The Ability of An Electronic Nose To Distinguish Between Complications In Lung Transplant Recipients.

Abstract

Complications like acute cellular rejection (ACR) and infection are known risk factors for the development of chronic lung allograft dysfunction (CLAD), impacting long-term patient and graft survival after lung transplantation (LTx). Differentiating between complications remains challenging and time-sensitive, highlighting the need for accurate and rapid diagnostic modalities. We assessed the ability of exhaled breath analysis using an electronic nose (eNose) to distinguish between ACR, infection and mechanical complications in LTx recipients (LTR) presenting with suspected complications. LTR with suspected complications and subsequently proven diagnosis underwent exhaled breath analysis using an eNose. Supervised machine learning was used to assess the eNose's ability to discriminate between complications. Next we determined the added value of the eNose measurement on top of standard clinical parameters. In 90 LTR, 161 measurements were performed during suspected complications, with 84 proven diagnoses. The eNose could distinguish between ACR, infection and mechanical complications with 74% accuracy, and ACR and infection with 82% accuracy. Combining eNose measurements with standard clinical parameters improved diagnostic accuracy to 88% (p=0.0139), with 94% sensitivity and 80% specificity. Exhaled breath analysis using eNose technology is a promising, non-invasive, diagnostic modality for distinguishing LTx complications, enabling timely diagnosis and interventions.