Sample sizes for surveillance of S. aureus transmission to monitor effectiveness and provide feedback on intraoperative infection control including for COVID-19

Epidemiology of Enterococcus , Staphylococcus aureus , Klebsiella , Acinetobacter , Pseudomonas , and Enterobacter Species Transmission in the Pediatric Anesthesia Work Area Environment With and Without Practitioner Use of a Personalized Body-Worn Alcohol Dispenser

BACKGROUND

Personalized body-worn alcohol dispensers may serve as an important tool for perioperative infection control, but the impact of these devices on the epidemiology of transmission of high-risk Enterococcus , Staphylococcus aureus , Klebsiella, Acinetobacter , Pseudomonas , and Enterobacter (ESKAPE) pathogens is unknown. We aimed to characterize the epidemiology of ESKAPE transmission in the pediatric anesthesia work area environment with and without a personalized body-worn alcohol dispenser.

METHODS

This controlled before and after study included 40 pediatric patients enrolled over a 1-year study period. Two groups of operating room cases were compared: (1) operating room cases caring for patients with usual care (December 17, 2019, to August 25, 2020), and (2) operating room cases caring for patients with usual care plus the addition of a personalized, body-worn alcohol hand rub dispenser (September 30, 2020, to December 16, 2020). Operating rooms were randomly selected for observation of ESKAPE transmission in both groups. Device use was tracked via wireless technology and recorded in hourly hand decontamination events.

RESULTS

Anesthesia providers used the alcohol dispenser 3.3 ± 2.1 times per hour. A total of 57 ESKAPE transmission events (29 treatment and 28 control) were identified. The personalized body-worn alcohol dispenser impacted ESKAPE transmission by increasing the contribution of provider hand contamination at case start (21/29 device versus 10/28 usual care; relative risk, [RR] 2.03; 99.17% confidence interval [CI], 1.025-5.27; P = .0066) and decreasing the contribution of environmental contamination at case end (3/29 device versus 12/28 usual care; RR, 0.24; 99.17% CI, 0.022-0.947; P = .0059). ESKAPE pathogen contamination involved 20% (8/40) of patient intravascular devices. There were 85% (34/40) of preoperative patient skin surfaces contaminated with ≥1 (1.78 ± 0.19 [standard deviation {SD}]) ESKAPE pathogens.

CONCLUSIONS

A personalized body-worn alcohol dispenser can impact the epidemiology of ESKAPE transmission in the pediatric anesthesia work area environment. Improved preoperative patient decolonization and vascular care are indicated to address ESKAPE pathogens among pediatric anesthesia work area reservoirs.

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.

Quantifying and interpreting inequality of surgical site infections among operating rooms

PURPOSE

The incidence of surgical site infection differs among operating rooms (ORs). However, cost effectiveness of interventions targeting ORs depends on infection counts. The purpose of this study was to quantify the inequality of infection counts among ORs.

METHODS

We performed a single-centre historical cohort study of elective surgical cases spanning a 160-week period from May 2017 to May 2020, identifying cases of infection within 90 days using International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes. We used the Gini index to measure inequality of infections among ORs. As a reference, the Gini index for inequality of household disposable income in the US in 2017 was 0.39, and 0.31 for Canada.

RESULTS

There were 3,148 (3.67%) infections among the 85,744 cases studied. The 20% of 57 ORs with the most and least infections accounted for 44% (99% confidence interval [CI], 36 to 52) and 5% (99% CI, 2 to 8), respectively. The Gini index was 0.40 (99% CI, 0.31 to 0.50), which is comparable to income inequality in the US. There were more infections in ORs with more minutes of cases (Spearman correlation ρ = 0.68; P < 0.001), but generally not in ORs with more total cases (ρ = 0.11; P = 0.43). Moderately long (3.3 to 4.8 hr) cases had a large effect, having greater incidences of infection, while not being so long as to have just one case per day per OR. There was substantially greater inequality in infection counts among the 557 observed combinations of OR specialty (Gini index 0.85; 99% CI, 0.81 to 0.88).

CONCLUSIONS

Inequality of infections among ORs is substantial and caused by both inequality in the incidence of infections and inequality in the total minutes of cases. Inequality in infections among OR and specialty combinations is due principally to inequality in total minutes of cases.

Sample times for surveillance of S. aureus transmission to monitor effectiveness and provide feedback on intraoperative infection control

Background

Reductions in perioperative surgical site infections are obtained by a multifaceted approach including patient decolonization, vascular care, hand hygiene, and environmental cleaning. Associated surveillance of S. aureus transmission quantifies the effectiveness of these basic measures to prevent transmission of pathogenic bacteria and viruses to patients and clinicians, including Coronavirus Disease 2019 (COVID-19). To measure transmission, the observational units are pairs of successive surgical cases in the same operating room on the same day. In this prospective cohort study, we measured sampling times for inexperienced and experienced personnel.

Methods

OR PathTrac kits included 6 samples collected before the start of surgery and 7 after surgery. The time for consent also was recorded. We obtained 1677 measurements of time among 132 cases.

Results

Sampling times were not significantly affected by technician's experience, type of anesthetic, or patient's American Society of Anesthesiologists’ Physical Status. Sampling times before the start of surgery averaged less than 5 min (3.39 min [SE 0.23], P < 0.0001). Sampling times after surgery took approximately 5 min (4.39 [SE 0.25], P = 0.015). Total sampling times averaged less than 10 min without consent (7.79 [SE 0.50], P < 0.0001), and approximately 10 min with consent (10.22 [0.56], P = 0.70).

Conclusions

For routine use of monitoring S. aureus transmission, when done by personnel already present in the operating rooms of the cases, the personnel time budget can be 10 min per case.