Electronic Nose Sensor Drift Affects Diagnostic Reliability and Accuracy of Disease-Specific Algorithms
<p>Association between sensors. Spearman correlation of sensors. Sensors are depicted on both the X-axis and Y-axis. A strong positive correlation is seen amongst the majority of sensors.</p> "> Figure 2
<p>Scaled heatmap of sensor output before correction for date of measurement. The mean and standard deviation per sensor across all samples were calculated. Then, each sample measurement was subtracted from the mean sensor outcome and divided by its standard deviation. This was performed across all sensors and samples so that all sensor measurements were on the same scale. Samples are depicted on the Y-axis, while sensors are depicted on the X-axis. Colored strips on the Y-axis depict the distribution of sample characteristics amongst the samples. (<b>A</b>) Distribution of CD, UC and controls. (<b>B</b>) Distribution of active disease or remission; control samples are depicted as separate group. (<b>C</b>) Distribution of diet including no diet, vegetarian diet, gluten-free diet, lactose-free diet and non-specified diets. (<b>D</b>) Distribution of abdominal surgical history except appendectomy, including no abdominal surgery, ileocecal surgery and colectomy; control samples are depicted as separate group. (<b>E</b>) Distribution of measurement date.</p> "> Figure 3
<p>Scaled heatmap of sensor output after correction for date of measurement. Scaled heatmaps of the sensor output. Date of sample measurement was found a confounding factor. Therefore, a correction was applied for date of measurement for all sensor outcomes. Then, the mean and standard deviation per sensor across all samples were calculated. Each sample measurement was subtracted from the mean sensor outcome and divided by its standard deviation. This was across all sensors and samples so that all sensor measurements were on the same scale. Samples are depicted on the Y-axis, while sensors are depicted on the X-axis. Colored strips on the Y-axis depict the distribution of sample characteristics amongst the samples. (<b>A</b>) Distribution of measurement date. (<b>B</b>) Distribution of CD, UC patients and controls. The six discriminating sensors are indicated by the <span class="html-italic">p</span>-values for the discrimination between IBD and controls.</p> "> Figure 4
<p>Prediction of inflammatory bowel disease based on leave-one-out cross-validation. Dashed line indicates a cut-off value of 0.6. Y-axis depicts the predicted probabilities of being healthy (so no IBD) for all samples. On the X-axis, the samples are depicted, sorted by predicted probability of IBD, represented by triangles. Dots indicate the known sample classification (healthy control, at y = 1 and blue; IBD, at y = 0 and red).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Participants
2.2.1. Inflammatory Bowel Disease
2.2.2. Controls
2.3. Data Collection
2.3.1. Sample Collection
2.3.2. Assessment of Variables
2.4. Sample Preparation
2.5. Electronic Nose Device
2.6. Fecal Volatile Organic Compound Analysis
2.7. Statistical Analyses
3. Results
3.1. Baseline Characteristics
3.2. The Effects of Sensor Drift on Fecal Volatile Organic Compound Profiles
3.3. Differentiation between Inflammatory Bowel Disease and Controls
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Controls | Inflammatory Bowel Disease | |||
---|---|---|---|---|
(n = 63) | Crohn’s disease (n = 24) | Ulcerative colitis (n = 39) | Total IBD (n = 63) | |
Sex, ♀ (n, %) | 39 (61.9) | 15 (62.5) | 24 (61.5) | 39 (61.5) |
Age, mean ± SD | 56.2 ± 11.1 | 34.2 ± 25.7 | 50.5 ± 17.6 | 44.3 ± 22.3 |
Smoking (n, %) | ||||
Current | 8 (12.7) | 6 (25.0) | 2 (5.1) | 8 (12.7) |
Past | 22 (34.9) | 8 (33.3) | 14 (35.9) | 22 (34.9) |
Never | 33 (52.4) | 10 (41.7) | 23 (59.0) | 33 (52.4) |
Disease activity (n, %) | ||||
Quiescent | N.A. | 5 (20.8) | 17 (43.6) | 22 (34.9) |
Active | N.A. | 19 (79.2) | 22 (56.4) | 41 (65.1) |
Diet (n, %) | ||||
None | 53 (84.1) | 18 (75.0) | 33 (84.6) | 51 (81.0) |
Vegetarian | 2 (3.2) | 1 (4.2) | 0 (0) | 1 (1.6) |
Gluten-free | 3 (4.8) | 2 (8.3) | 3 (7.7) | 5 (7.9) |
Lactose-free | 2 (3.2) | 1 (4.2) | 0 (0) | 1 (1.6) |
Other | 5 (7.9) | 2 (8.3) | 3 (7.7) | 5 (7.9) |
Indication for endoscopy * (n, %) | ||||
Positive FIT test | 5 (7.9) | 0 (0) | 2 (5.1) | 2 (3.2) |
Rectal blood loss | 8 (12.7) | 3 (12.5) | 3 (7.7) | 6 (9.5) |
Change in bowel habits | 10 (15.9) | 0 (0) | 0 (0) | 0 (0) |
Surveillance † | 14 (22.2) | 0 (0) | 20 (51.3) | 20 (31.7) |
Abdominal pain | 13 (20.6) | 3 (12.5) | 0 (0) | 3 (4.8) |
Diarrhea | 5 (7.9) | 1 (4.2) | 0 (0) | 1 (1.6) |
Family history of CRC | 4 (6.3) | 0 (0) | 0 (0) | 0 (0) |
Follow-up after diverticulitis | 2 (3.2) | 0 (0) | 0 (0) | 0 (0) |
Weight loss | 3 (4.8) | 0 (0) | 0 (0) | 0 (0) |
Constipation | 3 (4.8) | 0 (0) | 0 (0) | 0 (0) |
Anemia | 1 (1.6) | 0 (0) | 0 (0) | 0 (0) |
Disease monitoring | N.A. | 6 (25) | 0 (0) | 6 (9.5) |
Suspected exacerbation | N.A. | 10 (41.7) | 12 (30.8) | 22 (34.9) |
Other ** | N.A. | 3 (12.5) | 0 (0) | 3 (4.8) |
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Bosch, S.; de Menezes, R.X.; Pees, S.; Wintjens, D.J.; Seinen, M.; Bouma, G.; Kuyvenhoven, J.; Stokkers, P.C.F.; de Meij, T.G.J.; de Boer, N.K.H. Electronic Nose Sensor Drift Affects Diagnostic Reliability and Accuracy of Disease-Specific Algorithms. Sensors 2022, 22, 9246. https://doi.org/10.3390/s22239246
Bosch S, de Menezes RX, Pees S, Wintjens DJ, Seinen M, Bouma G, Kuyvenhoven J, Stokkers PCF, de Meij TGJ, de Boer NKH. Electronic Nose Sensor Drift Affects Diagnostic Reliability and Accuracy of Disease-Specific Algorithms. Sensors. 2022; 22(23):9246. https://doi.org/10.3390/s22239246
Chicago/Turabian StyleBosch, Sofie, Renée X. de Menezes, Suzanne Pees, Dion J. Wintjens, Margien Seinen, Gerd Bouma, Johan Kuyvenhoven, Pieter C. F. Stokkers, Tim G. J. de Meij, and Nanne K. H. de Boer. 2022. "Electronic Nose Sensor Drift Affects Diagnostic Reliability and Accuracy of Disease-Specific Algorithms" Sensors 22, no. 23: 9246. https://doi.org/10.3390/s22239246
APA StyleBosch, S., de Menezes, R. X., Pees, S., Wintjens, D. J., Seinen, M., Bouma, G., Kuyvenhoven, J., Stokkers, P. C. F., de Meij, T. G. J., & de Boer, N. K. H. (2022). Electronic Nose Sensor Drift Affects Diagnostic Reliability and Accuracy of Disease-Specific Algorithms. Sensors, 22(23), 9246. https://doi.org/10.3390/s22239246