Serological responses to COVID-19 vaccination are diminished in recipients of solid organ transplants, especially in lung transplant recipients (LTR), probably as result of immunosuppressive treatment. There is currently no marker of immunosuppression that can be used to predict the COVID-19 vaccination response. Here, we study whether torque tenovirus (TTV), a highly prevalent virus can be used as an indicator of immunosuppression. The humoral response to the mRNA 1273 vaccine was assessed in 103 LTR, who received a transplant between 4 and 237 months prior to vaccination, by measuring Spike (S)-specific IgG levels at baseline, 28 days after first, and 28 days after the second vaccination. TTV loads were determined by RT-PCR and Pearson's correlation coefficient was calculated to correlate serological responses to TTV load. Humoral responses to COVID-19 vaccination were observed in 41 of 103 (40%) LTR at 28 days after the second vaccination. Sixty-two of 103 (60%) were non-responders. Lower TTV loads at baseline (significantly) correlated with higher S-specific antibodies and a higher percentage of responders. Lower TTV loads also strongly correlated with longer time since transplantation, indicating that participants with lower TTV loads were longer after transplantation. This study shows a better humoral response to the SARS-CoV-2 vaccine in subjects with a lower TTV load pre-vaccination. In addition, TTV load correlates with the time after transplantation. Further studies on the use of TTV load in vaccination efficacy studies in immunocompromised cohorts should provide leads for the potential use of this marker for optimizing vaccination response.

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). 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. 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. 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.

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.

With each exhaled breath, thousands of molecules are expelled. Every person has a unique composition of this expelled air, the so-called breathprint, representing their current state of health. Identification of individual volatile organic compounds (VOCs), although specific, is an extremely time-consuming process and hard to implement in routine clinical care. An electronic nose (eNose) can be used to capture the complete mixture of VOCs in exhaled air by several cross-reactive gas sensors. Without identifying individual components in expelled air, the sensor captures information that results in a breathprint pattern which can be analysed with artificial intelligence using pattern recognition [1, 2]. Consequently, using an eNose to collect real-time measurements of the breathprint has potential as a cheap and fast point-of-care tool in clinical practice. In recent years, exhaled breath analysis using eNose technology has gained increasing attention and has demonstrated great potential as a real-time noninvasive diagnostic tool, where different vendors are available [3]. For example, promising results were demonstrated in diagnosis of asthma phenotypes and interstitial lung diseases, with international confirmation studies ongoing to bring this technology to outpatient clinics [3–5].

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.

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. Export