Detection of Barrett’s oesophagus through exhaled breath using an electronic nose device

Peters, Yonne; Schrauwen, Ruud; Tan, Adriaan C.; Bogers, Sanne; Jong, Bart de; Siersema, Peter D.

doi:10.1136/gutjnl-2019-320273

Cited by 63 publications

(39 citation statements)

References 25 publications

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“…A recent study of Peters et al showed that Aeonose technology can detect a Barrett esophagus in patients with or without gastroesophageal reflux disease with a sensitivity of 91% and a specificity of 74%. In the future, the Aeonose might play an important role in the primary care setting as non-invasive screening for Barrett esophagus [26]. In the detection of tuberculosis, comparable Aeonose technology has shown its good diagnostic accuracy as well: a sensitivity of 93.5% and a specificity of 85.3% were found in distinguishing tuberculosis patients from healthy controls in Bangladesh [30].…”

Section: Discussionmentioning

confidence: 99%

“…Since the diagnosis can be made within only sixteen minutes, the test can be considered a point-of-care test. Extensive research with the Aeonose has already been done not only in oncology [22][23][24][25] and pre-malignant disorders such as Barrett esophagus [26], but also into infectious diseases such as tuberculosis and in differentiating viral from bacterial respiratory infections in acute COPD exacerbations [27,28].…”

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confidence: 99%

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Applying the electronic nose for pre-operative SARS-CoV-2 screening

et al. 2020

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Background Infection with SARS-CoV-2 causes corona virus disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19-positive and negative persons based on volatile organic compounds (VOCs) analysis. Methods Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, the presence of SARS-CoV-2-specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine learning and used for pattern recognition. The result is a value between − 1 and + 1, indicating the infection probability. Results 219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96. Conclusions The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Applying the electronic nose for pre-operative SARS-CoV-2 screening

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“…Several approaches to overcome limitations of current screening policies have been explored to identify individuals at highest risk of BE and EAC. Recently, alternative screening modalities, such as transnasal endoscopy (TNE), nonendoscopic cell collection devices, and circulating and exhaled biomarkers, are being developed for the detection of BE to improve effectiveness, reduce costs, and minimize invasiveness of screening techniques compared with upper endoscopy ( 7 – 9 ). Although screening with these minimally invasive screening techniques is not yet recommended by current guidelines, various studies have piloted EAC screening with these techniques and have shown promising results ( 6 – 8 , 10 , 11 ).…”

Section: Introductionmentioning

confidence: 99%

Public Preferences and Predicted Uptake for Esophageal Cancer Screening Strategies: A Labeled Discrete Choice Experiment

Peters

Siersema²

2020

Clinical and Translational Gastroenterology

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INTRODUCTION: As novel, less invasive (non)endoscopic techniques for detection of Barrett's esophagus (BE) have been developed, there is now renewed interest in screening for BE and related neoplasia. We aimed to determine public preferences for esophageal adenocarcinoma screening to understand the potential of minimally invasive screening modalities. METHODS: A discrete choice experiment was conducted in 1,500 individuals, aged 50–75 years, from the general population. Individuals were repeatedly asked to choose between screening scenarios based on conventional upper endoscopy, transnasal endoscopy, nonendoscopic cell collection devices, breath analysis, and a blood test, combined with various levels of test sensitivity and specificity, and no screening. A multinomial logit model was used to estimate individuals' preferences and to calculate expected participation rates. RESULTS: In total, 554 respondents (36.9%) completed the survey. The average predicted uptake was 70.5% (95% confidence interval: 69.1%–71.8%). Test sensitivity (47.7%), screening technique (32.6%), and specificity (19.7%) affected screening participation (all P < 0.05). A low test sensitivity had the highest impact on screening participation, resulting in a 25.0% (95% confidence interval: 22.6%–27.7%) decrease. Respondents preferred noninvasive screening tests over endoscopic and capsule-based techniques, but only if sensitivity and specificity were above 80%. DISCUSSION: Our study suggests that individuals generally prefer noninvasive BE screening tests. However, these tests would unlikely improve screening uptake when associated with a much lower accuracy for detecting BE and esophageal adenocarcinoma compared with conventional upper endoscopy. Improving accuracy of minimally invasive screening strategies and informing the target population about these accuracies is therefore essential to maximally stimulate screening participation.

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“…A recent study of Peters et al showed that Aeonose technology can detect a Barrett esophagus in patients with or without gastroesophageal re ux disease with a sensitivity of 91% and a speci city of 74%. In the future, the Aeonose might play an important role in the primary care setting as non-invasive screening for Barrett esophagus (26). In the detection of tuberculosis, comparable Aeonose technology has shown its good diagnostic accuracy as well: a sensitivity of 93.5% and a speci city of 85.3% was found in distinguishing tuberculosis patients from healthy controls in Bangladesh (30).…”

Section: Discussionmentioning

confidence: 99%

“…Since the diagnosis can be made within only sixteen minutes, the test can be considered a point-of-care test. Extensive research with the Aeonose has already been done in oncology (22)(23)(24)(25) and pre-malignant disorders such as Barrett esophagus (26), but also into infectious diseases such as tuberculosis and in differentiating viral from bacterial respiratory infections in acute COPD exacerbations (27,28).…”

Section: Introductionmentioning

confidence: 99%

Applying the electronic nose for pre-operative SARS-CoV-2 screening

Wintjens

Hintzen

Engelen

et al. 2020

Preprint

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Background: Infection with SARS-CoV-2 causes Corona Virus Disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19 positive- and negative persons based on volatile organic compounds (VOCs) analysis.Methods: Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, presence of SARS-CoV-2 specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine-learning and used for pattern recognition. The result is a value between -1 and +1, indicating the infection probability.Results: 219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96. Conclusions: The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery.

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Detection of Barrett’s oesophagus through exhaled breath using an electronic nose device

Cited by 63 publications

References 25 publications

Applying the electronic nose for pre-operative SARS-CoV-2 screening

Applying the electronic nose for pre-operative SARS-CoV-2 screening

Public Preferences and Predicted Uptake for Esophageal Cancer Screening Strategies: A Labeled Discrete Choice Experiment

Applying the electronic nose for pre-operative SARS-CoV-2 screening

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