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

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.

Diagnostic accuracy of eNose 'breathprints' for therapeutic drug monitoring of Tacrolimus trough levels in lung transplantation.

In order to prevent long-term immunity-related complications after lung transplantation, close monitoring of immunosuppressant levels using therapeutic drug monitoring (TDM) is paramount. Novel electronic nose (eNose) technology may be a non-invasive alternative to the current invasive procedures for TDM. We investigated the diagnostic and categorization capacity of eNose breathprints for Tacrolimus trough blood plasma levels (TACtrough) in lung transplant recipients (LTRs). We performed eNose measurements in stable LTR attending the outpatient clinic. We evaluated (1) the correlation between eNose measurements and TACtrough, (2) the diagnostic capacity of eNose technology for TACtrough, and (3) the accuracy of eNose technology for categorization of TACtroughinto three clinically relevant categories (low: <7µg ml-1, medium: 7-10µg ml-1, and high: >10µg ml-1). A total of 186 measurements from 86 LTR were included. There was a weak but statistically significant correlation (r= 0.21,p= 0.004) between the eNose measurements and TACtrough. The root mean squared error of prediction for the diagnostic capacity was 3.186 in the training and 3.131 in the validation set. The accuracy of categorization ranged between 45%-63% for the training set and 52%-69% in the validation set. There is a weak correlation between eNose breathprints and TACtroughin LTR. However, the diagnostic as well as categorization capacity for TACtroughusing eNose breathprints is too inaccurate to be applicable in TDM.

Diagnostic performance of electronic nose technology in chronic lung allograft dysfunction.

BACKGROUND: There is a need for reliable biomarkers for the diagnosis of chronic lung allograft dysfunction (CLAD). In this light, we investigated the diagnostic value of exhaled breath analysis using an electronic nose (eNose) for CLAD, CLAD phenotype, and CLAD stage in lung transplant recipients (LTR). METHODS: We performed eNose measurements in LTR with and without CLAD, visiting the outpatient clinic. Through supervised machine learning, the diagnostic value of eNose for CLAD was assessed in a random training and validation set. Next, we investigated the diagnostic value of the eNose measurements combined with known risk factors for CLAD. Model performance was evaluated using ROC-analysis. RESULTS: We included 152 LTR (median age 60 years, 49% females), of whom 38 with CLAD. eNose-based classification of patients with and without CLAD provided an AUC of 0.86 in the training set, and 0.82 in the validation set. After adding established risk factors for CLAD (age, gender, type of transplantation, time after transplantation and prior occurrence of acute cellular rejection) to a model with the eNose data, the discriminative ability of the model improved to an AUC of 0.94 (p = 0.02) in the training set and 0.94 (p = 0.04) in the validation set. Discrimination between BOS and RAS was good (AUC 0.95). Discriminative ability for other phenotypes (AUCs ranging 0.50-0.92) or CLAD stages (AUC 0.56) was limited. CONCLUSION: Exhaled breath analysis using eNose is a promising novel biomarker for enabling diagnosis and phenotyping CLAD. eNose technology could be a valuable addition to the diagnostic armamentarium for suspected graft failure in LTR.

The potential of electronic nose technology in lung transplantation: a proof of principle.

Exhaled breath analysis using eNose technology holds promise as a point-of-care indicator of clinical status after lung transplantation. This case study invites further exploration of eNose technology in the field of lung transplantation. https://bit.ly/3wgQ3DE.

Long-term outcome and bridging success of patients evaluated and bridged to lung transplantation on the ICU.

BACKGROUND: Evaluating and bridging patients to lung transplantation (LTx) on the intensive care unit (ICU) remains controversial, especially without a previous waitlist status. Long term outcome data after LTx from ICU remains scarce. We compared long-term survival and development of chronic lung allograft dysfunction (CLAD) in elective and LTx from ICU, with or without previous waitlist status. METHODS: Patients transplanted between 2004 and 2018 in 2 large academic Dutch institutes were included. Long-term survival and development of CLAD was compared in patients who received an elective LTx (ELTx), those bridged and transplanted from the ICU with a previous listing status (BTT), and in patients urgently evaluated and bridged on ICU (EBTT). RESULTS: A total of 582 patients underwent a LTx, 70 (12%) from ICU, 39 BTT and 31 EBTT. Patients transplanted from ICU were younger than ELTx (46 vs 51 years) and were bridged with mechanical ventilation (n = 42 (60%)), extra corporeal membrane oxygenation (n = 28 (40%)), or both (n = 21/28). Bridging success was 48% in the BTT group and 72% in the EBTT group. Patients bridged to LTx on ICU had similar 1 and 5 year survival (86.8% and 78.4%) compared to elective LTx (86.8% and 71.9%). This was not different between the BTT and EBTT group. 5 year CLAD free survival was not different in patients transplanted from ICU vs ELTx. CONCLUSION: Patients bridged to LTx on the ICU with and without prior listing status had excellent short and long-term patient and graft outcomes, and was similar to patients electively transplanted.

Evaluation of a Home Monitoring Application for Follow Up after Lung Transplantation-A Pilot Study.

Home spirometry after lung transplantation is common practice, to monitor graft function. However, there is little experience with online home monitoring applications with direct data transfer to the hospital. We evaluated the feasibility and patient experiences with a new online home monitoring application, integrated with a Bluetooth-enabled spirometer and real-time data transfer. Consecutive lung transplant recipients were asked to evaluate this home monitoring application for three months in a pilot study. Home spirometry measurements were compared with in-hospital lung function tests (the forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC)) at the end of the study. Ten patients participated. The home and hospital spirometry measurements showed a high correlation, for both the FEV1 (r = 0.99, p < 0.01) and FVC (r = 0.99, p < 0.01). The adherence and patient satisfaction were high, and the patients preferred the home monitoring application over the current home spirometer, with a difference of 1.4 ± 1.5 points on a scale from 0 to 10 (p = 0.02). Online home monitoring with direct data transfer is feasible and reliable after lung transplantation and results in high patient satisfaction. Whether the implementation of online home monitoring enables the earlier detection of lung function decline and improves patient and graft outcomes will be the subject of future research.