Reference : VOLATILE ORGANIC COMPOUND PROFILES RELATE TO SUBSEQUENT PGD IN LUNG TRANSPLANT RECIPIENTS
Scientific congresses and symposiums : Poster
Physical, chemical, mathematical & earth Sciences : Chemistry
http://hdl.handle.net/2268/225952
VOLATILE ORGANIC COMPOUND PROFILES RELATE TO SUBSEQUENT PGD IN LUNG TRANSPLANT RECIPIENTS
English
Stefanuto, Pierre-Hugues mailto [Université de Liège - ULiège > Département de chimie (sciences) > Chimie analytique, organique et biologique >]
Rees, Christiaan []
Romano, Rosalba []
Nasir, Mavra []
Thakuria, Luit []
Pagani, Nicole []
Reed, Anna []
Marczin, Nandor []
Hill, Jane []
Jun-2018
Yes
International
International Association of Breath Research Summit
du 17 au 20 juin 2018
IABR
Maastricht
The Nederlands
[en] Lung transplantation ; Data processing ; GCxGC
[en] Background: Primary graft dysfunction (PGD) is a major complication following lung transplantation. PGD reflects the summation of injury inflicted on the donor lung by the transplant process including donor related factors, preservation and reperfusion injury, intraoperative factors, and consequences of intensive care management. It is a leading cause of death during the early post-transplant period. PGD is currently define based on the presence of chest infiltrates and the PaO2/FIO2 ratio. Patients are classified in four grades from healthy (0) to severe (3). There has been significant progress in delineating basic mechanisms of PGD and towards identification of genetic or molecular biomarkers capable of predicting, and monitoring PGD. However, continuous method development may provide insight into disease pathogenesis, as well as have prognostic value for predicting the disease trajectory of a patient with PGD.
Methods: The volatile organic compound (VOC) profiles of 58 bronchoalveolar lavage fluid (BAL) and blind bronchial aspirate (BBA) samples from 35 transplanted patients were analyzed using solid phase microextraction combined with comprehensive two-dimensional gas chromatography coupled to a time-of-flight mass spectrometer. Data were split evenly into training and testing. The support vector machine algorithm (SVM) was used to identify VOCs that could differentiate patients with severe (grade 3) PGD from low (grade 0-2) PGD. Model performance was evaluated using test set samples.
Results: Using 20 VOCs we achieved an AUROC of 0.899 (95% confidence interval: 0.777 – 1.000) and an accuracy of 0.828 (95% CI: 0.642 – 0.941) on test set samples. In terms of statistical performance indicators, the final model has a sensitivity of 0.636, specificity of 0.944, positive predictive value of 0.875, and negative predictive value of 0.809. The miss-classification are mainly coming from patient with grade 2 PGD indicating the potential risk profile of these patients.
Conclusions: The analysis of lung fluids provided high accuracy, specificity, and sensitivity for PGD detection at time of transplant. Our data suggests that BAL/BBA has potential and should be investigated in a larger scale study. The potential translation to breath volatile analysis should also be investigated.
http://hdl.handle.net/2268/225952

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