysis

Surface Cleaning with a Microfiber Cloth and Water followed by Ultraviolet-C Light Exposure Achieves Non-Inferior Disinfection of a Pathogenic Staphylococcus aureus Strain versus Use of Germicidal Wipes

BACKGROUND

We hypothesized that ultraviolet-C (UV-C) irradiation (Surfacide, Waukesha, WI) following use of microfiber cloths (Sanny Shop LLC, Longmont, CO) soaked in water would be noninferior to surface disinfection wipes containing a quaternary ammonium compound and alcohol (PDI Healthcare, Woodcliff Lake, NJ) for the pathogenic Staphylococcus aureus (S. aureus) sequence type 5 (ST5).

METHODS

This was a randomized laboratory study of disinfection approaches for S. aureus ST5. A total of 270 polycarbonate slides loaded with ST5 were prepared for the standard surface disinfection group (N=18) and water-soaked microfiber cloths and UV-C treatment group (N=144), along with positive and negative microbiological controls.

RESULTS

All 18 samples of S. aureus ST5 bacteria treated with standard chemical wipes showed complete disinfection (colony forming units (CFU) = 0). All 144 treatments with water-soaked microfiber wipes followed by UV-C exposure showed complete disinfection (CFU =0) regardless of soiling, height from the floor, or orientation to the emitters. The upper 95% exact one-sided confidence limit for any CFU >0 was 2.1%.

DISCUSSION

These data affirm our hypothesis that surface wiping with a damp cloth followed by triangular UV-C irradiation delivery is noninferior to surface disinfection for S. aureus ST5 using germicidal wipes, even when UV-C is compromised by height from the floor and orientation to the emitters and surface disinfection is targeted.

CONCLUSION

Removing bioburden with chemical-free microfiber cloths followed by triangular UV-C delivery is a noninferior strategy to targeted surface disinfection with chemical disinfecting wipes for the pathogenic S. aureus ST5 strain in the laboratory setting.

A threshold of 100 or more colony-forming units on the anesthesia machine predicts bacterial pathogen detection: a retrospective laboratory-based analysis

PURPOSE

Preventing the spread of pathogens in the anesthesia work area reduces surgical site infections. Improved cleaning reduces the percentage of anesthesia machine samples with ≥ 100 colony-forming units (CFU) per surface area sampled. Targeting a threshold of < 100 CFU when cleaning anesthesia machines may be associated with a lower prevalence of bacterial pathogens. We hypothesized that anesthesia work area reservoir samples returning < 100 CFU would have a low (< 5%) prevalence of pathogens.

METHODS

In this retrospective cohort study of bacterial count data from nine hospitals, obtained between 2017 and 2022, anesthesia attending and assistants' hands, patient skin sites (nares, axilla, and groin), and anesthesia machine (adjustable pressure-limiting valve and agent dials) reservoirs were sampled at case start and at case end. The patient intravenous stopcock set was sampled at case end. The isolation of ≥ 1 CFU of Staphylococcus aureus, methicillin-resistant Staphylococcus aureus, Enterococcus, vancomycin-resistant Enterococcus, gram-negative (i.e., Klebsiella, Acinetobacter, Pseudomonas, and Enterobacter spp.) or coagulase-negative Staphylococcus was compared for reservoir samples returning ≥ 100 CFU vs those returning < 100 CFU.

RESULTS

Bacterial pathogens were isolated from 24% (7,601/31,783) of reservoir samples, 93% (98/105) of operating rooms, and 83% (2,170/2,616) of cases. The ratio of total pathogen isolates to total CFU was < 0.0003%. Anesthesia machine reservoirs returned ≥ 100 CFU for 44% (2,262/5,150) of cases. Twenty-three percent of samples returning ≥ 100 CFU were positive for ≥ 1 bacterial pathogen (521/2,262; 99% lower confidence limit, 22%) vs 3% of samples returning < 100 CFU (96/2,888; 99% upper limit, 4%).

CONCLUSIONS

Anesthesia machine reservoir samples returning < 100 CFU were associated with negligible pathogen detection. This threshold can be used for assessment of terminal, routine, and between-case cleaning of the anesthesia machine and equipment. Such feedback may be useful because the 44% prevalence of ≥ 100 CFU was comparable to the 46% (25/54) reported earlier from an unrelated hospital.

Estimation of the contribution to intraoperative pathogen transmission from bacterial contamination of patient nose, patient groin and axilla, anesthesia practitioners' hands, anesthesia machine, and intravenous lumen

BACKGROUND

Earlier studies showed net cost saving from anesthesia practitioners' use of a bundle of infection prevention products, with feedback on monitored Staphylococcus aureus intraoperative transmission. ESKAPE pathogens also include Enterococcus and gram-negative pathogens: Klebsiella, Acinetobacter, Pseudomonas, and Enterobacter. We evaluated whether bacterial contamination of patient nose, patient groin and axilla, anesthesia practitioners' hands, anesthesia machine, and intravenous lumen all contribute meaningfully to ESKAPE pathogen transmission within anesthesia work areas.

METHODS

The retrospective cohort study used bacterial count data from nine hospitals, 43 months, and 448 ESKAPE pathogen transmission events within anesthesia areas of 86 operating rooms. Transmission was measured within and between pairs of successive surgical cases performed in the same operating room on the same day.

RESULTS

There were 203 transmission events with S. aureus, 72 with Enterococcus, and 173 with gram negatives. ESKAPE pathogens in the nose contributed to transmission for 50% (99% confidence limit ≥45%) of case pairs, on the groin or axilla for 54% (≥49%), on the hands for 53% (≥47%), on the anesthesia machine for 21% (≥17%), and in the intravenous lumen for 24% (≥20%). ESKAPE pathogens in the nose started a transmission pathway for 27% (≥22%) of case pairs, on the groin or axilla for 24% (≥19%), on the hands for 38% (≥33%), on the anesthesia machine for 11% (≥7.6%), and in the intravenous lumen for 8.0% (≥5.3%). All P ≤ 0.0022 compared with 5%.

CONCLUSIONS

To prevent intraoperative ESKAPE pathogen transmission, anesthesia practitioners would need to address all five categories of infection control approaches: nasal antisepsis (e.g., povidone-iodine applied the morning of surgery), skin antisepsis (e.g., chlorhexidine wipes), hand antisepsis with dispensers next to the patient, decontamination of the anesthesia machine before and during anesthetics, and disinfecting caps for needleless connectors, disinfecting port protectors, and disinfecting caps for open female Luer type connectors.

The importance of targeting intraoperative transmission of bacteria with antibiotic resistance and strain characteristics

BACKGROUND

Evidence-based intraoperative infection control measures can reduce Staphylococcus aureus transmission and infections. We aimed to determine whether transmitted S. aureus isolates were associated with increased risk of multidrug resistance and associated traits.

METHODS

S. aureus isolates obtained from intraoperative environmental, patient skin, and provider hand reservoirs among 274 operating room case pairs (1st and 2nd case of the day) across 3 major academic medical centers from March 2009 to February 2010 underwent systematic-phenotypic-genomic analysis to identify clonal transmission events. The association of clonal S. aureus transmission with multidrug resistance and resistance traits was investigated. Transmission dynamics were characterized.

RESULTS

Transmitted isolates (N=58) were associated with increased risk of multi-drug antibiotic resistance [33% (19/58) transmitted vs. 10% (12/115) other isolates, risk ratio 3.14, 99% CI 1.34-7.38, P=0.0006]. Transmission was associated with a significant increase in resistance traits including mecA [40% transmitted isolates vs. 17% other isolates, risk ratio 2.28, P=0.0026] and ant (6)-Ia [26% transmitted isolates vs. 9% other isolates, risk ratio 2.97, P=0.0050]. Provider hands were a frequent reservoir of origin, between-case a common mode of transmission, and patient skin and provider hands frequent transmission locations for multidrug resistant pathogens.

CONCLUSIONS

Intraoperative S. aureus transmission was associated with multidrug resistance and resistance traits. Proven infection control measures should be leveraged to target intraoperative transmission of multidrug resistant pathogens.

Average and longest expected treatment times for ultraviolet light disinfection of rooms

BACKGROUND

Planning Ultraviolet-C (UV-C) disinfection of operating rooms (ORs) is equivalent to scheduling brief OR cases. The study purpose was evaluation of methods for predicting surgical case duration applied to treatment times for ORs and hospital rooms.

METHODS

Data used were disinfection times with a 3-tower UV-C disinfection system in N=700 rooms each with ≥100 completed treatments.

RESULTS

The coefficient of variation of mean treatment duration among rooms was 19.6% (99% confidence interval [CI] 18.2%-21.0%); pooled mean 18.3 minutes among the 133,927 treatments. The 50^th percentile of coefficients of variation among treatments of the same room was 27.3% (CI 26.3%-28.4%), comparable to variabilities in durations of surgical procedures. The ratios of the 90^th percentile to mean differed among rooms. Log-normal distributions had poor fits for 33% of rooms. Combining results, we calculated 90% upper prediction limits for treatment times by room using a distribution-free method (e.g., third longest of preceding 29 durations). This approach was suitable because, once UV-C disinfection started, the median difference between the duration estimated by the system and actual time was 1 second.

CONCLUSIONS

Times for disinfection should be listed as treatment of a specific room (e.g., "UV-C main OR16"), not generically (e.g., "UV-C"). For estimating disinfection time after single surgical cases, use distribution-free upper prediction limits, because of considerable proportional variabilities in duration.

Statistical Design of Overnight Trials for the Evaluation of the Number of Operating Rooms That Can Be Disinfected by an Ultraviolet Light Disinfection Robotic System

Background and objective

The number of ultraviolet light disinfection robot systems that are needed for a facility’s surgical suite(s) and/or procedure suite(s) depends in part on how many rooms need to be disinfected overnight by each robot and how long this will take. The answer needs to be determined separately for each surgical and procedure suite because those variables vary both among facilities and among operating rooms or procedure rooms within facilities. In this study, we consider statistical designs to assess how many rooms a facility can reliably (≥90% chance) disinfect overnight using an ultraviolet light disinfection robot system.

Methods

We used 133,927 observed disinfection times from 700 rooms as a population from which repeated samples were drawn with replacement in Monte-Carlo simulations. We used eight-hour and 10-hour shift lengths being multiples of 40 hours for full-time hourly employees.

Results

One possible strategy that we examined was to estimate total disinfection times by estimating the mean for each room and then summing up the means. However, that did not correctly answer the question of how many rooms can reliably be available for the next day’s first case. Summing up a percentile (e.g., 90%) instead also was inaccurate, because the proper percentile depended on the number of rooms.

A suitable strategy is a brief trial (e.g., nine nights or 19 nights) with the endpoint being the daily number of rooms disinfected. Empirically, the smallest count of rooms disinfected among nine nights or the second smallest count among 19 nights are 10^th percentiles (i.e., ≈90% probability that at least that number of rooms can be disinfected in the future). The drawback is that while this approach gives the probability of a night with fewer rooms disinfected, it does not give information as to how many fewer rooms may either skip ultraviolet decontamination or start late the next workday because disinfection was not completed. Our simulations showed that there is a substantial probability (≥95%) of at most two rooms fewer or one room greater than the 10^th percentile with a nine-night trial and one room fewer or greater with a 19-night trial.

Conclusions

Because probability distributions of disinfection times are heterogeneous both among rooms and among treatments for the same room, each facility should plan to perform its own trial of nine nights or 19 nights. This will provide results that are within two rooms or one room of the correct answer in the long term. This information can be used when planning purchasing decisions, leasing, and technician staffing decisions.