Adenoma Detection Rate Falls at the End of the Day in a Large Multi-site Sample

Marcondes, Felippe O.; Gourevitch, Rebecca; Schoen, Robert E.; Crockett, Seth D.; Morris, Michele; Mehrotra, Ateev

doi:10.1007/s10620-018-4947-1

Cited by 22 publications

(18 citation statements)

References 24 publications

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“…Our known data analysis found a significant association between the time of day that a colonoscopy is performed and mean WT, with lower times later in the day, culminating in an increased proportion with a WT less than 6 min. This finding is in agreement with Marcondes et al, who found a roughly 20% reduction in WT from the first to last colonoscopy of the day [ 8 ]. A decline in ADR and PDR was also apparent in colonoscopies performed later in the clinic schedule.…”

Section: Discussionsupporting

confidence: 93%

Factors Associated with Withdrawal Time in European Colonoscopy Practice: Findings of the European Colonoscopy Quality Investigation (ECQI) Group

et al. 2022

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The European Colonoscopy Quality Investigation (ECQI) Group aims to raise awareness for improvement in colonoscopy standards across Europe. We analyzed data collected on a sample of procedures conducted across Europe to evaluate the achievement of the European Society of Gastrointestinal Endoscopy (ESGE) mean withdrawal time (WT) target. We also investigated factors associated with WT, in the hope of establishing areas that could lead to a quality improvement. Methods: 6445 form completions from 12 countries between 2 June 2016 and 30 April 2018 were considered for this analysis. We performed an exploratory analysis looking at WT according to the ESGE definition. Stepwise multivariable logistic regression analysis was conducted to determine the most influential associated factors after adjusting for the other pre-specified variables. Results: In 1150 qualifying colonoscopies, the mean WT was 7.8 min. Stepwise analysis, including 587 procedures where all inputs were known, found that the variables most associated with mean WT were a previous total colonoscopy in the last five years (p = 0.0011) and the time of day the colonoscopy was performed (p = 0.0192). The main factor associated with a WT < 6 min was the time of day that a colonoscopy was performed. Use of sedation was the main factor associated with a higher proportion of WT > 10 min, along with a previous colonoscopy. Conclusions: On average, the sample of European practice captured by the ECQI survey met the minimum standard set by the ESGE. However, there was variation and potential for improvement.

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Section: Discussionsupporting

confidence: 93%

Factors Associated with Withdrawal Time in European Colonoscopy Practice: Findings of the European Colonoscopy Quality Investigation (ECQI) Group

et al. 2022

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“…5,6 ADR varies dramatically among different endoscopists (7%-53%) 7 and also for each operator during the course of the day (approximately 7%), probably due to the intrinsic imprecision of the human eye brought about by fatigue and rush activities. 8 New technologies have been reported in the literature to improve ADR, including enhanced optics, distal attachments, cap-assisted techniques, and balloon-assisted devices, with the goal of improving mucosa visualization and eventually diagnosing hardly detectable polyps. 9 By aiding polyp detection on colonoscopy images (Figs.…”

Section: Introductionmentioning

confidence: 99%

“…Higher ADR is associated with lower interval CRCs and lower CRC mortality [ 5 , 6 ]. ADR varies dramatically among different endoscopists (7%–53%) [ 7 ] and also for each operator during the course of the day (approximately 7%), probably due to the intrinsic imprecision of the human eye brought about by fatigue and rush activities [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Lower Gastrointestinal Endoscopy: The Current Status and Future Perspective

et al. 2021

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aims to review the history, recent advances, evidence, and challenges of artificial intelligence (AI) in colonoscopy. Although it is mainly focused on polyp detection and characterization, it also considers other potential applications (i.e., inflammatory bowel disease) and future perspectives. Some of the most recent algorithms show promising results that are similar to human expert performance. The integration of AI in routine clinical practice will be challenging, with significant issues to overcome (i.e., regulatory, reimbursement). Medico-legal issues will also need to be addressed. With the exception of an AI system that is already available in selected countries (GI Genius; Medtronic, Minneapolis, MN, USA), the majority of the technology is still in its infancy and has not yet been proven to reach a sufficient diagnostic performance to be adopted in the clinical practice. However, larger players will enter the arena of AI in the next few months. Clin Endosc 2021 Jan 13. [Epub ahead of print]

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“…), light reflection of normal mucosa and feces. One study reported that a colonoscopist achieves a polyp detection rate ranging from 7% to 20% in a day 21 . Previous studies have suggested that additional observers are helpful in reducing the misdetection of visible polyps 22–24 .…”

Section: Discussionmentioning

confidence: 99%

“…One study reported that a colonoscopist achieves a polyp detection rate ranging from 7% to 20% in a day. 21 Previous studies have suggested that additional observers are helpful in reducing the misdetection of visible polyps. [22][23][24] Given the low falsepositive rate and high sensitivity, the CADe algorithm may assist colonoscopists to improve their performance and ultimately benefit patients.…”

Section: Discussionmentioning

confidence: 99%

Polyp detection algorithm can detect small polyps: Ex vivo reading test compared with endoscopists

Guo

Nemoto

Zhu

et al. 2020

Digestive Endoscopy

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Background and Study Aims: Small polyps are occasionally missed during colonoscopy. This study was conducted to validate the diagnostic performance of a polyp-detection algorithm to alert endoscopists to unrecognized lesions. Methods: A computer-aided detection (CADe) algorithm was developed based on convolutional neural networks using training data from 1991 still colonoscopy images from 283 subjects with adenomatous polyps. The CADe algorithm was evaluated on a validation dataset including 50 short videos with 1-2 polyps (3.5 AE 1.5 mm, range 2-8 mm) and 50 videos without polyps. Two expert colonoscopists and two physicians in training separately read the same videos, blinded to the presence of polyps. The CADe algorithm was also evaluated using eight full videos with polyps and seven full videos without a polyp. Results: The per-video sensitivity of CADe for polyp detection was 88% and the per-frame false-positive rate was 2.8%, with a confidence level of ≥30%. The per-video sensitivity of both experts was 88%, and the sensitivities of the two physicians in training were 84% and 76%. For each reader, the frames with missed polyps appearing on short videos were significantly less than the frames with detected polyps, but no trends were observed regarding polyp size, morphology or color. For full video readings, per-polyp sensitivity was 100% with a per-frame false-positive rate of 1.7%, and per-frame specificity of 98.3%. Conclusions: The sensitivity of CADe to detect small polyps was almost equivalent to experts and superior to physicians in training. A clinical trial using CADe is warranted.

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Adenoma Detection Rate Falls at the End of the Day in a Large Multi-site Sample

Abstract: In our study population, ADR and withdrawal time decrease by roughly 7 and 20%, respectively, by the end of the day. Our results imply that rather than mental or physical fatigue, lower ADR at the end of the day might be driven by endoscopists rushing.

Cited by 22 publications

References 24 publications

Factors Associated with Withdrawal Time in European Colonoscopy Practice: Findings of the European Colonoscopy Quality Investigation (ECQI) Group

Factors Associated with Withdrawal Time in European Colonoscopy Practice: Findings of the European Colonoscopy Quality Investigation (ECQI) Group

Artificial Intelligence in Lower Gastrointestinal Endoscopy: The Current Status and Future Perspective

Polyp detection algorithm can detect small polyps: Ex vivo reading test compared with endoscopists

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