Society: ASGE

Background and aim: Despite extensive cleaning and disinfection protocols, 5-15% of ready-to-use duodenoscopes are still contaminated with high concern (HC) microorganisms such as Klebsiella pneumoniae, Escherichia coli and Pseudomonas aeruginosa. Changes in cleaning protocols might reduce the contamination rate and hereby prevent future outbreaks. This study aims to investigate the effect of a new endoscope cleaning brush on contamination of duodenoscopes with HC microorganisms.
Methods: In this retrospective observational study, the results of the duodenoscope surveillance cultures between March 2018 and June 2022 were collected. Contamination with HC microorganisms was defined as ≥1 colony-forming units of gastrointestinal microorganisms including P. aeruginosa or Staphylococcus aureus. Cultures of quarantined duodenoscopes were not included. From December 2020, a new endoscope cleaning brush with an additional wiper (Endoss Push and Pull brush, JPP50) was introduced for the manual pre-cleaning of the Pentax ED34-i10T2 duodenoscopes instead of the Pentax Single Use Brush (CS5522A). Using a generalized mixed effect model, the effect of the introduction of the new endoscope cleaning brush on Pentax ED34-i10T2 duodenoscope contamination with HC microorganisms was assessed. Other covariates were frequency of use, repairs by the manufacturer and the result of the prior culture.
Results: Data from 195 cultures of eight duodenoscopes was collected; 122 cultures prior to the introduction of the new brush and 73 after introduction. Prior to the introduction, 51/122 (41.8%) cultures were positive with HC microorganisms, especially with P. aeruginosa (29/122, 23.8%). After introduction of the brush only 7/73 (9.6%) were positive with HC microorganisms of which only one was positive with P. aeruginosa. Duodenoscopes cleaned with the new brush had a significantly lower odds of contamination with HC microorganisms compared to duodenoscopes cleaned with the old brush (OR = 0.17, 95 % CI [0.07, 0.42], p <0.001).
Conclusions: The Endoss Push and Pull brush significantly reduced contamination of ED34-i10T2 duodenoscopes, and it is therefore promising in the prevention of healthcare-associated infections and outbreaks. In the future, these results should be confirmed in a prospective study with different types of duodenoscopes.

INTRODUCTION:
Improvements in colonoscopy quality are associated with reductions in interval colorectal cancer death. However, measurement of colonoscopy quality in practice remains challenging. We aim to describe and validate an interactive tool for artificial intelligence (AI) assessment of colonoscopy quality (AI-CQ) using recorded videos.

METHODS:
Based on quality guidelines, metrics were selected for AI development: insertion time (IT), withdrawal time (WT), retroflexion frequency, polyp detection rate (PDR), polyps per colonoscopy (PPC), and number of right colon evaluations. We also developed two novel metrics: withdrawal time excluding polypectomy time (WT-PT) and high-quality withdrawal time (HQ-WT; withdrawal time with clear colon image). The AI model was pre-trained using a self-supervised vision transformer on unlabeled colonoscopy images (n = 1x10⁷) mutually exclusive from all other datasets. The vision transformer model was finetuned for multi-label classification on another mutually exclusive colonoscopy image dataset (n = 9854) using anatomical, procedural, and pathological labels (label n = 14). During inference, colonoscopy video frame predictions were generated at a resolution of one frame per second and employed a binary threshold of ≥ 0.5 to denote presence; these predictions were subsequently used to calculate all metrics. A timeline of video predictions and metric calculations were presented to clinicians in addition to the raw video using a web-based application. The model was developed using videos at a single hospital and externally validated using 50 screening and surveillance colonoscopy videos from 6 colonoscopists at a second hospital.

RESULTS:
The interactive AI-CQ tool is presented (Figure). The cecum was reached in 48/50 cases; AI-CQ accuracy to identify cecal intubation was 88%. In 6 cases, AI-CQ did not identify the cecum due to inadequate bowel preparation obscuring landmarks (n=4) and failure to recognize landmarks (n=2).

IT (p = 0.26) and WT (p = 0.34) were similar between manual and AI-CQ measurements and significantly (p < 0.001) positively correlated (Table). On average, HQ-WT was 45.9% (IQR: 14) of, and significantly correlated with (ρ = 0.85; p < 0.001), normal WT time. Mean WT-PT was 567s, similar to mean normal colonoscopy WT (558s).

AI-CQ produced similar PDR (p = 0.66) and PPC compared to manual (p = 0.34). Rectal retroflexion was correctly identified in 95.2% of colonoscopies and the number of right colon evaluations in all colonoscopies.

DISCUSSION:
AI-CQ can be utilized to rapidly measure quality and facilitates AI-augmented review of inspection and polypectomy technique to provide actionable feedback. Further, novel inspection metrics such as WT-PT and HQ-WT, which can only be feasibly calculated by AI, may prove beneficial but require further study.

The AI-CQ is an interactive tool that automatically identifies multiple landmark events during a colonoscopy (e.g., the time the cecum is reached, when a polyp is identified, what tool is utilized to remove the polyp, etc.). This facilitates measurement of multiple colonoscopy metrics as well as allows the reviewer to rapidly watch a segment of the colonoscopy video to provide actionable feedback.

The AI-CQ is an interactive tool that automatically identifies multiple landmark events during a colonoscopy (e.g., the time the cecum is reached, when a polyp is identified, what tool is utilized to remove the polyp, etc.). This facilitates measurement of multiple colonoscopy metrics as well as allows the reviewer to rapidly watch a segment of the colonoscopy video to provide actionable feedback.

Manual and Artificial Intelligence Colonoscopy Quality (AI-CQ) timings and polyp detection. Data presented as median (IQR) unless noted. ** p < 0.001

Manual and Artificial Intelligence Colonoscopy Quality (AI-CQ) timings and polyp detection. Data presented as median (IQR) unless noted. ** p < 0.001

Background: Our work and that of others has demonstrated that simethicone residue and associated moisture persist in fully reprocessed endoscopes despite adherence to current endoscope reprocessing guidelines and manual drying. In light of this, the three major endoscope manufacturers now discourage the use of simethicone due to concern that retained moisture associated with simethicone may foster microbial growth and development of biofilms. However, simethicone can be highly advantageous during endoscopy due to its bubble dissolution capability and enhanced mucosal visualization/lesion detection. We have demonstrated that 10 minutes of automated drying is sufficient to eliminate moisture from all endoscope working channels without simethicone use. We now assess whether there is an automated drying duration at which all moisture is eliminated even following simethicone use.

Methods: Colonoscopy was performed utilizing water or standardized amounts of varied Simethicone concentrations (0.5%-low, 1%-moderate, 3%-high) for flushing. Following HLD, automated drying was performed using DriScope^TM for all concentrations of simethicone. We then conducted blinded borescope inspection of endoscope working channels to assess for retained fluid droplets under each condition.

Results: Following 10-minute automated drying, use of low concentration simethicone was associated with rare (median of 2, range 0-4) retained fluid droplets within endoscope working channels, use of moderate concentration simethicone was associated with a median of 5 (range 2-10) droplets, and use of high concentration simethicone was associated with a median of 12 (range 4-28) droplets (Table 1). Droplets appeared clear to opaque. 15 minute automated drying resulted in no visible fluid droplets upon endoscope inspection for all simethicone concentrations evaluated.

Conclusions: Simethicone can be highly beneficial to clear bubbles, thereby enhancing endoscopic visualization and detection of lesion. We found that extended automated drying for 15 minutes led to elimination of residual moisture within endoscope working channels even in the setting of up to 3% (high concentration) simethicone use. This warrants further study, but suggests that prolonged automated drying may mitigate the fluid retention associated with simethicone use during endoscopic procedures.

Introduction: High quality colonoscopy is the hallmark of effective colorectal cancer (CRC) screening. Despite a national focus on colonoscopy quality, measuring quality indicators (QIs) is labor-intensive and often done inconsistently. We previously developed and validated a natural language processing (NLP) algorithm that automates the extraction and reporting of colonoscopy QIs in our health system. In this quality initiative, we used these NLP-derived QI measures to build a clinical dashboard that tracks real-time colonoscopy QI data.

Methods: The setting for this study is a large academic health center with a defined primary care population, robust referral-based care, and 6 outpatient endoscopy facilities that perform over 17,000 screening colonoscopies annually. In prior work we developed, validated, and integrated into our health system an NLP algorithm that utilizes machine learning to identify, extract and structure data from free-text electronic health record colonoscopy and pathology reports. These data enable real-time measurement of colonoscopy QIs, based on the 2015 ASGE/ACG colonoscopy quality indicator recommendations. For this quality improvement initiative, we held interdisciplinary meetings to discuss dashboard content and formatting for optimal QI information dispersion. The dashboard currently consists of five QIs measured across all screening/surveillance colonoscopies performed at our institution: documentation of colonoscopy indication (IND), cecal intubation (CI), documentation of bowel preparation (BP), adequate bowel preparation (ABP), and adenoma detection rate (ADR; by institution, provider, and patient sex). ASGE/ACG performance goals for each QI are indicated as benchmarks. The dashboard excludes colonoscopists who performed <20 colonoscopies per year.

Result: The figure shows a snapshot of the colonoscopy QI clinical dashboard for the period between 1/1/2022 and 09/30/2022. In that period, there were 12,903 colonoscopies performed for 12,792 patients. Patients were 52.2% female and 48.2% non-White, and mean age was 56.4 ± 8.51 (Table). Mean health system performance was: 100% for IND, 100% for CI, 100% for BP, 97.9% for ABP, 30.5% for female ADR, and 43.0% for male ADR. All five measured institutional QIs exceeded ASGE/ACG performance goals. In all, 94.1% of providers met the ASGE/ACG male ADR, and 84.3% of providers met the ASGE/ACG female ADR goal. (Figure)

Conclusion: We successfully developed a real-time clinical dashboard that allows for accessible visualization and regular feedback of screening colonoscopy quality. The dashboard will be used to identify underperforming colonoscopists, help assess whether future interventions are needed, and allow for convenient evaluation of those interventions. Future development will include indexing pre-procedural and post-procedural QIs in the dashboard.

<b>Table: </b>Snapshot population: patients aged 45-75 years with at least one screening/surveillance colonoscopy performed at UCLA Health between 1/1/2022 to 9/30/2022; n=12792 patients (12,903 colonoscopies)

Table: Snapshot population: patients aged 45-75 years with at least one screening/surveillance colonoscopy performed at UCLA Health between 1/1/2022 to 9/30/2022; n=12792 patients (12,903 colonoscopies)

<b>Figure:</b> Snapshot of the UCLA Health Colonoscopy Quality Indicators Clinical Dashboard

Figure: Snapshot of the UCLA Health Colonoscopy Quality Indicators Clinical Dashboard

Background: Current colorectal cancer (CRC) screening recommendations (e.g. USPSTF) rely on modeling that balance patient-level benefits and harms. Environmental harms are not considered although their impact to planet and human health can be substantial as healthcare generates 8.5% of all greenhouse gas emissions in the US.
Aim: To compare the carbon footprint generated by travel and waste of a primary colonoscopy screening strategy with fecal immunochemical testing (FIT) based screening strategies.
Methods: We examined three hypothetical cohorts of 1000 screen eligible persons in the US undergoing one of three screening strategies over a 10-year time horizon: a) primary colonoscopy, b) annual FIT, c) biennial FIT. Probabilities were obtained from publicly available data to calculate total colonoscopy use and travel needs for each strategy. Waste estimates were based on a 5-day audit at two hospitals. Environmental impact analysis was performed using SimaPro software and ISO14040 standards. The main outcome of interest was the carbon footprint of travel and waste in each of these cohorts (expressed as kgCO₂e). We then extrapolated our results to all screen eligible 45-75-year-old persons in the US to estimate the carbon footprint and potential savings per year at the national level (expressed as tCO₂e).
Results: The primary colonoscopy screening strategy generated the greatest carbon footprint (9,806 kgCO₂e), followed by the annual FIT strategy (3,970 kgCO₂e), and the biennial FIT strategy (2,202 kgCO₂e). Compared to a colonoscopy screening program, an annual FIT program would reduce the carbon footprint by 60% and a biennial FIT program by 78% (table). When applying these results to all screen eligible persons in the US each year (9 million), the total reduction in carbon footprint per year would approximate 5,360 tCO₂e when transitioning from a primary colonoscopy program to an annual FIT program, and 6,983 tCO₂e for a biennial-FIT program (equivalent of 5,100 and 6,600 transatlantic passenger flights avoided, and 10 and 13 square miles of forest needed to absorb these emissions, respectively).
Limitations: We assumed 100% adherence to screening guidelines and subsequent follow up recommendations. Important carbon contributions to the screening pathways were not considered (e.g., supply chain, electricity needs), however, these would likely expand emission differences.
Conclusion: Switching from a primary colonoscopy CRC screening program to a primary FIT program would considerably lower the carbon footprint related to travel and waste alone by approximately 60% at a minimum. Assuming equal effectiveness, switching to a FIT based screening program would achieve a carbon footprint reduction of CRC screening that is in line with the international goal of a 50% reduction of greenhouse gas emissions by 2030.

<b>Table.</b> Carbon footprint from travel and waste of three CRC screening approaches for cohorts of 1000 screen-eligible persons (45-75 years of age) over a 10-year screening period and applied to all screen eligible persons in the US each year.

Table. Carbon footprint from travel and waste of three CRC screening approaches for cohorts of 1000 screen-eligible persons (45-75 years of age) over a 10-year screening period and applied to all screen eligible persons in the US each year.

Introduction
The COVID-19 pandemic dramatically impacted endoscopy practice with significant reductions in procedural capacity and recommendations to postpone elective cases. Professional societies issued triaging suggestions but did not address the acuity or priority of therapeutic procedures for evaluation and removal of premalignant lesions in the esophagus, stomach, or colon. The aim of this study was to evaluate the time to endoscopy related to the pandemic and whether delays led to pathology upstaging.

Methods
We conducted a retrospective cohort study at a single tertiary care center of all individuals referred for endoscopic therapy between July 2019-January 2022 for 1) large tubular adenoma or sessile serrated polyp >2cm or with villous histology or high-grade dysplasia, 2) Barrett’s esophagus (BE) with any dysplasia, and 3)gastric nodule with any dysplasia. Index histology for BE was reclassified according to expert pathology review when available to account for misclassification bias. The log-rank test was used to compare time from index exam to therapeutic exam pre pandemic (before March 20,2020) and during the pandemic period. Multivariable logistic regression was used to evaluate whether days to therapeutic exam and lesion type were associated with the primary outcome of change in histology (upstaging).

Results
There were 322 patients included (56% male, mean age 65) with 258 colon polyps, 54 BE related neoplasia and 10 gastric lesions with dysplasia. The median time to therapeutic exam was 78 days (range 0-799). Time to therapeutic exam was significantly shorter in cases completed after the onset of the pandemic compared to prior (HR: 1.35, 95% CI 1.07-1.72, p= 0.013). Change in histology from index to therapeutic exam was noted in 56 cases (17.4%) and was mostly upstaging (12.4%). There was no association between time to therapeutic exam and upstaged diagnosis (OR 1, p= 0.56). Lesion type was not significantly associated with the probability of being upstaged (p = 0.9) and neither were sex, age, or timing of exam in relation to the COVID-19 pandemic. Adjusted probability for having an upstaged diagnosis was 0.13 for BE, 0.11 for patients with colon polyps, and 0.09 for gastric lesions.

Conclusions
Contrary to our hypothesis, time to endoscopy was significantly shorter for patients whose procedure took place after the onset of the COVID-19 pandemic. Less than 20% of cases had a change in histology from index to therapeutic exam and most of these were upstaged. These findings suggest that procedures for removal of dysplastic BE, advanced colon polyps, or dysplastic gastric lesions can potentially be postponed with minimal impact. These data can help inform evidence-based recommendations and guide triage decision making for future interruptions in endoscopy care.

<b>Table. Patient characteristics overall and by lesion type</b>

Table. Patient characteristics overall and by lesion type

<b>Figure. Time to therapeutic exam before and after the COVID-19 pandemic</b>

Figure. Time to therapeutic exam before and after the COVID-19 pandemic

Introduction
Inefficiencies and delays in the endoscopy suite are a source of consternation for physicians, hospital administrators, staff, and patients alike. One variable that is often cited as a source of inefficiency is “turnover time” (TOT), meaning the amount of time between one case ending and another beginning. However, the exact method of measuring this variable varies considerably depending on which team member is asked. In this study we aimed to define the components that contribute to TOT, characterize TOT according to different perceptions, and identify which steps within the turnover process could be optimized in an endoscopy unit at a single tertiary center. In addition, we examined various factors affecting TOT.
Methods
We identified 7 key components in our pathway that define perceived TOT (PTOT) for various subgroups (Figure 1). Time stamps for these components were available in the electronic medical record. We defined gastroenterologist PTOT as time from procedure end until procedure start. Anesthesiology PTOT was defined as time from previous patient arrival to post-anesthesia care unit (PACU) to the next patient in the procedure room. Standard TOT was defined as the time between a patient leaving the room and the next patient entering the room. A total of 7241 cases were included. For each case, the PTOT was calculated for each subgroup (gastroenterologist, anesthesiologist, standard/administrative) and the average was calculated for all cases.
Results
Our preliminary results show that there is a difference in gastroenterologist PTOT depending on the type of anesthesia used, general vs monitored anesthesia care (MAC): there was a 14 minute increase in gastroenterologist PTOT for general anesthesia cases (Figure 2). Interestingly there is only a minimal difference in terms of anesthesia PTOT and standard TOT when comparing MAC and general anesthesia, which could be due to post-procedure anesthesia care in the endoscopy suite. Gastroenterologist PTOT was also higher in ERCP and EUS cases compared with other types of endoscopy, which could be due to increased use of general anesthesia for these cases.
Discussion
This preliminary study provides important information on perceptions of endoscopy TOT and identifies factors that can be targeted with the ultimate goal to improve efficiency in endoscopy. Future studies could examine the utility of using multiple anesthesia teams working with one gastroenterologist or providing post-procedure anesthesia care in PACU rather than endoscopy suite.

Figure 1. Overview of percieved turn over time (PTOT) with identified steps in the PTOT pathway and description of the various turnover time definitions (anesthesia PTOT, standard TOT, RN/Tech PTOT, gastroenterology attending PTOT). RN = registered nurse, Tech = technician, GI = gastroenterology, PACU = post-anesthesia care unit, OR = operating room, PR = procedure room

Figure 1. Overview of percieved turn over time (PTOT) with identified steps in the PTOT pathway and description of the various turnover time definitions (anesthesia PTOT, standard TOT, RN/Tech PTOT, gastroenterology attending PTOT). RN = registered nurse, Tech = technician, GI = gastroenterology, PACU = post-anesthesia care unit, OR = operating room, PR = procedure room

Figure 2. Subgroup analysis of Percieved TOT. A) Average TOT for gastroenterologist, anesthesia and standard for general versus monitored anesthesia care. B) Overview of average time used for the steps within the critical care pathway, which is the time between the procedure ending and starting time out. C) Average TOT per procedure type (EGD, colonoscopy, EGD + colonoscopy or advanced endoscopy (EUS or ERCP). EUS = endoscopic ultrasound, ERCP = endoscopic retrograde cholangiopancreatography, EGD = esophagogastroduodenoscopy, Std = standard, RN = registered nurse, Tech = technician, Anes = anesthesiology, CRNA = certified registered nurse anesthetist, Doc = doctor of gastroenterology,

Figure 2. Subgroup analysis of Percieved TOT. A) Average TOT for gastroenterologist, anesthesia and standard for general versus monitored anesthesia care. B) Overview of average time used for the steps within the critical care pathway, which is the time between the procedure ending and starting time out. C) Average TOT per procedure type (EGD, colonoscopy, EGD + colonoscopy or advanced endoscopy (EUS or ERCP). EUS = endoscopic ultrasound, ERCP = endoscopic retrograde cholangiopancreatography, EGD = esophagogastroduodenoscopy, Std = standard, RN = registered nurse, Tech = technician, Anes = anesthesiology, CRNA = certified registered nurse anesthetist, Doc = doctor of gastroenterology,

Prabakar Theivaraaj

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