Accuracy of the Electronic Nose Breath Tests in Clinical Application: A Systematic Review and Meta-Analysis

(1) Background: An electronic nose applies a sensor array to detect volatile biomarkers in exhaled breath to diagnose diseases. The overall diagnostic accuracy remains unknown. The objective of this review was to provide an estimate of the diagnostic accuracy of sensor-based breath tests for the diagnosis of diseases. (2) Methods: We searched the PubMed and Web of Science databases for studies published between 1 January 2010 and 14 October 2021. The search was limited to human studies published in the English language. Clinical trials were not included in this review. (3) Results: Of the 2418 records identified, 44 publications were eligible, and 5728 patients were included in the final analyses. The pooled sensitivity was 90.0% (95% CI, 86.3–92.8%, I2 = 47.7%), the specificity was 88.4% (95% CI, 87.1–89.5%, I2 = 81.4%), and the pooled area under the curve was 0.93 (95% CI 0.91–0.95). (4) Conclusion: The findings of our review suggest that a standardized report of diagnostic accuracy and a report of the accuracy in a test set are needed. Sensor array systems of electronic noses have the potential for noninvasiveness at the point-of-care in hospitals. Nevertheless, the procedure for reporting the accuracy of a diagnostic test must be standardized.

Hydrogen and Methane Breath Test in the Diagnosis of Lactose Intolerance

The hydrogen (H2) breath test is a non-invasive investigation used to diagnose lactose intolerance (LI). Patients with LI may also expire increased amounts of methane (CH4) during a lactose test. The aim of this study is to evaluate the contribution of CH4 measurements. We tested 209 children (1–17 years old) with symptoms suggesting LI with lactose H2 and CH4 breath tests. The result was positive when the H2 excretion exceeded 20 parts per million (ppm) and the CH4 was 10 ppm above the baseline. A clinician, blinded for the results of the breath test, registered the symptoms. Of the patient population, 101/209 (48%) were negative for both H2 and CH4; 96/209 (46%) had a positive H2 breath test result; 31/96 (32%) were also positive for CH4; 12/209 (6%) patients were only positive for CH4. The majority of hydrogen producers showed symptoms, whereas this was only the case in half of the H2-negative CH4 producers. Almost all patients treated with a lactose-poor diet reported significant symptom improvement. These results indicate that CH4 measurements may possibly be of additional value for the diagnosis of LI, since 5.7% of patients were negative for H2 and positive for CH4, and half of them experienced symptoms during the test.

A re-testing range is recommended for 13C- and 14C-urea breath tests for Helicobacter pylori infection in China

Background The urea breath test (UBT) is widely used for diagnosing Helicobacter pylori infection. In the Shenzhen Kuichong People’s Hospital, some UBT findings were contradictory to the histology outcomes, therefore this study aimed to assess and compare the diagnostic performance of both 13C- and 14C-UBT assays. Methods We recruited 484 H. pylori-treatment naïve patients, among which 217 and 267 were tested by the 13C-UBT or 14C-UBT, respectively. The cutoff value for H. pylori positivity based on manufacturer’s instruction was 4% delta over baseline (DOB) for the 13C-UBT, and 100 disintegrations per minute (DPM) for the 14C-UBT. Gastric biopsies of the antrum and corpus were obtained during endoscopy for histopathology. Results In patients who were tested using the 13C-UBT kit, histopathology was positive in 136 out of 164 UBT-positive patients (82.9% concordance), and negative in 46 out of 53 UBT-negative cases (86.8% concordance). For the 14C-UBT-tested patients, histopathology was positive for H. pylori in 186 out of 220 UBT-positive patients (84.5% concordance), and negative in 41 out of 47 UBT-negative cases (87.2% concordance). While the 13C-UBT and 14C-UBT each had a high sensitivity level of 95.1% and 96.9%, respectively, their specificity was low, at 62.2% and 54.7%, respectively. By using new optimal cutoff values and including an indeterminate range (3–10.3% DOB for 13C-UBT and 87–237 DPM for 14C-UBT), the specificity values can be improved to 76.7% and 76.9% for the 13C- and 14C-UBT, respectively. Conclusions The establishment of an indeterminate range is recommended to allow for repeated testing to confirm H. pylori infection, and thereby avoiding unnecessary antibiotic treatment. Trial registration: Chinese Clinical Trial Registry, ChiCTR2000041570. Registered 29 December 2020- Retrospectively registered, http://www.chictr.org.cn/edit.aspx?pid=66416&htm=4

Unsupervised Learning for Hydrogen Breath Tests

Hydrogen breath tests are a well-established method to help diagnose functional intestinal disorders such as carbohydrate malabsorption or small intestinal bacterial overgrowth. In this work we apply unsupervised machine learning techniques to analyze hydrogen breath test datasets. We propose a method that uses 26 internal cluster validation measures to determine a suitable number of clusters. In an induced external validation step we use a predefined categorization proposed by a medical expert. The results indicate that the majority of the considered internal validation indexes was not able to produce a reasonable clustering. Considering a predefined categorization performed by a medical expert, a novel shape-based method obtained the highest external validation measure in terms of adjusted rand index. The predefined clusterings constitute the basis of a supervised machine learning step that is part of our ongoing research.