An investigation that has just been presented at the International Congress of the European Respiratory Society, held virtually, and which is pending publication, shows the development of a powerful electronic nose that is capable of detecting, with 86% accuracy , when a lung transplant is beginning to fail. This finding could help doctors detect rejections early so that treatments can be given to prevent worsening. However, more research will be required before this eNose can be used clinically.
“About 50% of lung transplant patients are diagnosed with chronic allograft dysfunction (CLAD) or chronic rejection within five years after transplantation. Chronic rejection remains the most important cause of death after lung transplantation and, at the moment, there is no treatment available to reverse it, ”explained Nynke Wijbenga, PhD student and medical technician at the Erasmus University Medical Center from Rotterdam, the Netherlands. “Once chronic rejection has been confirmed, patients can survive an average of one to five years. A transplant could be a last resort for specific patients with advanced chronic rejection. Therefore, it is of utmost importance to assess whether we can predict or diagnose lung transplant dysfunction at an early stage, possibly allowing for more successful early treatment. “
Currently, the diagnosis of CLAD can take several months. Doctors evaluate lung function at each visit and compare it to the best maximal lung function achieved after transplantation. If it drops to 80% or less, they investigate further to exclude causes that could respond to treatment, such as a lung infection that could be treated with antibiotics. Chronic rejection can only be confirmed after these investigations and if the deterioration in lung function persists for three months.
This is how eNose works
The eNose is a small device that contains sensors to detect chemicals called volatile organic compounds (VOCs), which are present in approximately 1% of our exhaled breath and can vary depending on the metabolic processes that occur throughout the body or some parts such as lungs. When patients exhale into the eNose , the sensors not only detect the VOC pattern in their breath, but also correct the results to account for the ambient air that has been inhaled. The results are analyzed using machine learning algorithms and the “breath fingerprint” can be used to identify various lung diseases.
To validate the functioning of the electronic nose, the researchers recruited 91 lung transplant patients, took an eNose measurement from each patient, and then compared their results with the usual diagnoses that had already been made to them. In 86% of the cases, the researchers found that eNose was able to discriminate between the 68 patients who had stable lung transplants and the 23 patients who had CLAD.
“These results suggest that eNose is a promising tool for the detection of CLAD,” explained 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. Furthermore, we need to confirm our results in other groups of patients. However, our aim is to develop this as a technique for widespread use across Europe. “
Texto: European Lung Foundation
