Sensor drift is a well-known disadvantage of electronic nose (eNose) technology and may affect the accuracy of diagnostic algorithms. Correction for this phenomenon is not routinely performed. The aim of this study was to investigate the influence of eNose sensor drift on the development of a disease-specific algorithm in a real-life cohort of inflammatory bowel disease patients (IBD). In this multi-center cohort, patients undergoing colonoscopy collected a fecal sample prior to bowel lavage. Mucosal disease activity was assessed based on endoscopy. Controls underwent colonoscopy for various reasons and had no endoscopic abnormalities. Fecal eNose profiles were measured using Cyranose 320^®. Fecal samples of 63 IBD patients and 63 controls were measured on four subsequent days. Sensor data displayed associations with date of measurement, which was reproducible across all samples irrespective of disease state, disease activity state, disease localization and diet of participants. Based on logistic regression, corrections for sensor drift improved accuracy to differentiate between IBD patients and controls based on the significant differences of six sensors (p = 0.004; p < 0.001; p = 0.001; p = 0.028; p < 0.001 and p = 0.005) with an accuracy of 0.68. In this clinical study, short-term sensor drift affected fecal eNose profiles more profoundly than clinical features. These outcomes emphasize the importance of sensor drift correction to improve reliability and repeatability, both within and across eNose studies.

Screening for colorectal cancer (CRC) reduces its mortality but has limited sensitivity and specificity. Aims We aimed to explore potential biomarker panels for CRC and adenoma detection and to gai...

Summary: Background: Faecal immunochemical test (FIT) is emerging as a valid test to rule‐out the presence of colorectal cancer (CRC). However, the accuracy of FIT is dependent on the cut‐off applied. An additional low‐cost test could improve further detection of CRC.Aims: To evaluate the efficacy of combined FIT and volatile organic compounds (VOC) in the detection of CRC within symptomatic populations.Methods: Systematic reviews on the diagnostic accuracy of FIT and VOC, for the detection of CRC, were updated. Meta‐analyses were performed adopting a bivariate model for sensitivity and specificity. Clinical utility of combined FIT and VOC was estimated using Fagan's nomogram. Post‐test probability of FIT negatives was used as a pre‐test probability for VOC.Results: The pooled sensitivity and specificity of FIT at 10 µg/g faeces, for the detection of CRC, were 0.914 (95% confidence interval [CI] = 0.894‐0.936) and 0.783 (CI = 0.850‐0.696), respectively. For VOC, the sensitivity was 0.837 (CI = 0.781‐0.881) and the specificity was 0.803 (CI = 0.870‐0.712). The area under the curve for FIT and VOC were 0.926 and 0.885, respectively. In a population with 5% CRC prevalence, the estimated probability of having CRC following a negative FIT was 0.5% and following both negative FIT and VOC was 0.1%.Conclusions: In a FIT‐negative symptomatic population, VOC can be a good test to rule‐out the presence of CRC. The estimated probability reduction by 0.4% when both tests being negative offers adequate safety netting in primary care for the exclusion of CRC. The number needed to colonoscope to identify one CRC is eight if either FIT or VOC positive. Cost‐effectiveness and clinical accuracy of this approach will need further evaluation.

Introduction : Fecal volatile organic compounds (VOCs) are gaseous metabolic products which are increasingly considered potential non-invasive biomarkers for the detection of various (gastrointestinal) diseases. The influence of lifestyle factors on fecal VOC patterns remains unexplored but is of importance prior to implementation of VOC analysis as a diagnostic tool. The aim of this study was to investigate the effects of age, gender, body mass index, smoking status, dietary preferences, medication use and co-morbidity on fecal VOC patterns. Methods : For this study, fecal samples of patients undergoing a colonoscopy were collected prior to endoscopy. All participants completed a questionnaire on lifestyle factors, co-morbidity and medication use. Patients without colonic abnormalities were included in this study. Fecal VOC patterns were analyzed by means of an electronic nose (eNose) device (Cyranose ^® 320). Results : From the 1039 participants willing to participate in the initial study, 211 were eligible as controls. All unique lifestyle variables investigated in this study affected the fecal VOC composition. The strongest influences were caused by low BMI, a vegetarian diet and an active smoking status, whereas the least influence was found for the variables gender, age > 55 years and previous smokers. Discussion : Age, gender, BMI, smoking habits, dietary preferences, co-morbidity and medication use all have unique effects on fecal VOC composition. Future studies should carefully consider this influence on VOC outcome when defining VOC signatures as biomarker for diagnostic purposes.