Detection of Bacterial Colonization in Lung Transplant Recipients Using an Electronic Nose

Nynke Wijbenga; Nadine L A de Jong; Rogier A S Hoek; Bas J Mathot; Leonard Seghers; Joachim G J V Aerts; Daniel Bos; Olivier C Manintveld; Merel E Hellemons

doi:10.1097/TXD.0000000000001533

Detection of Bacterial Colonization in Lung Transplant Recipients Using an Electronic Nose

Transplant Direct. 2023 Sep 20;9(10):e1533. doi: 10.1097/TXD.0000000000001533. eCollection 2023 Oct.

Authors

Nynke Wijbenga^{1

2}, Nadine L A de Jong^{1

2

3}, Rogier A S Hoek^{1

2}, Bas J Mathot^{1

2}, Leonard Seghers^{1

2}, Joachim G J V Aerts¹, Daniel Bos^{4

5}, Olivier C Manintveld^{2

6}, Merel E Hellemons^{1

2}

Affiliations

¹ Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.
² Erasmus Medical Center Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.
³ Educational Program Technical Medicine, Leiden University Medical Center, Delft University of Technology and Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.
⁴ Department of Radiology and Nuclear Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.
⁵ Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.
⁶ Department of Cardiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.

Abstract

Background: Bacterial colonization (BC) of the lower airways is common in lung transplant recipients (LTRs) and increases the risk of chronic lung allograft dysfunction. Diagnosis often requires bronchoscopy. Exhaled breath analysis using electronic nose (eNose) technology may noninvasively detect BC in LTRs. Therefore, we aimed to assess the diagnostic accuracy of an eNose to detect BC in LTRs.

Methods: We performed a cross-sectional analysis within a prospective, single-center cohort study assessing the diagnostic accuracy of detecting BC using eNose technology in LTRs. In the outpatient clinic, consecutive LTR eNose measurements were collected. We assessed and classified the eNose measurements for the presence of BC. Using supervised machine learning, the diagnostic accuracy of eNose for BC was assessed in a random training and validation set. Model performance was evaluated using receiver operating characteristic analysis.

Results: In total, 161 LTRs were included with 80 exclusions because of various reasons. Of the remaining 81 patients, 16 (20%) were classified as BC and 65 (80%) as non-BC. eNose-based classification of patients with and without BC provided an area under the curve of 0.82 in the training set and 0.97 in the validation set.

Conclusions: Exhaled breath analysis using eNose technology has the potential to noninvasively detect BC.