Pollution exposure in the 1st 3 months post transplant is associated with lower baseline FEV1 and higher CLAD risk.

BACKGROUND: Exposure to air pollution post lung transplant has been shown to decrease graft and patient survival. This study examines the impact of air pollution exposure in the first 3 months post-transplant on baseline (i.e. highest) forced expiratory volume in 1s (FEV1) achieved and development of chronic lung allograft dysfunction (CLAD). METHODS: Double-lung transplant recipients (n=82) were prospectively enrolled for comprehensive indoor and personal environmental monitoring at 6- and 12-weeks post-transplant and followed for >4 years. Associations between clinical and exposure variables were investigated using an exposomics approach followed by analysis with a Cox Proportional Hazards model. Multivariable analyses were used to examine the impact of air pollution on baseline % predicted FEV1 (defined as the average of the 2 highest values achieved post-transplant) and risk of CLAD. RESULTS: Multivariable analysis revealed a significant inverse relationship between personal black carbon (BC) levels and baseline % FEV1. The multivariable model indicated that patients with higher-than-median exposure to BC (>350 ng/m3) attained a baseline % FEV1 that was 8.8% lower than those with lower-than-median BC exposure (p = 0.019). Cox proportional hazards model analysis revealed that patients with high personal BC exposure had a 2.4 times higher hazard risk for CLAD than patients with low BC exposure (p = 0.045). CONCLUSIONS: Higher personal BC levels during the first 3 months post-transplant decreases baseline FEV1 and doubles the risk of CLAD. Strategies to reduce BC exposure early following lung transplant may help improve lung function and long-term outcomes.

Respiratory oscillometry with CT image analysis in idiopathic pulmonary fibrosis following single lung transplant.

Oscillometry is an emerging pulmonary function testing tool that is conducted during tidal breaths with minimal patient effort. It is highly sensitive to changes in lung mechanics. Oscillometry was recently shown to be highly associated with disease severity in idiopathic pulmonary fibrosis (IPF). The usefulness of oscillometry after single lung transplant in IPF patients is not well understood. Our study demonstrated that oscillometry can detect changes in the graft despite presence of a native fibrotic lung to provide useful information to complement spirometry.

Effects of donor smoking history on early post-transplant lung function measured by oscillometry.

Introduction: Prior studies assessing outcomes of lung transplants from cigarette-smoking donors found mixed results. Oscillometry, a non-invasive test of respiratory impedance, detects changes in lung function of smokers prior to diagnosis of COPD, and identifies spirometrically silent episodes of rejection post-transplant. We hypothesise that oscillometry could identify abnormalities in recipients of smoking donor lungs and discriminate from non-smoking donors. Methods: This prospective single-center cohort study analysed 233 double-lung recipients. Oscillometry was performed alongside routine conventional pulmonary function tests (PFT) post-transplant. Multivariable regression models were constructed to compare oscillometry and conventional PFT parameters between recipients of lungs from smoking vs non-smoking donors. Results: The analysis included 109 patients who received lungs from non-smokers and 124 from smokers. Multivariable analysis identified significant differences between recipients of smoking and non-smoking lungs in the oscillometric measurements R5-19, X5, AX, R5z and X5z, but no differences in %predicted FEV1, FEV1/FVC, %predicted TLC or %predicted DLCO. An analysis of the smoking group also demonstrated associations between increasing smoke exposure, quantified in pack years, and all the oscillometry parameters, but not the conventional PFT parameters. Conclusion: An interaction was identified between donor-recipient sex match and the effect of smoking. The association between donor smoking and oscillometry outcomes was significant predominantly in the female donor/female recipient group.

Intra-subject variability in oscillometry correlates with acute rejection and CLAD post-lung transplant.

Background: Chronic lung allograft dysfunction (CLAD) is the major cause of death post-lung transplantation, with acute cellular rejection (ACR) being the biggest contributing risk factor. Although patients are routinely monitored with spirometry, FEV1 is stable or improving in most ACR episodes. In contrast, oscillometry is highly sensitive to respiratory mechanics and shown to track graft injury associated with ACR and its improvement following treatment. We hypothesize that intra-subject variability in oscillometry measurements correlates with ACR and risk of CLAD. Methods: Of 289 bilateral lung recipients enrolled for oscillometry prior to laboratory-based spirometry between December 2017 and March 2020, 230 had ≥ 3 months and 175 had ≥ 6 months of follow-up. While 37 patients developed CLAD, only 29 had oscillometry at time of CLAD onset and were included for analysis. These 29 CLAD patients were time-matched with 129 CLAD-free recipients. We performed multivariable regression to investigate the associations between variance in spirometry/oscillometry and the A-score, a cumulative index of ACR, as our predictor of primary interest. Conditional logistic regression models were built to investigate associations with CLAD. Results: Multivariable regression showed that the A-score was positively associated with the variance in oscillometry measurements. Conditional logistic regression models revealed that higher variance in the oscillometry metrics of ventilatory inhomogeneity, X5, AX, and R5-19, was independently associated with increased risk of CLAD (p < 0.05); no association was found for variance in %predicted FEV1. Conclusion: Oscillometry tracks graft injury and recovery post-transplant. Monitoring with oscillometry could facilitate earlier identification of graft injury, prompting investigation to identify treatable causes and decrease the risk of CLAD.

Quality control in respiratory oscillometry: reproducibility measures ignoring reactance?

This study demonstrates the inadequacy of the current technical standards of oscillometry that are based on the within-trial reproducibility of the lowest-frequency Rrs, and suggests the use of a simple variability measure encompassing both Rrs and Xrs https://bit.ly/3AYRid6.

Deep learning using multilayer perception improves the diagnostic acumen of spirometry: a single-centre Canadian study.

RATIONALE: Spirometry and plethysmography are the gold standard pulmonary function tests (PFT) for diagnosis and management of lung disease. Due to the inaccessibility of plethysmography, spirometry is often used alone but this leads to missed or misdiagnoses as spirometry cannot identify restrictive disease without plethysmography. We aimed to develop a deep learning model to improve interpretation of spirometry alone. METHODS: We built a multilayer perceptron model using full PFTs from 748 patients, interpreted according to international guidelines. Inputs included spirometry (forced vital capacity, forced expiratory volume in 1 s, forced mid-expiratory flow25-75), plethysmography (total lung capacity, residual volume) and biometrics (sex, age, height). The model was developed with 2582 PFTs from 477 patients, randomly divided into training (80%), validation (10%) and test (10%) sets, and refined using 1245 previously unseen PFTs from 271 patients, split 50/50 as validation (136 patients) and test (135 patients) sets. Only one test per patient was used for each of 10 experiments conducted for each input combination. The final model was compared with interpretation of 82 spirometry tests by 6 trained pulmonologists and a decision tree. RESULTS: Accuracies from the first 477 patients were similar when inputs included biometrics+spirometry+plethysmography (95%±3%) vs biometrics+spirometry (90%±2%). Model refinement with the next 271 patients improved accuracies with biometrics+pirometry (95%±2%) but no change for biometrics+spirometry+plethysmography (95%±2%). The final model significantly outperformed (94.67%±2.63%, p<0.01 for both) interpretation of 82 spirometry tests by the decision tree (75.61%±0.00%) and pulmonologists (66.67%±14.63%). CONCLUSIONS: Deep learning improves the diagnostic acumen of spirometry and classifies lung physiology better than pulmonologists with accuracies comparable to full PFTs.

Correlation of respiratory oscillometry with CT image analysis in a prospective cohort of idiopathic pulmonary fibrosis.

BACKGROUND: Markers of idiopathic pulmonary fibrosis (IPF) severity are based on measurements of forced vital capacity (FVC), diffusing capacity (DLCO) and CT. The pulmonary vessel volume (PVV) is a novel quantitative and independent prognostic structural indicator derived from automated CT analysis. The current prospective cross-sectional study investigated whether respiratory oscillometry provides complementary data to pulmonary function tests (PFTs) and is correlated with PVV. METHODS: From September 2019 to March 2020, we enrolled 89 patients with IPF diagnosed according to international guidelines. We performed standard spectral (5-37 Hz) and novel intrabreath tracking (10 Hz) oscillometry followed by PFTs. Patients were characterised with the gender-age-physiology (GAP) score. CT images within 6 months of oscillometry were analysed in a subgroup (26 patients) using automated lung texture analysis. Correlations between PFTs, oscillometry and imaging variables were investigated using different regression models. FINDINGS: The cohort (29F/60M; age=71.7±7.8 years) had mild IPF (%FVC=70±17, %DLCO=62±17). Spectral oscillometry revealed normal respiratory resistance, low reactance, especially during inspiration at 5 Hz (X5in), elevated reactance area and resonance frequency. Intrabreath oscillometry identified markedly low reactance at end-inspiration (XeI). XeI and X5in strongly correlated with FVC (r2=0.499 and 0.435) while XeI was highly (p=0.004) and uniquely correlated with the GAP score. XeI and PVV exhibited the strongest structural-functional relationship (r2=0.690), which remained significant after adjusting for %FVC, %DLCO and GAP score. INTERPRETATION: XeI is an independent marker of IPF severity that offers additional information to standard PFTs. The data provide a cogent rationale for adding oscillometry in IPF assessment.

Changes in interpretation of spirometry by implementing the GLI 2012 reference equations: impact on patients tested in a hospital-based PFT lab in a large metropolitan city.

BACKGROUND: The Global Lung Function Initiative (GLI-2012) focused on race/ethnicity as an important factor in determining reference values. This study evaluated the effects of changing from Canadian reference equations developed from an all-Caucasian cohort with European ancestry to the GLI-2012 on the interpretation of spirometry in a multiethnic population and aimed to identify the ethnic groups affected by discrepant interpretations. METHODS: Clinically indicated spirometry in a multiethnic population (aged 20-80 years) collected from 2018 to 2021 was analysed. The predicted and lower limit of normal (LLN) values were calculated using three sets of reference equations: Canadian, GLI-race/ethnic-based (GLI-Race) and GLI-race/ethnic-neutral (GLI-Other). We compared the prevalence of concordance in the abnormal diagnoses (defined as

Airway Oscillometry Detects Spirometric-Silent Episodes of Acute Cellular Rejection.

Rationale: Acute cellular rejection (ACR) is common during the initial 3 months after lung transplant. Patients are monitored with spirometry and routine surveillance transbronchial biopsies. However, many centers monitor patients with spirometry only because of the risks and insensitivity of transbronchial biopsy for detecting ACR. Airway oscillometry is a lung function test that detects peripheral airway inhomogeneity with greater sensitivity than spirometry. Little is known about the role of oscillometry in patient monitoring after a transplant.Objectives: To characterize oscillometry measurements in biopsy-proven clinically significant (grade ≥2 ACR) in the first 3 months after a transplant.Methods: We enrolled 156 of the 209 double lung transplant recipients between December 2017 and March 2019. Weekly outpatient oscillometry and spirometry and surveillance biopsies at Weeks 6 and 12 were conducted at our center.Measurements and Main Results: Of the 138 patients followed for 3 or more months, 15 patients had 16 episodes of grade 2 ACR (AR2) and 44 patients had 64 episodes of grade 0 ACR (AR0) rejection associated with stable and/or improving spirometry. In 15/16 episodes of AR2, spirometry was stable or improving in the weeks leading to transbronchial biopsy. However, oscillometry was markedly abnormal and significantly different from AR0 (P < 0.05), particularly in integrated area of reactance and the resistance between 5 and 19 Hz, the indices of peripheral airway obstruction. By 2 weeks after biopsy, after treatment for AR2, oscillometry in the AR2 group improved and was similar to the AR0 group.Conclusions: Oscillometry identified physiological changes associated with AR2 that were not discernible by spirometry and is useful for graft monitoring after a lung transplant.