Browsing by Author "Bretthauer, Michael"

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    Combination of Mucosa-Exposure Device and Computer-Aided Detection for Adenoma Detection During Colonoscopy: A Randomized Trial
    (Elsevier, 2023-07) Spadaccini, Marco; Hassan, Cesare; Rondonotti, Emanuele; Antonelli, Giulio; Andrisani, Gianluca; Lollo, Gianluca; Auriemma, Francesco; Iacopini, Federico; Facciorusso, Antonio; Maselli, Roberta; Fugazza, Alessandro; Bambina Bergna, Irene Maria; Cereatti, Fabrizio; Mangiavillano, Benedetto; Radaelli, Franco; Di Matteo, Francesco; Gross, Seth A.; Sharma, Prateek; Mori, Yuichi; Bretthauer, Michael; Rex, Douglas K.; Repici, Alessandro; Medicine, School of Medicine
    Background & Aims Both computer-aided detection (CADe)-assisted and Endocuff-assisted colonoscopy have been found to increase adenoma detection. We investigated the performance of the combination of the 2 tools compared with CADe-assisted colonoscopy alone to detect colorectal neoplasias during colonoscopy in a multicenter randomized trial. Methods Men and women undergoing colonoscopy for colorectal cancer screening, polyp surveillance, or clincial indications at 6 centers in Italy and Switzerland were enrolled. Patients were assigned (1:1) to colonoscopy with the combinations of CADe (GI-Genius; Medtronic) and a mucosal exposure device (Endocuff Vision [ECV]; Olympus) or to CADe-assisted colonoscopy alone (control group). All detected lesions were removed and sent to histopathology for diagnosis. The primary outcome was adenoma detection rate (percentage of patients with at least 1 histologically proven adenoma or carcinoma). Secondary outcomes were adenomas detected per colonoscopy, advanced adenomas and serrated lesions detection rate, the rate of unnecessary polypectomies (polyp resection without histologically proven adenomas), and withdrawal time. Results From July 1, 2021 to May 31, 2022, there were 1316 subjects randomized and eligible for analysis; 660 to the ECV group, 656 to the control group). The adenoma detection rate was significantly higher in the ECV group (49.6%) than in the control group (44.0%) (relative risk, 1.12; 95% CI, 1.00–1.26; P = .04). Adenomas detected per colonoscopy were significantly higher in the ECV group (mean ± SD, 0.94 ± 0.54) than in the control group (0.74 ± 0.21) (incidence rate ratio, 1.26; 95% CI, 1.04–1.54; P = .02). The 2 groups did not differ in term of detection of advanced adenomas and serrated lesions. There was no significant difference between groups in mean ± SD withdrawal time (9.01 ± 2.48 seconds for the ECV group vs 8.96 ± 2.24 seconds for controls; P = .69) or proportion of subjects undergoing unnecessary polypectomies (relative risk, 0.89; 95% CI, 0.69–1.14; P = .38). Conclusions The combination of CADe and ECV during colonoscopy increases adenoma detection rate and adenomas detected per colonoscopy without increasing withdrawal time compared with CADe alone.
  • Thumbnail Image
    Item
    Comparative Performance of Artificial Intelligence Optical Diagnosis Systems for Leaving in Situ Colorectal Polyps
    (Elsevier, 2023-03) Hassan, Cesare; Sharma, Prateek; Mori, Yuichi; Bretthauer, Michael; Rex, Douglas K.; COMBO Study Group; Repici, Alessandro; Medicine, School of Medicine
  • Thumbnail Image
    Item
    Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study
    (Elsevier, 2022) Areia, Miguel; Mori, Yuichi; Correale, Loredana; Repici, Alessandro; Bretthauer, Michael; Sharma, Prateek; Taveira, Filipe; Spadaccini, Marco; Antonelli, Giulio; Ebigbo, Alanna; Kudo, Shin-ei; Arribas, Julia; Barua, Ishita; Kaminski, Michal F.; Messmann, Helmut; Rex, Douglas K.; Dinis-Ribeiro, Mário; Hassan, Cesare; Medicine, School of Medicine
    Background: Artificial intelligence (AI) tools increase detection of precancerous polyps during colonoscopy and might contribute to long-term colorectal cancer prevention. The aim of the study was to investigate the incremental effect of the implementation of AI detection tools in screening colonoscopy on colorectal cancer incidence and mortality, and the cost-effectiveness of such tools. Methods: We conducted Markov model microsimulation of using colonoscopy with and without AI for colorectal cancer screening for individuals at average risk (no personal or family history of colorectal cancer, adenomas, inflammatory bowel disease, or hereditary colorectal cancer syndrome). We ran the microsimulation in a hypothetical cohort of 100 000 individuals in the USA aged 50-100 years. The primary analysis investigated screening colonoscopy with versus without AI every 10 years starting at age 50 years and finishing at age 80 years, with follow-up until age 100 years, assuming 60% screening population uptake. In secondary analyses, we modelled once-in-life screening colonoscopy at age 65 years in adults aged 50-79 years at average risk for colorectal cancer. Post-polypectomy surveillance followed the simplified current guideline. Costs of AI tools and cost for downstream treatment of screening detected disease were estimated with 3% annual discount rates. The main outcome measures included the incremental effect of AI-assisted colonoscopy versus standard (no-AI) colonoscopy on colorectal cancer incidence and mortality, and cost-effectiveness of screening projected for the average risk screening US population. Findings: In the primary analyses, compared with no screening, the relative reduction of colorectal cancer incidence with screening colonoscopy without AI tools was 44·2% and with screening colonoscopy with AI tools was 48·9% (4·8% incremental gain). Compared with no screening, the relative reduction in colorectal cancer mortality with screening colonoscopy with no AI was 48·7% and with screening colonoscopy with AI was 52·3% (3·6% incremental gain). AI detection tools decreased the discounted costs per screened individual from $3400 to $3343 (a saving of $57 per individual). Results were similar in the secondary analyses modelling once-in-life colonoscopy. At the US population level, the implementation of AI detection during screening colonoscopy resulted in yearly additional prevention of 7194 colorectal cancer cases and 2089 related deaths, and a yearly saving of US$290 million. Interpretation: Our findings suggest that implementation of AI detection tools in screening colonoscopy is a cost-saving strategy to further prevent colorectal cancer incidence and mortality.
  • Thumbnail Image
    Item
    Efficacy and Tolerability of High- vs Low-Volume Split-Dose Bowel Cleansing Regimens for Colonoscopy: A Systematic Review and Meta-analysis
    (Elsevier, 2019) Spadaccini, Marco; Frazzoni, Leonardo; Vanella, Giuseppe; East, James; Radaelli, Franco; Spada, Cristiano; Fuccio, Lorenzo; Benamouzig, Robert; Bisschops, Raf; Bretthauer, Michael; Dekker, Evelien; Dinis-Ribeiro, Mario; Ferlitsch, Monika; Gralnek, Ian; Jover, Rodrigo; Kaminski, Michael F.; Pellisé, Maria; Triantafyllou, Konstantinos; Van Hooft, Jeanin E.; Dumonceau, Jean-Marc; Marmo, Clelia; Alfieri, Sergio; Chandrasekar, Viveksandeep Thoguluva; Sharma, Prateek; Rex, Doug K.; Repici, Alessandro; Hassan, Cesare; Medicine, School of Medicine
    Background & Aims Efficacy of bowel preparation is an important determinant of outcomes of colonoscopy. It is not clear whether approved low-volume polyethylene glycol (PEG) and non-PEG regimens are as effective as high-volume PEG regimens when taken in a split dose. Methods In a systematic review of multiple electronic databases through January 31, 2019 with a registered protocol (PROSPERO: CRD42019128067), we identified randomized controlled trials that compared low- vs high-volume bowel cleansing regimens, administered in a split dose, for colonoscopy. The primary efficacy outcome was rate of adequate bowel cleansing, and the secondary efficacy outcome was adenoma detection rate. Primary tolerability outcomes were compliance, tolerability, and willingness to repeat. We calculated relative risk (RR) and 95% CI values and assessed heterogeneity among studies by using the I2 statistic. The overall quality of evidence was assessed using the GRADE framework. Results In an analysis of data from 17 randomized controlled trials, comprising 7528 patients, we found no significant differences in adequacy of bowel cleansing between the low- vs high-volume split-dose regimens (86.1% vs 87.4%; RR, 1.00; 95% CI, 0.98–1.02) and there was minimal heterogeneity (I2 = 17%). There was no significant difference in adenoma detection rate (RR, 0.96; 95% CI, 0.87–1.08) among 4 randomized controlled trials. Compared with high-volume, split-dose regimens, low-volume split-dose regimens had higher odds for compliance or completion (RR, 1.06; 95% CI, 1.02–1.10), tolerability (RR, 1.39; 95% CI, 1.12–1.74), and willingness to repeat bowel preparation (RR, 1.41; 95% CI, 1.20–1.66). The overall quality of evidence was moderate. Conclusions Based on a systematic review of 17 randomized controlled trials, low-volume, split-dose regimens appear to be as effective as high-volume, split-dose regimens in bowel cleansing and are better tolerated, with superior compliance.
  • Thumbnail Image
    Item
    Impact of Artificial Intelligence on Colonoscopy Surveillance After Polyp Removal: A Pooled Analysis of Randomized Trials
    (Elsevier, 2023) Mori, Yuichi; Wang, Pu; Løberg, Magnus; Misawa, Masashi; Repici, Alessandro; Spadaccini, Marco; Correale, Loredana; Antonelli, Giulio; Yu, Honggang; Gong, Dexin; Ishiyama, Misaki; Kudo, Shin-ei; Kamba, Shunsuke; Sumiyama, Kazuki; Saito, Yutaka; Nishino, Haruo; Liu, Peixi; Glissen Brown, Jeremy R.; Mansour, Nabil M.; Gross, Seth A.; Kalager, Mette; Bretthauer, Michael; Rex, Douglas K.; Sharma, Prateek; Berzin, Tyler M.; Hassan, Cesare; Medicine, School of Medicine
    Background and aims: Artificial intelligence (AI) tools aimed at improving polyp detection have been shown to increase the adenoma detection rate during colonoscopy. However, it is unknown how increased polyp detection rates by AI affect the burden of patient surveillance after polyp removal. Methods: We conducted a pooled analysis of 9 randomized controlled trials (5 in China, 2 in Italy, 1 in Japan, and 1 in the United States) comparing colonoscopy with or without AI detection aids. The primary outcome was the proportion of patients recommended to undergo intensive surveillance (ie, 3-year interval). We analyzed intervals for AI and non-AI colonoscopies for the U.S. and European recommendations separately. We estimated proportions by calculating relative risks using the Mantel-Haenszel method. Results: A total of 5796 patients (51% male, mean 53 years of age) were included; 2894 underwent AI-assisted colonoscopy and 2902 non-AI colonoscopy. When following U.S. guidelines, the proportion of patients recommended intensive surveillance increased from 8.4% (95% CI, 7.4%-9.5%) in the non-AI group to 11.3% (95% CI, 10.2%-12.6%) in the AI group (absolute difference, 2.9% [95% CI, 1.4%-4.4%]; risk ratio, 1.35 [95% CI, 1.16-1.57]). When following European guidelines, it increased from 6.1% (95% CI, 5.3%-7.0%) to 7.4% (95% CI, 6.5%-8.4%) (absolute difference, 1.3% [95% CI, 0.01%-2.6%]; risk ratio, 1.22 [95% CI, 1.01-1.47]). Conclusions: The use of AI during colonoscopy increased the proportion of patients requiring intensive colonoscopy surveillance by approximately 35% in the United States and 20% in Europe (absolute increases of 2.9% and 1.3%, respectively). While this may contribute to improved cancer prevention, it significantly adds patient burden and healthcare costs.
  • Thumbnail Image
    Item
    Variability in Adenoma Detection Rate in Control Groups of Randomized Colonoscopy Trials
    (Elsevier, 2022) Hassan, Cesare; Piovani, Daniele; Spadaccini, Marco; Parigi, Tommaso; Khalaf, Kareem; Facciorusso, Antonio; Fugazza, Alessandro; Rösch, Thomas; Bretthauer, Michael; Mori, Yuichi; Sharma, Prateek; Rex, Douglas K.; Bonovas, Stefanos; Repici, Alessandro; Medicine, School of Medicine
    Background: Adenoma Detection Rate (ADR) is still the main surrogate outcome parameter of screening colonoscopy, but most of the studies included mixed indications and basic ADR is quite variable. We therefore looked at the control groups in randomized ADR trials using advanced imaging or mechanical methods to find out whether indications or other factors influence ADR levels. Methods: Patients in the control groups of randomized studies on ADR increase using various methods were collected based on a systematic review; this control group had to use high-definition (HD) white-light endoscopy performed between 2008 and 2021. Random-effects meta-analysis was used to pool ADR in control groups and its 95% confidence interval [CI] according to the following parameters: clinical (indication and demographic), study setting (tandem/parallel, N° centres, sample size), and technical (type of intervention, withdrawal time). Inter-study heterogeneity was reported with I-squared statistic. Multivariable mixed-effects meta-regression was performed for potentially relevant variables. Findings: 25,304 patients from 80 studies in the respective control groups were included. ADR in control arms varied between 8.2% and 68.1% with a high degree of heterogeneity (I2 = 95.1%; random-effect pooled value: 37.5% [34.6‒40.5]). There was no difference in ADR levels between primary colonoscopy screening (12 RCTs, 15%), and mixed indications including screening/surveillance and diagnostic colonoscopy; however, FIT as an indication for colonoscopy was an independent predictor of ADR (OR: 1.6 [1.1‒2.4]). Other well known parameters were confirmed by our analysis such as age (OR: 1.038 [1.004‒1.074]) and sex (male sex: OR: 1.02 [1.01‒1.03) as well withdrawal time (OR: 1.1 [1.0‒1.1). The type of intervention (imaging vs. mechanical) had no influence, but methodological factors did: more recent year of publication and smaller sample size were associated with higher ADR. Interpretation: A high level of variability was found in the level of ADR in the controls of RCTs. With regards to indications, only FIT-based colonoscopy studies influenced basic ADR, primary colonoscopy screening appeared to be similar to other indications. Standardization for variables related to clinical, methodological, and technical parameters is required to achieve generalizability and reproducibility.