scholarly journals Anal Neoplasia in Inflammatory Bowel Disease: Classification Proposal, Epidemiology, Carcinogenesis, and Risk Management Perspectives

2017 ◽  
Vol 11 (8) ◽  
pp. 1011-1018 ◽  
Author(s):  
Andrew Wisniewski ◽  
Jean-Francois Fléjou ◽  
Laurent Siproudhis ◽  
Laurent Abramowitz ◽  
Magali Svrcek ◽  
...  
Keyword(s):  
Inflammatory Bowel Disease ◽  
Risk Management ◽  
Bowel Disease ◽  
Disease Classification ◽  
Anal Neoplasia ◽  
Inflammatory Bowel

N04 Knowledge, attitude and perception of inflammatory bowel disease patients towards colorectal cancer risk, its management and the role of healthcare providers: a cross-sectional study in the UK

2020 ◽  
Vol 14 (Supplement_1) ◽  
pp. S659-S659
Author(s):  
F Khan ◽  
W Czuber-Dochan ◽  
C Norton
Keyword(s):  
Colorectal Cancer ◽  
Inflammatory Bowel Disease ◽  
Risk Management ◽  
Cancer Risk ◽  
Bowel Disease ◽  
Initiation Time ◽  
Cross Sectional ◽  
Early Cancer Diagnosis ◽  
Inflammatory Bowel

Abstract Background Inflammatory bowel disease (IBD) increases the risk of colorectal cancer (CRC) and requires specialised cancer risk management. Although literature exists on general disease-related knowledge in IBD patients, limited studies have assessed IBD patients’ knowledge of CRC risk and its management. Consequently, patient perception of the role of a healthcare provider (HCP) in patient education of CRC risk and their attitude towards recommended risk management has not been assessed in UK IBD patients. Methods We conducted a cross-sectional online survey with IBD patients recruited via charity sources from April-July 2019. Adult patients (>18 years) with a confirmed diagnosis of IBD for 2 years and adequate command of English language were included. A self-designed and piloted questionnaire with open and closed-ended questions was used. Closed-ended data were analysed using descriptive statistics and open-ended responses were analysed using content analysis. Fischer’s exact test and bivariate logistic regression were used to test for association between knowledge and patient demographics. Results 92 participants, including 52.5% CD and 67.5% females, responded. 88% knew that IBD increases CRC risk. The mean fear of CRC risk (0–10 visual analogue scale) was 6.37 (SD ± 2.8). One-fifth were aware of colonoscopy as the best screening tool; 88% were unaware of screening initiation time. 90% would agree to their doctor’s recommendation of colonoscopy to ensure early cancer diagnosis and treatment. For dysplasia with 10% risk of CRC, 46.7% would not agree to colectomy mainly due to 10% risk of CRC not being high enough to undergo surgery. Forty-eight per cent of participants said that they never had a discussion about increased CRC risk in IBD with their doctor. Almost two-thirds were not informed about the role of screening/surveillance in cancer. Two-thirds were satisfied with the information provided by their HCP. Overall, patients desired more information about their individualised cancer risk and services available for managing the increased risk. Conclusion IBD patients are well informed about IBD-associated CRC risk, feared this risk greatly but were poorly aware of screening initiation time. HCP’s role in cancer knowledge dissemination was sub-optimal and patients desired more knowledge. We need deeper understanding of patients’ educational needs related to CRC.

scholarly journals Anal Neoplasia in Inflammatory Bowel Disease Is Associated With HPV and Perianal Disease

2016 ◽  
Vol 7 (3) ◽  
pp. e148 ◽  
Author(s):  
Joannie Ruel ◽  
Huaibin Mabel Ko ◽  
Giulia Roda ◽  
Ninad Patil ◽  
David Zhang ◽  
...  
Keyword(s):  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Perianal Disease ◽  
Anal Neoplasia ◽  
Inflammatory Bowel

Effect of inflammatory bowel disease classification changes on NOD2 genotype–phenotype associations in a population-based cohort

2007 ◽  
Vol 13 (10) ◽  
pp. 1220-1227 ◽  
Author(s):  
Richard B. Gearry ◽  
Rebecca L. Roberts ◽  
Michael J. Burt ◽  
Chris M.A. Frampton ◽  
Bruce A. Chapman ◽  
...  
Keyword(s):  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Population Based ◽  
Disease Classification ◽  
Inflammatory Bowel

Artificial intelligence and inflammatory bowel disease: practicalities and future prospects

2021 ◽  
pp. flgastro-2021-102003
Author(s):  
Johanne Brooks-Warburton ◽  
James Ashton ◽  
Anjan Dhar ◽  
Tony Tham ◽  
Patrick B Allen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Inflammatory Bowel Disease ◽  
Risk Stratification ◽  
Bowel Disease ◽  
Emerging Technology ◽  
Disease Classification ◽  
Future Prospects ◽  
Patient Journey ◽  
Self Monitoring ◽  
Inflammatory Bowel

Artificial intelligence (AI) is an emerging technology predicted to have significant applications in healthcare. This review highlights AI applications that impact the patient journey in inflammatory bowel disease (IBD), from genomics to endoscopic applications in disease classification, stratification and self-monitoring to risk stratification for personalised management. We discuss the practical AI applications currently in use while giving a balanced view of concerns and pitfalls and look to the future with the potential of where AI can provide significant value to the care of the patient with IBD.

scholarly journals Inflammatory Bowel Disease and Thrombosis: A National Inpatient Sample Study

TH Open ◽  
2020 ◽  
Vol 04 (01) ◽  
pp. e51-e58
Author(s):  
Jessica B. Cohen ◽  
Diane M. Comer ◽  
Jonathan G. Yabes ◽  
Margaret V. Ragni
Keyword(s):  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Severity Of Illness ◽  
Disease Classification ◽  
Continuous Variables ◽  
National Inpatient Sample ◽  
Chi Square ◽  
Discharge Data ◽  
Thrombotic Risk ◽  
Inflammatory Bowel

Abstract Introduction Thrombosis is more common in inflammatory bowel disease (IBD) patients than the general population, but disease-specific correlates of thrombosis remain unclear. Methods We performed a retrospective analysis of discharge data from the National Inpatient Sample between 2009 and 2014, using International Disease Classification codes to identify IBD and non-IBD patients with or without thrombosis. We used NIS-provided discharge-level weights to reflect prevalence estimates. Categoric variables were analyzed by Rao-Scott Chi-square test, continuous variables by weighted simple linear regression, and covariates associated with thrombosis by weighted multivariable logistic regression. Results Thrombosis prevalence in IBD was significantly greater than in non-IBD, 7.52 versus 4.54%, p < 0.0001. IBD patients with thrombosis were older and more likely to be Caucasian than IBD without thrombosis, each p < 0.001. Thrombosis occurred most commonly in the mesenteric vein. Thrombotic risk factors in IBD include surgery, ports, malignancy, dehydration, malnutrition, and steroids at 53.7, 13.2, 13.1, 12.4, 8.9, and 8.2%, respectively. Those with thrombosis had greater severity of illness, 1.42 versus 0.96; length of stay, 7.7 versus 5.5 days; and mortality, 3.8 versus 1.5%; all p < 0.0001. Adjusting for age and comorbidity, odds ratios for predictors of thrombosis included ports, steroids, malnutrition, and malignancy at 1.73, 1.61, 1.34, and 1.13, respectively, while Asian race, 0.61, was protective, each p < 0.001. Conclusion Thrombosis prevalence is 1.7-fold greater in IBD than non-IBD patients. Adjusting for age and comorbidity, the odds ratio for thrombosis in IBD was 73% higher with ports, 61% higher with steroids, 34% with malnutrition, and 13% with malignancy. Whether long-term anticoagulation would benefit the latter is unknown.

Su1287 Anal Neoplasia in Inflammatory Bowel Disease Is Associated With HPV and Perianal Disease

2015 ◽  
Vol 148 (4) ◽  
pp. S-462
Author(s):  
Huaibin M. Ko ◽  
Joannie Ruel ◽  
Ninad Patil ◽  
Giulia Roda ◽  
David Zhang ◽  
...  
Keyword(s):  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Perianal Disease ◽  
Anal Neoplasia ◽  
Inflammatory Bowel

INFLAMMATORY BOWEL DISEASE CLASSIFICATION USING THE GUT MICROBIOME: A BENCHMARK OF MICROBIAL DATA ANALYSIS METHODS

2021 ◽  
Vol 27 (Supplement_1) ◽  
pp. S40-S40
Author(s):  
Ryszard Kubinski ◽  
Jean Djamen ◽  
Timur Zhanabaev ◽  
Ryan Martin
Keyword(s):  
Machine Learning ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Empirical Bayes ◽  
Data Preprocessing ◽  
Disease Classification ◽  
Diagnostic Tools ◽  
Non Invasive ◽  
Inflammatory Bowel ◽  
Log Ratio

Abstract The prevalence of inflammatory bowel disease (IBD) is increasing throughout the developed world. For the newly diagnosed, the time between the appearance of symptoms and diagnosis can take months, involving invasive procedures. There is an urgent need to develop a simple, low cost, accurate and non-invasive diagnostic test. With decreasing costs of next-generation sequencing, many studies have compared IBD gut microbiomes to healthy controls, successfully identifying bacterial biomarkers for IBD. Unfortunately, a majority of these studies utilize machine learning and statistical methods on either single or low-sample size datasets. This results in the creation of disease classification models that have a high level of overfitting and therefore minimal clinical application to new patient cohorts. There are several data preprocessing methods available for data normalization and reduction of cohort specific signals (batch reduction) which can address this lack of cross-dataset performance. With an abundance of potential methods, there is a need to benchmark the performance and generalizability of various machine learning pipelines (combination of data preprocessing and model) for microbiome-based IBD diagnostic tools. We used a collection of 12 IBD-associated North American microbiome datasets (~4000 samples) to benchmark several machine learning pipelines. Raw sequencing data was processed, collapsed at the OTU or Genus level and merged using QIIME2. Datasets were then normalized using either sum-scaling or log based methods and batch reduction was performed using either zero-centering or Empirical Bayes’ approaches. Performance of pipelines was evaluated using binary accuracy, AUC, F1 metric and MCC score. Generalizability of pipelines was evaluated using leave one out cross validation, where data from one study was left out of the training set and tested upon. The best performing and most generalizable pipeline included a Random Forest model paired with centered log ratio based normalization and batch reduction via an Empirical Bayes’ based approach. This combination, along with others, showed equivalent or higher performance to that of more complex models involving deep neural networks (DNNs). In addition to benchmarking our pipelines, we also explore their limitations, such as the tendency of zero-centered batch reduction to rely on balanced data as input or the tendency of Empirical Bayes’ based methods to introduce artificial signals into data, evidencing certain methods as poor tools for clinical use. To our knowledge, this is the first comprehensive benchmark of data preprocessing and machine learning methods for microbiome-based disease classification of IBD. These findings will help improve the generalizability of machine learning models as we move towards non-invasive diagnostic and disease management tools for patients with IBD.

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