scholarly journals Breath Analysis Using eNose and Ion Mobility Technology to Diagnose Inflammatory Bowel Disease—A Pilot Study

Biosensors ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 55 ◽  
Author(s):  
Tiele ◽  
Wicaksono ◽  
Kansara ◽  
Arasaradnam ◽  
Covington
Keyword(s):  
Inflammatory Bowel Disease ◽  
Pilot Study ◽  
Ion Mobility ◽  
Bowel Disease ◽  
Breath Analysis ◽  
Diagnostic Tools ◽  
Exhaled Breath ◽  
Faecal Calprotectin ◽  
Non Invasive ◽  
Inflammatory Bowel

Early diagnosis of inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), remains a clinical challenge with current tests being invasive and costly. The analysis of volatile organic compounds (VOCs) in exhaled breath and biomarkers in stool (faecal calprotectin (FCP)) show increasing potential as non-invasive diagnostic tools. The aim of this pilot study is to evaluate the efficacy of breath analysis and determine if FCP can be used as an additional non-invasive parameter to supplement breath results, for the diagnosis of IBD. Thirty-nine subjects were recruited (14 CD, 16 UC, 9 controls). Breath samples were analysed using an in-house built electronic nose (Wolf eNose) and commercial gas chromatograph–ion mobility spectrometer (G.A.S. BreathSpec GC-IMS). Both technologies could consistently separate IBD and controls [AUC ± 95%, sensitivity, specificity], eNose: [0.81, 0.67, 0.89]; GC-IMS: [0.93, 0.87, 0.89]. Furthermore, we could separate CD from UC, eNose: [0.88, 0.71, 0.88]; GC-IMS: [0.71, 0.86, 0.62]. Including FCP did not improve distinction between CD vs UC; eNose: [0.74, 1.00, 0.56], but rather, improved separation of CD vs controls and UC vs controls; eNose: [0.77, 0.55, 1.00] and [0.72, 0.89, 0.67] without FCP, [0.81, 0.73, 0.78] and [0.90, 1.00, 0.78] with FCP, respectively. These results confirm the utility of breath analysis to distinguish between IBD-related diagnostic groups. FCP does not add significant diagnostic value to breath analysis within this study.

P0702 CLINICAL UTILITY OF NON-INVASIVE DIAGNOSTIC TOOLS IN CHILDREN WITH SUSPECTED INFLAMMATORY BOWEL DISEASE

2004 ◽  
Vol 39 (Supplement 1) ◽  
pp. S324 ◽  
Author(s):  
R. Berni Canani ◽  
L. Tanturri de Horatio ◽  
M. T. Romano ◽  
F. Manguso ◽  
L. Rapacciuolo ◽  
...  
Keyword(s):  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Clinical Utility ◽  
Diagnostic Tools ◽  
Non Invasive ◽  
Inflammatory Bowel

Exhaled Breath Analysis for Assessment of IBD (Inflammatory Bowel Disease) Based on Electronic Nose System

2020 ◽  
Vol MA2020-01 (34) ◽  
pp. 2407-2407
Author(s):  
Hyung-Gi Byun ◽  
Joon-Bu Yu ◽  
Chong-Yun Kang ◽  
Yoo-Jin Lee ◽  
Byung-Kuk Jang ◽  
...  
Keyword(s):  
Inflammatory Bowel Disease ◽  
Electronic Nose ◽  
Bowel Disease ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis ◽  
Inflammatory Bowel ◽  
Electronic Nose System

scholarly journals Analysis of Exhaled Breath Volatile Organic Compounds in Inflammatory Bowel Disease: A Pilot Study

2015 ◽  
Vol 9 (9) ◽  
pp. 731-737 ◽  
Author(s):  
Lucy C. Hicks ◽  
Juzheng Huang ◽  
Sacheen Kumar ◽  
Sam T. Powles ◽  
Timothy R. Orchard ◽  
...  
Keyword(s):  
Inflammatory Bowel Disease ◽  
Pilot Study ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Bowel Disease ◽  
Exhaled Breath ◽  
Volatile Organic ◽  
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.

scholarly journals Utility of routinely collected electronic health records data to support effectiveness evaluations in inflammatory bowel disease: a pilot study of tofacitinib

2021 ◽  
Vol 28 (1) ◽  
pp. e100337
Author(s):  
Vivek Ashok Rudrapatna ◽  
Benjamin Scott Glicksberg ◽  
Atul Janardhan Butte
Keyword(s):  
Inflammatory Bowel Disease ◽  
Pilot Study ◽  
Disease Activity ◽  
Real World ◽  
Bowel Disease ◽  
P Value ◽  
Health Records ◽  
Real World Evidence ◽  
Inflammatory Bowel

ObjectivesElectronic health records (EHR) are receiving growing attention from regulators, biopharmaceuticals and payors as a potential source of real-world evidence. However, their suitability for the study of diseases with complex activity measures is unclear. We sought to evaluate the use of EHR data for estimating treatment effectiveness in inflammatory bowel disease (IBD), using tofacitinib as a use case.MethodsRecords from the University of California, San Francisco (6/2012 to 4/2019) were queried to identify tofacitinib-treated IBD patients. Disease activity variables at baseline and follow-up were manually abstracted according to a preregistered protocol. The proportion of patients meeting the endpoints of recent randomised trials in ulcerative colitis (UC) and Crohn’s disease (CD) was assessed.Results86 patients initiated tofacitinib. Baseline characteristics of the real-world and trial cohorts were similar, except for universal failure of tumour necrosis factor inhibitors in the former. 54% (UC) and 62% (CD) of patients had complete capture of disease activity at baseline (month −6 to 0), while only 32% (UC) and 69% (CD) of patients had complete follow-up data (month 2 to 8). Using data imputation, we estimated the proportion achieving the trial primary endpoints as being similar to the published estimates for both UC (16%, p value=0.5) and CD (38%, p-value=0.8).Discussion/ConclusionThis pilot study reproduced trial-based estimates of tofacitinib efficacy despite its use in a different cohort but revealed substantial missingness in routinely collected data. Future work is needed to strengthen EHR data and enable real-world evidence in complex diseases like IBD.

Use of faecal immunochemical testing as an alternative to faecal calprotectin in children

2021 ◽  
pp. 000456322198935
Author(s):  
Kirn Sandhu ◽  
Sandhia Naik ◽  
Ruth M Ayling
Keyword(s):  
Bowel Disease ◽  
Intestinal Inflammation ◽  
Paediatric Population ◽  
Mucosal Healing ◽  
Faecal Calprotectin ◽  
Non Invasive ◽  
Three Samples ◽  
Immunochemical Test ◽  
Inflammatory Bowel ◽  
Rule Out

Background Faecal calprotectin has been widely used as a non-invasive marker of intestinal inflammation in children. Measurement of faecal haemoglobin using faecal immunochemical test is well established in adults for detection of colorectal cancer. In adults, faecal haemoglobin has been recommended as a reliable tool to aid identification of those at low risk of significant bowel disease and has also been used in inflammatory bowel disease to assess mucosal healing. Aims We aimed to evaluate the performance of faecal haemoglobin in the paediatric population and compare it with faecal calprotectin. Methods Children being assessed in the paediatric gastroenterology clinic for bowel symptoms had a sample sent for both faecal calprotectin and faecal haemoglobin. Samples were collected over a 10-month period from November 2018 to September 2019. Faecal haemoglobin was measured using an OC-Sensor. Faecal calprotectin was measured using Liason®Calprotectin. Results One hundred forty three samples were returned for faecal haemoglobin and in 107 a paired faecal calprotectin was also available. Faecal haemoglobin correlated with faecal calprotectin, Spearman’s rank coefficient 0.656 ( P < 0.0001). There were 35 patients with faecal haemoglobin >20 μg/g and in 32 of these patients faecal calprotectin was >200 μg/g; 74 patients with faecal haemoglobin and 38 patients with faecal calprotectin underwent colonoscopy. Patients with normal histology had faecal haemoglobin <4 μg/g; faecal haemoglobin >20 µg/g was associated with signification inflammation Conclusion Our study is the first to compare faecal haemoglobin and faecal calprotectin in a paediatric population. Results suggest that faecal haemoglobin correlates with faecal calprotectin and, as in adults, may be useful to rule out significant bowel disease. A faecal haemoglobin >20 μg/g was consistent with significant histological inflammation.

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