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

Characterizing the molecular epidemiology of anaesthesia work area transmission of Staphylococcus aureus sequence type 5

BACKGROUND

Staphylococcus aureus sequence type 5 (ST5) is an emerging global threat.

AIM

To characterize the epidemiology of ST5 transmission in the anaesthesia work area.

METHODS

The retrospective cohort study analysed transmitted, prophylactic antibiotic-resistant Staphylococcus aureus isolates involving anaesthesia work area reservoirs. Using whole-genome analysis, the epidemiology of ST5 transmission was characterized by reservoir(s) of origin, transmission location(s), portal of entry, and mode(s) of transmission. All patients were followed for at least 30 days for surgical site infection (SSI) development.

FINDINGS

Forty-one percent (18/44; 95% confidence interval: 28-56%) of isolates were ST5. Provider hands were the reservoir of origin for 28% (5/18) of transmitted ST5 vs 4% (1/26) for other STs. Provider hands were the transmission location for 28% (5/18) of ST5 vs 7% (2/26) of other STs. Stopcock contamination occurred for 8% (1/13) of ST5 isolates vs 12% (3/25) of other STs. Sixty-three percent of transmission events occurring between cases on separate operative dates involved ST5. ST5 was more likely to harbour resistance traits (ST5 median (interquartile range) 3 (2-3) vs 2 (1-2) other STs; P < 0.001) and had greater resistance to cefazolin, piperacillin-tazobactam, and/or ciprofloxacin (ST5: 3 (2-3) vs 2 (1-3) other STs; P = 0.02). ST5 was associated with three of six SSIs.

CONCLUSION

ST5 is prevalent among transmitted, prophylactic antibiotic-resistant isolates in the anaesthesia work area. Transmission involves provider hands and one patient to another on future date(s). ST5 is associated with a greater number of resistance traits and reduced in-vitro susceptibility vs other intraoperative meticillin-resistant S. aureus.

Quantifying and Interpreting Inequality in Surgical Site Infections per Quarter Among Anesthetizing Locations and Specialties

Background Earlier studies have shown that prevention of surgical site infection can achieve net cost savings when targeted to operating rooms with the most surgical site infections. Methodology This retrospective cohort study included all 231,057 anesthetics between May 2017 and June 2022 at a large teaching hospital. The anesthetics were administered in operating rooms, procedure rooms, radiology, and other sites. The 8,941 postoperative infections were identified from International Classification of Diseases diagnosis codes relevant to surgical site infections documented during all follow-up encounters over 90 days postoperatively. To quantify the inequality in the counts of infections among anesthetizing locations, the Gini index was used, with the Gini index being proportional to the sum of the absolute pairwise differences among anesthetizing locations in the counts of infections. Results The Gini index for infections among the 112 anesthetizing locations at the hospital was 0.64 (99% confidence interval = 0.56 to 0.71). The value of 0.64 is so large that, for comparison, it exceeds nearly all countries' Gini index for income inequality. The 50% of locations with the fewest infections accounted for 5% of infections. The 10% of locations with the most infections accounted for 40% of infections and 15% of anesthetics. Among the 57 operating room locations, there was no association between counts of cases and infections (Spearman correlation coefficient r = 0.01). Among the non-operating room locations (e.g., interventional radiology), there was a significant association (Spearman r = 0.79). Conclusions Targeting specific anesthetizing locations is important for the multiple interventions to reduce surgical site infections that represent fixed costs irrespective of the number of patients (e.g., specialized ventilatory systems and nightly ultraviolet-C disinfection).

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.