The eNose could save doctors and patients months of time in diagnosing lung transplant failure by detecting certain molecules in exhaled air.
A PhD student and technical physician at Erasmus University Medical Centre in the Netherlands is working on an electronic nose designed to sniff out patients that are in the early stages of lung transplant failure. While Nynke Wijbenga’s device is still in the early period of trials, the eNose could represent a powerful tool in follow-up care for lung transplants.
The research is being presented in a session today (7 September) at the European Respiratory Society Conference called Optimising outcomes of lung transplantation: how to move forward?
“About 50pc of lung transplant patients are diagnosed with chronic allograft dysfunction or chronic rejection within five years of the transplant. Chronic rejection remains the most important cause of death after lung transplantation and, at the moment, there is no treatment available to reverse it,” said Wijbenga.
“Once chronic rejection has been confirmed, patients can on average survive for between one and five years. A re-transplantation could be a last resort for specific patients with advanced chronic rejection. Therefore, it is of utmost importance to assess if we can predict or diagnose lung transplant dysfunction at an early stage, possibly enabling more successful early treatment.”
Modern lung transplants
Currently, diagnosing lung failure in transplants can take several months. This condition is known as chronic allograft dysfunction (CLAD). Doctors presently test a patient’s lung function during each visit and measure this against their peak lung function after their transplant.
If a measurement comes back below 80pc, they will begin looking for possible causes that might respond to treatment, such as an infection. If no other reasons can be found and the decline continues for three months, a CLAD diagnosis may be given.
Wijbenga’s eNose aims to avoid this long diagnostic journey by detecting chemicals called volatile organic compounds (VOCs). These molecules are present in approximately 1pc of our exhaled breath, but this varies depending on the body’s metabolic processes.
When patients breathe into eNose, the sensors detect the patterns of VOCs while correcting for the ambient air that is being inhaled. By using machine learning algorithms, the patient’s ‘breathprint’ could be used to identify a variety of lung diseases.
Putting your money where your nose is
To test the invention, Wijbenga and colleagues recruited 91 patients who had lung transplants. The eNose took one measurement per patient. Wijbenga then compared these results to the diagnoses made by the patients’ consultants.
Of the 68 patients who were stable and the 23 with CLAD, the eNose was able to group a given patient correctly 86pc of the time.
“These results suggest that the eNose is a promising tool for detection of CLAD,” said Wijbenga. “However, more research is required before it can be used in the clinic. We need to assess whether repeated measurements in the same patients can provide more accurate diagnoses and even predict CLAD before it occurs.
“Also, we need to confirm our results in other groups of patients. Nonetheless, we aim to develop this as a technique for wide use across Europe.”
Those patients initially measured will continue to provide eNose measurements to allow the researchers to chart their progress in relation to the data. The hope is that by using the new data, the eNose will be able to differentiate between forms of CLAD.
“We hope that our further research will reveal whether eNose technology could distinguish between bronchitis obliterans syndrome and restrictive allograft syndrome. Additionally, we want to investigate if it could be used for other complications after lung transplantation, such as acute rejection and infection,” concluded Wijbenga.