Data quality and colonoscopy performance indicators in the prevalent round of a FIT-based colorectal cancer screening program

Sessile serrated polyp detection rates after fecal immunochemical test or multitarget stool DNA test: Systematic review and meta-analysis

Background and study aims Published studies report a higher adenoma detection rate (ADR) for FIT-DNA as compared with FIT. Data are less replete about the performance of stool-based tests for sessile serrated polyp (SSP) detection. We performed a meta-analysis to evaluate the performance of FIT and FIT-DNA testing for SSP detection rate (SSPDR) in patients undergoing colonoscopy for follow up of positive noninvasive tests. Methods A comprehensive literature search of multiple databases (until September 2022) was performed to identify studies reporting SSPDR in patients with positive FIT or FIT-DNA tests. The outcome was overall colonoscopy detection of any SSPs and advanced serrated polyps (ASP: SSP ≥ 10 mm and/or dysplasia). Results Included were 482,405 patients (52.4% females) with a mean age of 62.3 ± 4.4 years from 23 studies. The pooled SSPDR for all positive stool-based tests was 5.3% and higher for FIT-DNA (15.0%, 95% confidence interval [CI] 8.3-25.7) versus FIT (4.1%, 95% CI 3.0-5.6; P = 0.0002). The overall pooled ASP detection rate was 1.4% (95% CI 0.81-2.3) and higher for FIT-DNA (3.8 %, 95% CI 1.7-8.6) compared with FIT (0.71%, 95% CI 0.36-1.4; P <0.01). SSPDR with FIT-DNA was also significantly higher than FIT when the FIT cutoff was >10 ug/g and in FIT-positive patients in studies conducted in North America ( P <0.05). Conclusions FIT-DNA outperformed FIT in both SSP and ASP detection including FIT with a lower threshold cutoff of >10 ug/g. Further comparative studies are needed to assess the impact of our findings on colorectal cancer reduction.

Root-cause Analysis of 762 Danish Post-colonoscopy Colorectal Cancer Patients

BACKGROUND & AIMS

The term post-colonoscopy colorectal cancer (PCCRC) refers to colorectal cancer (CRC) diagnosed after a negative colonoscopy. Using the root-cause algorithm proposed by the World Endoscopy Organization, we aimed to investigate plausible explanations for PCCRCs and potential changes in plausible explanations for PCCRCs over time in a Danish Region.

METHODS

During 1995 to 2021, we used national health registries and electronic medical records in the Central Denmark Region to identify PCCRC cases, defined as CRCs recorded within 6 to 48 months after a colonoscopy. We then applied the World Endoscopy Organization algorithm to categorize explanations for PCCRC as follows: (A) possible missed lesion, prior examination adequate; (B) possible missed lesion, prior examination inadequate; (C) detected lesion, not resected; or (D) likely incomplete resection of previously identified lesion. PCCRCs were identified before (1995-2013) and after (2014-2021) implementation of nationwide fecal immunochemical test-based CRC screening and quality indicators for colonoscopy.

RESULTS

We identified 762 PCCRCs, 53.5% among males and 57% among individuals ≥70 years. Forty-five percent were located in the proximal colon. We identified 616 (80.8%; 95% confidence interval [CI], 74.6%-87.5%) category A PCCRCs; 36 (4.7%; 95% CI, 3.3%-6.5%) category B PCCRCs; 26 (3.4%; 95% CI, 2.2%-4.9%) category C PCCRCs; and 84 (11%; 95% CI, 8.8%-13.6%) category D PCCRCs. Similar patterns were observed during the early (1995-2013) and late (2014-2021) study periods.

CONCLUSIONS

Most PCCRCs originate from possible missed lesions and incompletely resected lesions during the complete study period. These findings indicate the importance of quality assurance of colonoscopy procedures and polypectomy techniques.

Colonoscopy performance monitoring: do we need to adjust for case mix?

INTRODUCTION

Overall caecum intubation rate(oCIR) and overall polyp detection rate(oPDR) have been proposed as performance indicators, but varying complexity in case mix among endoscopists may potentially affect validity. The study aims to explore the effect of adjusting for case mix on individual endoscopist performance by calculating case mix-adjusted performance estimates (cmCIR and cmPDR) and comparing them to overall performance estimates (oCIR and oPDR). The study also provides an R program for case mix analysis.

METHODS

Logistic regression associated endoscopist, colonoscopy indication, patient age and patient gender with the binary outcomes of cecum intubation and polyp detection. Case mix-adjusted performance indicators were calculated for each endoscopist based on logistic regression and bootstraps. Endoscopists were ranked from best to worst by overall and case mix-adjusted performance estimates, and differences were evaluated using percentage points(pp) and rank changes.

RESULTS

The dataset consisted of 7376 colonoscopies performed by 47 endoscopists. The maximum rank change for an endoscopist comparing oCIR and cmCIR was eight positions, interquartile range (IQR 1-3). The maximum change in CIR was 1.95 percentage point (pp) (IQR 0.27-0.86). The maximum rank change in the oPDR versus cmPDR analysis was 17 positions (IQR 1.5-8.5). The maximum change in PDR was 11.21 pp (IQR 2.05-6.70). Three endoscopists improved their performance from significantly inferior to within the 95% confidence interval (CI) range of performance targets using case mix-adjusted estimates.

CONCLUSIONS

The majority of endoscopists were unaffected by adjustment for case mix, but a few unfortunate endoscopists had an unfavourable case mix that could invite incorrect suspicion of inferior performance.

Colonoscopy may be weak link in organised colorectal cancer screening programme with faecal immunochemical test

OBJECTIVE

To evaluate the quality of colonoscopies performed after a positive faecal immunochemical test in the French colorectal cancer screening programme.

METHODS

Retrospective analysis of all colonoscopies performed between 2015 and 2019 after a positive quantitative faecal immunochemical test in the population-based colorectal cancer screening programme organised in Alsace, part of the French programme. The following indicators were evaluated: annual colonoscopy volume, caecal intubation rate, adenoma detection rate, proximal serrated lesion detection rate and proportion of patients referred directly to surgery for benign polyp management. Endoscopists who performed <30 faecal immunochemical test positive colonoscopies were non-assessable.

RESULTS

Overall, 13,455 faecal immunochemical test-positive colonoscopies performed by 116 community gastroenterologists were included, 13,067 of them by 80 assessable endoscopists. The overall caecal intubation, adenoma detection and proximal serrated lesion detection rates were 97.9%, 57.6% and 7.6%, respectively. They were <90%, <45% and <1% for 1.3%, 12.5% and 6.3% of the endoscopists, respectively. Overall, 1028 (7.9%) individuals were examined by 13 low-performing endoscopists and 328 (2.4%) individuals by 33 low-volume non-assessable endoscopists. Among 9133 individuals harbouring polyps, 155 (1.7%) had unwarranted surgery for a benign polyp. Overall, 1487 individuals (11.1%; 95% confidence interval 10.5-11.6) were not given the best possible chances, whereas 5545 individuals (41.2%; 95% confidence interval 40.4-42.0) were offered the best possible chances by 37 endoscopists.

CONCLUSIONS

At programme level, the key performance indicators evaluated largely exceeded the target standards. At individual level, at least one in nine individuals was not given the best possible chances during faecal immunochemical test-positive colonoscopies by a minority of poor-performing and/or low-volume endoscopists.

Automated detection of cecal intubation with variable bowel preparation using a deep convolutional neural network

Background and study aims  Colonoscopy completion reduces post-colonoscopy colorectal cancer. As a result, there have been attempts at implementing artificial intelligence to automate the detection of the appendiceal orifice (AO) for quality assurance. However, the utilization of these algorithms has not been demonstrated in suboptimal conditions, including variable bowel preparation. We present an automated computer-assisted method using a deep convolutional neural network to detect the AO irrespective of bowel preparation. Methods  A total of 13,222 images (6,663 AO and 1,322 non-AO) were extracted from 35 colonoscopy videos recorded between 2015 and 2018. The images were labelled with Boston Bowel Preparation Scale scores. A total of 11,900 images were used for training/validation and 1,322 for testing. We developed a convolutional neural network (CNN) with a DenseNet architecture pre-trained on ImageNet as a feature extractor on our data and trained a classifier uniquely tailored for identification of AO and non-AO images using binary cross entropy loss. Results  The deep convolutional neural network was able to correctly classify the AO and non-AO images with an accuracy of 94 %. The area under the receiver operating curve of this neural network was 0.98. The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were 0.96, 0.92, 0.92 and 0.96, respectively. AO detection was > 95 % regardless of BBPS scores, while non-AO detection improved from BBPS 1 score (83.95 %) to BBPS 3 score (98.28 %). Conclusions  A deep convolutional neural network was created demonstrating excellent discrimination between AO from non-AO images despite variable bowel preparation. This algorithm will require further testing to ascertain its effectiveness in real-time colonoscopy.