Measuring the observer (Hawthorne) effect on adenoma detection rates.

Background and study aims An independent observer can improve procedural quality. We evaluated the impact of the observer (Hawthorne effect) on important quality metrics during colonoscopies. Patients and Methods In a single-center comparative study, consecutive patients undergoing routine screening or diagnostic colonoscopy were prospectively enrolled. In the index group, all procedural steps and quality metrics were observed and documented, and the procedure was video recorded by an independent research assistant. In the reference group, colonoscopies were performed without independent observation. Colonoscopy quality metrics such as polyp, adenoma, serrated lesions, and advanced adenoma detection rates (PDR, ADR, SLDR, AADR) were compared. The probabilities of increased quality metrics were evaluated through regression analyses weighted by the inversed probability of observation during the procedure. Results We included 327 index individuals and 360 referents in the final analyses. The index group had significantly higher PDRs (62.4% vs. 53.1%, P =0.02) and ADRs (39.4% vs. 28.3%, P =0.002) compared with the reference group. The SLDR and AADR were not significantly increased. After adjusting for potential confounders, the ADR and SLDR were 50% (relative risk [RR] 1.51; 95%, CI 1.05-2.17) and more than twofold (RR 2.17; 95%, CI 1.05-4.47) more likely to be higher in the index group than in the reference group. Conclusions The presence of an independent observer documenting colonoscopy quality metrics and video recording the colonoscopy resulted in a significant increase in ADR and other quality metrics. The Hawthorne effect should be considered an alternative strategy to advanced devices to improve colonoscopy quality in practice.

Automated Detection of Anatomical Landmarks During Colonoscopy Using a Deep Learning Model.

Background and aims: Identification and photo-documentation of the ileocecal valve (ICV) and appendiceal orifice (AO) confirm completeness of colonoscopy examinations. We aimed to develop and test a deep convolutional neural network (DCNN) model that can automatically identify ICV and AO, and differentiate these landmarks from normal mucosa and colorectal polyps. Methods: We prospectively collected annotated full-length colonoscopy videos of 318 patients undergoing outpatient colonoscopies. We created three nonoverlapping training, validation, and test data sets with 25,444 unaltered frames extracted from the colonoscopy videos showing four landmarks/image classes (AO, ICV, normal mucosa, and polyps). A DCNN classification model was developed, validated, and tested in separate data sets of images containing the four different landmarks. Results: After training and validation, the DCNN model could identify both AO and ICV in 18 out of 21 patients (85.7%). The accuracy of the model for differentiating AO from normal mucosa, and ICV from normal mucosa were 86.4% (95% CI 84.1% to 88.5%), and 86.4% (95% CI 84.1% to 88.6%), respectively. Furthermore, the accuracy of the model for differentiating polyps from normal mucosa was 88.6% (95% CI 86.6% to 90.3%). Conclusion: This model offers a novel tool to assist endoscopists with automated identification of AO and ICV during colonoscopy. The model can reliably distinguish these anatomical landmarks from normal mucosa and colorectal polyps. It can be implemented into automated colonoscopy report generation, photo-documentation, and quality auditing solutions to improve colonoscopy reporting quality.

The location-based resect and discard strategy for diminutive colorectal polyps: a prospective clinical study.

BACKGROUND: Clinical implementation of the resect-and-discard strategy has been difficult because optical diagnosis is highly operator dependent. This prospective study aimed to evaluate a resect-and-discard strategy that is not operator dependent. METHODS: The study evaluated a resect-and-discard strategy that uses the anatomical polyp location to classify colonic polyps into non-neoplastic or low risk neoplastic. All rectosigmoid diminutive polyps were considered hyperplastic and all polyps located proximally to the sigmoid colon were considered neoplastic. Surveillance interval assignments based on these a priori assumptions were compared with those based on actual pathology results and on optical diagnosis. The primary outcome was ≥ 90 % agreement with pathology in surveillance interval assignment. RESULTS: 1117 patients undergoing complete colonoscopy were included and 482 (43.1 %) had at least one diminutive polyp. Surveillance interval agreement between the location-based strategy and pathological findings using the 2020 US Multi-Society Task Force guideline was 97.0 % (95 % confidence interval [CI] 0.96-0.98), surpassing the ≥ 90 % benchmark. Optical diagnoses using the NICE and Sano classifications reached 89.1 % and 90.01 % agreement, respectively (P < 0.001), and were inferior to the location-based strategy. The location-based resect-and-discard strategy allowed a 69.7 % (95 %CI 0.67-0.72) reduction in pathology examinations compared with 55.3 % (95 %CI 0.52-0.58; NICE and Sano) and 41.9 % (95 %CI 0.39-0.45; WASP) with optical diagnosis. CONCLUSION: The location-based resect-and-discard strategy achieved very high surveillance interval agreement with pathology-based surveillance interval assignment, surpassing the ≥ 90 % benchmark and outperforming optical diagnosis in surveillance interval agreement and the number of pathology examinations avoided.

Evaluation of the polyp-based resect and discard strategy: a retrospective study.

BACKGROUND: Standard colonoscopy practice requires removal and histological characterization of almost all detected small (< 10 mm) and diminutive (≤ 5 mm) colorectal polyps. This study aimed to test a simplified polyp-based resect and discard (PBRD) strategy that assigns surveillance intervals based only on size and number of small/diminutive polyps, without the need for pathology examination. METHODS: A post hoc analysis was performed on patients enrolled in a prospective study. The primary outcome was surveillance interval agreement of the PBRD strategy with pathology-based management according to 2020 US Multi-Society Task Force guidelines. Chart analysis also evaluated clinician adherence to pathology-based recommendations. One-sided testing was performed with a null-hypothesis of 90 % agreement with pathology-based surveillance intervals and a two-sided 96.7 % confidence interval (CI) using correction for multiple testing. RESULTS: 452 patients were included in the study. Surveillance intervals assigned using the PBRD strategy were correct in 97.8 % (96.7 %CI 96.3-99.3 %) of patients compared with pathology-based management. The PBRD strategy reduced pathology examinations by 58.7 % while providing 87.8 % of patients with immediate surveillance interval recommendations on the day of colonoscopy, compared with 47.1 % when using pathology-based management. Chart analysis of surveillance interval assignments showed 63.3 % adherence to pathology-based guidelines. CONCLUSION: The PBRD strategy surpassed the 90 % agreement with the pathology-based standard for determining surveillance interval, reduced the need for pathology examinations, and increased the proportion of patients receiving immediate surveillance interval recommendations. The PBRD strategy does not require expertise in optical diagnosis and may replace histological characterization of small and diminutive colorectal polyps.