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

Bita Naini

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