key: cord-0902761-6p7je20a
authors: Dexter, Franklin; Ledolter, Johannes; Wall, Russell T.; Datta, Subhradeep; Loftus, Randy W.
title: Sample sizes for surveillance of S. aureus transmission to monitor effectiveness and provide feedback on intraoperative infection control including for COVID-19
date: 2020-05-21
journal: Perioper Care Oper Room Manag
DOI: 10.1016/j.pcorm.2020.100115
sha: 72b3e073943fe60bc496803774bf4e08520f1040
doc_id: 902761
cord_uid: 6p7je20a

Reductions in perioperative surgical site infections are obtained by a multifaceted approach including patient decolonization, hand hygiene, use of closed lumen intravenous systems and hub disinfection, and environmental cleaning. Associated surveillance of S. aureus transmission quantifies the effectiveness of the basic measures to prevent the transmission to patients and clinicians of pathogenic bacteria and viruses, 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. We evaluated appropriate sample sizes and strategies for measuring transmission. We used historical cohort data from multiple hospitals. There was absence of serial correlation among observed counts of transmitted isolates within each of several periods (all P ≥.18). Similarly, observing transmission within or between cases of a pair did not increase the probability that the next sampled pair of cases also had observed transmission (all P ≥.23). Most pairs of cases had no detected transmitted isolates. Also, although transmission (yes/no) was associated with surgical site infection (P =.004), among cases with transmission, there was no detected dose response between counts of transmitted isolates and probability of infection (P =.25). Therefore, we recommend analyzing the presence/absence of transmission. The first of a fixed series of tests is to use the binomial test to compare the proportion of pairs of cases with S. aureus transmission to an acceptable threshold. An appropriate sample size for this screening is N =25 pairs. If significant, more samples are obtained while additional measures are implemented to reduce transmission and infections. Subsequent sampling is done to evaluate effectiveness. The two independent binomial proportions are compared using Boschloo's exact test. The total sample size for the 1(st) and 2(nd) stage is N =100 pairs. Because S. aureus transmission is invisible without testing, when choosing what population(s) to screen for surveillance, another endpoint needs to be used (e.g., infections). Only 10/298 combinations of specialty and operating room were relatively common (≥1.0% of cases) and had expected incidence ≥0.20 infections per 8 hours of sampled cases. The 10 combinations encompassed ≅17% of cases, showing the value of targeting surveillance of transmission to a few combinations of specialties and rooms. In conclusion, we created a sampling protocol and appropriate sample sizes for using S. aureus transmission within and between pairs of successive cases in the same operating room, the purpose being to monitor the quality of prevention of intraoperative spread of pathogenic bacteria and viruses.

To measure transmission, the observational units are pairs of successive surgical cases in the 18 same operating room on the same day. We evaluated appropriate sample sizes and strategies 19

for measuring transmission. 20

We used historical cohort data from multiple hospitals. 21

There was absence of serial correlation among observed counts of transmitted isolates 22

within each of several periods (all P . 18) . Similarly, observing transmission within or between 23 cases of a pair did not increase the probability that the next sampled pair of cases also had 24 observed transmission (all P ≥.23). 25

Most pairs of cases had no detected transmitted isolates. Also, although transmission 26 (yes/no) was associated with surgical site infection (P =.004), among cases with transmission, 27 there was no detected dose response between counts of transmitted isolates and probability of 28 infection (P =.25). Therefore, we recommend analyzing the presence/absence of transmission. 29

The first of a fixed series of tests is to use the binomial test to compare the proportion of 30 pairs of cases with S. aureus transmission to an acceptable threshold. An appropriate sample 31 size for this screening is N =25 pairs. If significant, more samples are obtained while additional 32 measures are implemented to reduce transmission and infections. Subsequent sampling is 33 done to evaluate effectiveness. The two independent binomial proportions are compared using 34

Boschloo's exact test. The total sample size for the 1 st and 2 nd stage is N =100 pairs. 35

Because S. aureus transmission is invisible without testing, when choosing what 36 population(s) to screen for surveillance, another endpoint needs to be used (e.g., infections). 37 PCORM-D-20-00042R1, Page 3

Only 10/298 combinations of specialty and operating room were relatively common (1.0% 38 of cases) and had expected incidence ≥0.20 infections per 8 hours of sampled cases. The 10 39 combinations encompassed 17% of cases, showing the value of targeting surveillance of 40

transmission to a few combinations of specialties and rooms. 41

In conclusion, we created a sampling protocol and appropriate sample sizes for using 42 S. aureus transmission within and between pairs of successive cases in the same operating 43 room, the purpose being to monitor the quality of prevention of intraoperative spread 44 of pathogenic bacteria and viruses. 45 The sample size of 336 pairs of cases (Table 1) was not chosen to perform the current 98 study. The start date of samples was chosen based on the hospital's objective to improve basic 99 perioperative infection control measures. The last date of samples was early March 2020, by 100 when there had been >100 cases in the USA of COVID-19. 16 The authors performed analyses 101 throughout March 2020, because we recognized that the data being collected could be used 102 to address quickly the need for greater intraoperative infection control because of COVID-19. 3 

The hospital collected no additional samples in March because elective surgery was stopped 104 because of the COVID-19 pandemic. 105

Interventions to reduce environmental contamination include capital equipment, and thus 106 vary among operating rooms (e.g., anesthesia machines with greater ease of 107 decontamination, 17 specialized ventilation systems, 18, 19 germicidal lighting, 20 door locks and 108 electronic signage to prevent main and inner core doors from being open simultaneously, 21 and 109 rooms of different physical configurations 22 ). Also, cases of different specialties are scheduled 110 non-randomly into specific operating rooms. 15, 23 However, using the data (Table 1) for 111 illustration, Kruskal-Wallis test (i.e., analysis of variance on ranks) for the counts of transmitted 112 isolates did not differ significantly among the 24 rooms, P =.32 (STATA 16.0, College Station, 113 TX). We created a fourth variable in the upload for readers, column D) transmitted isolate 0 114 versus 1. The incidence of transmission did not differ significantly among rooms, Fisher exact while pooling among rooms. 117

We divided the pairs of cases into quartiles of successive observations, creating the fifth 118 variable in the uploaded worksheet, column E. Because the sample size was not a multiple of 4, 119 the pairs of cases in each of the periods were similar, but not identical (Table 1 , range 83-86 120 pairs of cases). The unequal sample sizes among periods were selected to achieve absence 121 of overlap of dates among the periods. The counts of transmitted isolates differed among 122 periods, P <.001 using the Kruskal-Wallis test. The percentage incidences of transmission also 123 differed among periods, P <.001 using Fisher's exact test. 124

During the 1 st period, there were progressively increasing counts of transmitted S. aureus 125 isolates and proportion of pairs of cases with transmission ( Table 2 ). This is shown by the 126

Kendall's  b of the count data having positive association with date, and by logistic regression 127 of the binary data having positive association with date. This increase in transmission during 128 period 1 motivated the study hospital to improve patient decolonization, hand hygiene, use of 129 closed lumen intravenous systems and hub disinfection, and environmental cleaning, 130 supplemented by ongoing surveillance (e.g., to target the use of ultraviolet light [UV-C] for 131 specific rooms 1, 24 ). For our purposes in using the data to evaluate sample sizes for surveillance 132 of intraoperative transmission, the impact of the trend in period 1 was that the data from that 133 period were omitted from subsequent analyses. The hospital did not revise the infection-134 reducing bundle during the periods with collected samples; during period 4, COVID-19 was 135 minimally in its community. 136

Sample sizes for accurately estimating transmission within or between periods depend 138 markedly on the serial correlation. 25 When there is positive serial correlation, consecutive 139 observations stay above average for some time, and vice-versa. Sample sizes need to be has been foundational to operating room management science and how those data need to be 142 analyzed. 26, 27, 28, 29, 30 For example, serial correlation is a reason why adjusted and raw utilization 143 of operating room block time cannot be estimated accurately by surgeon. 26, 27 Serial correlation 144 affects why cancellation rates and turnover times need to be analyzed using batches (bins) of 145 two-weeks or longer. 28,30,31,32 146 Another reason to know whether serial correlation is present is that positive serial 147 correlation could be an advantage when selecting pairs of cases for sampling. 33 For example, 148 suppose that a hospital may implement an infection control bundle and feedback for two 149 populations, plastic surgery and gynecological oncology. If there were substantial serial 150 correlation, then if a pair of successive cases of plastic surgery had detected transmission, the 151 next pair of cases sampled likely should be plastic surgery. 33 There also would be an advantage 152 to having the S. aureus results available by early the next day for purposes of selecting pairs of 153 cases for surveillance. However, if there was no serial correlation, results need not (and should 154 not) influence sampling. 15 155

When a pair of cases for which sampling was done at the studied hospital had counts 156 of transmitted isolates that were above average for the period, with the average estimated using 157 the sample mean (Table 1) , was there increased chance that the next sampled pair of cases 158 also had transmitted isolates that were above average? That was not so, P =.18, P =.23, and 159 P =.63 among periods 2, 3, and 4, respectively (Table 2) . Similarly, we asked whether observing 160 transmission within or between cases of a pair 1,14 increased the probability that the next 161 sampled pair of cases also had observed transmission. That too was not so, P =.82, P =.23, and 162 P =.63, respectively (Table 2) . Finally, there was not significant serial correlation detected by the 163 runs test by date (Table 3 ). These results imply that there is no need to adjust the sample size 164 calculations shown below for serial correlation.

Three facts indicate that the endpoint for designing sampling should be the presence 167 versus absence of transmission (i.e., 0 versus 1 transmitted isolate). 168

First, most (i.e., greater than half) of the pairs of cases had no detected transmitted isolates 169 (Table 1) . words, the recommended sample size based on the means may not be generalizable among 179

Third, in the randomized study, surgeons were assigned at random to a group with usual 181 infection control or to another group with an infection control bundle and feedback on S. aureus 182 perioperative transmission. 1 Transmission, the primary study endpoint, was associated with 183 surgical site infection; 11.0% (8/73) of patients with S. aureus transmission detection had 184 infection versus 1.8% (3/163) without transmission detection, risk ratio 5.95, 95% confidence 185 interval 1.63-21.80, P =.004. 1 We evaluated for the current study whether transmission of more 186

isolates was associated with greater risk for infection. 1 Among the 11 patients with surgical site 187 infections, 1 the counts of transmitted isolates × patients were 8 ×1, 7 ×1, 3 ×4, and 1 ×2. 188

In comparison, among all 73 patients with transmitted isolates, the distribution of counts 189 of transmitted isolates were 10 ×1, 8 ×1, 7 ×4, 6 ×3, 5 ×7, 4 ×8, 3 ×8, 2 ×16, and 1 ×25. Using 190 the Kolmogorov-Smirnov two-sample test to compare the distributions of non-zero counts distributions did not differ significantly. 193

Based on the preceding three observations, we subsequently neglect the counts for 194 purposes of inference, and use the binary, presence or absence of transmission. 195

What we have determined to this point is that two statistical tests will be performed in 197 a fixed sequence (Table 4 ). First, the binomial proportion of pairs of cases with S. aureus 198 transmission will be estimated. The null hypothesis tested using the binomial test will be that the 199 incidence does not differ from an acceptable threshold. If P  (e.g., with  =.05), sampling 200 ceases because it would be more useful to target other populations (e.g., operating rooms). 201

If P < for the one-sided alternative that the incidence of transmission is greater than threshold, What we consider in the current section is how to adjust the selected Type I error rates for 211 each of the 2 sequential tests to achieve an overall  =.05. This is straightforward because the 212 2 hypotheses are tested in a fixed sequence, and if the finding for the first hypothesis were 213 P , there would be no need for consideration for the second hypothesis. To achieve an overall 214 family-wise Type I error rate of  under such a model, each of the sequential tests is performed 215

As summarized in the preceding section, the first of the two hypotheses tests whether the 218 proportion of pairs of cases with incidence of transmission of S. aureus significantly exceeds 219 a low threshold. For that threshold we use 12.05%, from Table 1 period 4. We plan for 80% 220 statistical power to detect a greater incidence of transmission at baseline, using the largest 221 observed incidence, 40.70% from Table 1 Period 2, 35 of 86 pairs. Using  =.05, the exact 222 sample size for the one-sided binomial test would be N =15 pairs of cases. Calculations were 223 performed using StatXact-12.0 (Cytel, Inc., Cambridge, MA). 224

The null hypothesis for the second test is no difference between the two groups, before and 225 after. We use the two-group one-sided Boschloo's exact test for the difference of independent The preceding sample size is based on use of Boschloo's test (Table 4 ). Because we are 232 performing one-sided tests, three common alternatives (Fisher's exact test, Pearson chi-233 squared test, and the likelihood ratio test) give identical P-values. 38 

The preceding incidences were not obtained as part of a formally described and followed 249 protocol. Therefore, although the incidences were estimated accurately, they may not 250 be generalizable. To address this potential limitation, we repeated our sample size calculations 251 using estimates of incidence from the recently published randomized trial. 1 

In the trial, surgeons were assigned either to a group with usual infection control or to 253 another group with an infection control bundle for 4 months followed by bundle with feedback on 254 S. aureus perioperative transmission for 8 months. 1 The first of the two hypotheses tests would be the one group one-sided binomial test. The University of Iowa's plan is to rely on the need for 25 pairs of cases for screening, and 274 75 additional pairs for each studied population (Table 4) . reported daily, including use of patient decolonization (viral antisepsis) and patient initial phase I 280 post-anesthesia care unit recovery and multimodal cleaning after each higher risk procedure or 281 when the procedure is sufficiently urgent that it was started before the results of testing for 282 SARS-CoV-2 were complete. 1,2,3,4,12,13, 24 We provide an example of defining the population for 283 sampling by using patient outcomes (i.e., infection) data from 4 studies of closed lumen 284 intravenous systems and hand decontamination. 41, 42, 43, 44 (These were deidentified data that 285 we had available, and quickly given that the current study was performed acutely when the 286 COVID-19 pandemic spread to the USA). The overall incidence of hospital acquired infection 287 was 7.5%, 294 infections among the 3936 cases (i.e., rows in the uploaded file). Associations 288 known from previous studies supported validity to this use of the data. Specifically, increases in 289 anesthesia duration were associated with greater incidence of infection (P <.0001 by Kendall's  290 and logistic regression). 23, 45, 46, 47 In addition, by Kruskal-Wallis tests, the incidence differed (P =.0001) 23 . For example, the incidences of infection were 15.5% for urology and 11.5% for 293 gynecology versus 4.7% for otolaryngology and 5.0% for orthopedic surgery. Among the 24 of 294 86 rooms each with at least 1.0% of cases, the two largest incidences of infection were 14.1% 295 and 14.0%, and the lowest observed incidences were 3.1% and 3.2%. 296

We examined the benefit of limiting the population sampled to a few combinations 297 of specialty and room (Table 4) . 15 ,a Operating room matters not only because environmental 298 contamination affects the room, and interventions to reduce transmission include capital 299 equipment installed in specific rooms, 17, 18, 19, 20, 21, 22 but because sampling of S. aureus 300 transmission is for successive cases in the same room and day. Specialty matters because 301 transmission differs among specialties, the incidences of infection differ among specialties, 302

interventions to reduce surgical site infections have contributions differing among specialties, 303 and the distribution of cases of different specialties differ among rooms due to case 304 scheduling. 6, 7, 14, 15, 23 For example, even though a breast surgery case may follow an orthopedic 305 case, 43,44 the pairing is sufficiently uncommon, and patient care sufficiently different, as to make 306 such pairing non-insightful. Because S. aureus transmission does not appear to be influenced 307 by duration, 1, 6, 7, 14 there is more opportunity by choosing pairs of cases from specialty ×rooms 308 with more expected infections per hour 15 . Among the 298 combinations of specialty and room, 309 there were only 10 that were both relatively common, 1.0% of cases, and expected to have 310 ≥0.20 infections per 8 hours of sampled cases ( Table 5 ). The 10 combinations encompassed 311 17% of cases, the population(s) to be targeted for surveillance of transmission. If sampling will 312 be done, pooled, with several combinations of specialty and room, then stratified sampling 313 a As written in the Introduction, from our previous studies, more pairs of cases can be sampled in a day if cases are brief, because duration is not a significant covariate for transmission. 1, 15 For the observational data (Table 1) , case durations of the pairs were available for the first 209 pairs by date. Using the Wilcoxon-Mann-Whitney test, there was the same finding of absence of association between duration and transmission (P = .91). Consequently, having chosen the specialty and room combinations to study, and knowing the total sample sizes of pairs of cases to obtain, sampling strategies should include the daily, deliberate, selection of the briefest pairs of cases.

for the effectiveness of basic measures to prevent the transmission of pathogenic bacteria and 318 viruses. The efficacy of an infection control bundle alone is greater when combined with 319 feedback on transmission. 1 We determined the statistical design for initiating surveillance. 320

As listed pointwise in Table 4 follow-up monitoring can be done using Bernoulli CUSUM control charts. 332

The surveillance of S. aureus transmission as studied in this paper applies to feedback 333 to a hospital (e.g., Tables 1-3), surgical specialty (e.g., Table 4 ), and/or study sponsor with 334 capital equipment installed in individual operating rooms (e.g., as described in Reference 15) . 335 A limitation of our study is that the data did not include transmission measured using whole 354 cell genome analysis. 7 Genomic analysis permits characterization of reservoirs of origin leading 355 to infection and identification of reservoirs such as operating room environments or provider 356 hands that infect repeatedly over days and weeks. Such feedback, different than examined in 357 the current study, is useful when a hospital is faced with especially pathogenic pathogens, such 358 as strong biofilm forming and/or desiccation tolerating S. aureus strains or ESKAPE 359 pathogens. 6, 7, 8, 14 Furthermore, a hospital may want to examine the particular path for 360 transmission of pathogens commonly linked to infections in their organization. Such 361 transmission stories (i.e., essentially quality improvement case reports within the organization), 362 require analysis of data by individual patient. The surveillance approach presented in the current 363 paper represents only one option for use of surveillance for improvement in perioperative 364 infection control practices. The approach in the current paper is that applicable to monitoring 365 of S. aureus transmission as a quantitative measure for infection control quality (e.g., 366

appropriate for the COVID-19 crisis). 3, 4 within and among proven reservoirs as a marker of behavioral performance at applying all 369 facets of an infection-control bundle. 5, 6, 7, 41, 43 Preventing environmental contamination 370 is important not only because it endangers patients but also healthcare workers. For example, 371

hyper-transmissible desiccation resistant S. aureus was isolated at the end of cases from 372 anesthesiology residents' hands, certified registered nurse anesthetists' hands, and anesthesia 373 machines' dials and valves. 6 Similarly, viable SARS-CoV-1 and SARS-CoV-2 was isolated days 374 later after experimental placement from plastic and stainless steel surfaces, and in hospital 375 swabbing studies SARS-CoV-1 was found to have been transmitted to nursing stations' 376 computers, telephones, doorknobs, and tables. 52, 53, 54, 55 SARS-CoV-2 was detected on water 377 machines, elevator buttons, telephones, computer mice, and keyboards (i.e., environmental 378

surfacesno air samples had detectable virus). 56 In a previous study we addressed operating 379 room management strategies to reduce personnel risk from COVID-19 (e.g., preoperatively 380 testing all patients for SARS-CoV-2 before elective surgery and limiting lower airway aerosol 381 producing procedures to a few designated rooms). 4 Others have addressed the disinfection of 382 personal protective equipment. 57,58,59 383 Finally, our results are limited by the data used, because they were not collected for 384 purposes of the study. However, because of the COVID-19 pandemic, the University of Iowa 385 initiated rapid dissemination of the multifaceted approach 1 to reduce intraoperative bacterial and 386 viral contamination, and plans to use Bernoulli CUSUM to monitor for group level feedback of 387 sustainability. 40 We needed to know appropriate sample sizes, inferential testing, and basis for 388 selecting pairs of cases to sample. Yet, implementation of intraoperative infection control 389 bundles and feedback are not literally two periods, as we modeled, they are a time series. The 390 use of segmented regression in the logit scale, for the binary variable of transmission or not, 391 seems an intuitively better choice. However, from the available data, we not only have 392 insufficient sample size to develop such a model for statistical power analyses, we lack the Because preventing intraoperative environmental contamination from COVID-19 is essential for 395 the acute crisis, we hope others use our results, collect more data, and can make comparisons 396 with our uploaded data. The counts of transmitted isolates differed significantly among periods, P < .001 using the Kruskal-Wallis test. The percentage incidence of transmission also differed among periods, P < .001 using Fisher's exact test. PCORM-D-20-00042R1, Page 31 

Improving basic preventive measures in the perioperative arena to reduce S. aureus transmission and surgical site infections, a randomized trial

Importance of oral and nasal decontamination for patients undergoing anesthetics during the COVID-19 era

Perioperative COVID-19 defense: an evidencebased approach for optimization of infection control and operating room management

Strategies for daily operating room management of ambulatory surgery centers following resolution of the acute phase of the COVID-19 pandemic

Methicillin-resistant S. aureus has greater risk of transmission in the operating room than methicillin-sensitive S. aureus

Desiccation tolerance is associated with Staphylococcus aureus hyper transmissibility, resistance, and infection development in the operating room

High-risk S. aureus transmission in the operating room: a call for widespread improvements in perioperative hand hygiene and patient decolonization practices

Dynamics of intraoperative Klebsiella, Acinetobacter, Pseudomonas, and Enterobacter transmission

The dynamics of Enterococcus transmission from bacterial reservoirs commonly encountered by anesthesia providers

Transmission dynamics of gram negative bacterial pathogens in the anesthesia work area

Multiple reservoirs contribute to intraoperative bacterial transmission

Rapid and effective virucidal activity of povidone-iodine products against Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and Modified Vaccinia Virus Ankara (MVA)

Infectious disease management and control with povidone iodine

Operating room PathTrac analysis of current intraoperative S. aureus transmission dynamics

Benefit of systematic selection of pairs of cases matched by surgical specialty for surveillance of bacterial transmission in operating rooms

Assessment of anesthesia machine redesign on cleaning of the anesthesia machine using surface disinfection wipes

Effect of ventilation strategies on infection control inside operating theatres

The effectiveness of ultra-clean air operating theatres in the prevention of deep infection in joint arthroplasty surgery

Influence of a visible-light continuous environmental disinfection system on microbial contamination and surgical site infections in an orthopedic operating room

The effect of door opening on positive pressure and airflow in operating rooms

Factors affecting acoustics and speech intelligibility in the operating room: size matters

Importance of operating room case scheduling on analyses of observed reductions in surgical site infections from the purchase and installation of capital equipment in operating rooms

Efficacy of an automated multiple emitter whole-room Ultraviolet-C disinfection system against coronaviruses MHV and MERS-CoV

Simulation Modeling and Analysis

An operating room scheduling strategy to maximize the use of operating room block time: Computer simulation of patient scheduling and survey of patients' preferences for surgical waiting time

Operating room utilization alone is not an accurate metric for the allocation of operating room block time to individual surgeons with low caseloads

Validation of statistical methods to compare cancellation rates on the day of surgery

Estimating the incidence of prolonged turnover times and delays by time of day

Elective change of surgeon during the OR day has an operationally negligible impact on turnover time

Redesigning intensive care unit flow using variability management to improve access and safety

Relative influence on total cancelled operating room time from patients who are inpatients or outpatients preoperatively

How to whack moles

Clinical trials.gov, Evaluation of the impact of germicidal lights (Indigo-Clean) on intraoperative S. aureus exposure

Multiple testing in clinical trials

Testing strategies in multi-dose experiments including active control

The fallback procedure for evaluating a single family of hypotheses

Exact tests for 2 x 2 contingency tables

A cautionary note on exact unconditional inference for a difference between two independent binomial proportions

Bernoulli cumulative sum (CUSUM) control charts for monitoring of anesthesiologists' performance in supervising anesthesia residents and nurse anesthetists

Reduction in intraoperative bacterial contamination of peripheral intravenous tubing through the use of a novel device

Hand contamination of anesthesia providers is an important risk factor for intraoperative bacterial transmission

Reduction in intraoperative bacterial contamination of peripheral intravenous tubing through the use of a passive catheter care system

Frequency of hand decontamination of intraoperative providers and reduction of postoperative healthcareassociated infections: A randomized clinical trial of a novel hand hygiene system

Identifying patients at high risk of surgical wound infection: a simple multivariate index of patient susceptibility and wound contamination

Surgical site infection (SSI) rates in the United States, 1992-1998: the National Nosocomial Infections Surveillance System basic SSI risk index

Improving risk-adjusted measures of surgical site infection for the national healthcare safety network

Decreasing the hours that anesthesiologist and nurse anesthetists work late by making decisions to reduce the hours of over-utilized operating room time

Surgeons' and anesthesiologists' perceptions of turnover times

At most hospitals in the State of Iowa, most surgeons' daily lists of elective cases include only 1 or 2 cases: individual surgeons' percentage operating room utilization is a consistently unreliable metric

Appropriate operating room time allocations and half-day block time for low caseload proceduralists, including anesthesiologist pain medicine physicians in the State of Florida

Severe acute respiratory syndrome coronavirus on hospital surfaces

Transmission of SARS and MERS coronaviruses and influenza virus in healthcare settings: the possible role of dry surface contamination

Stability of SARSCoV-2 in different environmental conditions

Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1

Environmental contamination by SARS-CoV-2 in a designated hospital for coronavirus disease 2019

Effectiveness of ultraviolet-C light and a high-level disinfection cabinet for decontamination of N95 respirators

Can N95 respirators be reused after disinfection? How many times?

Disinfection of N95 respirators by ionized hydrogen peroxide during pandemic coronavirus disease 2019 (COVID-19) due to SARS-CoV-2