Background:In 2020, a mandatory, nationwide 1-day bronchoscopy simulation-based training (SBT) course was implemented for novice pulmonology residents in the Netherlands. This pretest-posttest study was the first to evaluate the effectiveness of such a nationwide course in improving residents' simulated basic bronchoscopy skills. Methods:After passing a theoretical test, residents followed a 1-day SBT course, available in 7 centers, where they practiced their bronchoscopy skills step-by-step on a virtual reality simulator under pulmonologist supervision. Residents practiced scope handling efficiency (task 1) and navigational skills combined with lung anatomy knowledge (task 2). Task 1 outcome measures were navigational skill simulator metrics: percentage of time at mid-lumen, percentage of time with scope-wall contact, procedure time (PT), number of wall contacts and number of wall contacts per minute of PT. Task 2 outcome measures were PT, observational assessment scores of a validated tool with a 5-point scale (1 representing the worst and 5 the best competence) and blinded dexterity assessments. Results:The study included 100 residents. All outcome measures of task 1 improved significantly (P<0.001), except for the number of wall contacts per minute of PT (4.3 [IQR 3.0 to 6.2] pre vs. 3.5 [IQR 2.6 to 5.3] post, P=0.07). For task 2, PT was reduced by 54% (10.3 +/- 2.7 minutes pre vs. 4.7 +/- 0.9 minutes post, P<0.001) with an improvement in overall-competence scores (2.0 [IQR 1.0 to 2.0] pre vs. 4.0 [IQR 4.0 to 5.0] post, P<0.001) and all dexterity parameters (P<0.001). Conclusion:Nationwide implementation of a SBT course led to rapid improvement of residents' basic bronchoscopy skills while halving PT.

Background The blood eosinophil count has been shown to be a promising biomarker for establishing personalised treatment strategies to reduce corticosteroid use, either inhaled or systemic, in chronic obstructive pulmonary disease (COPD). Eosinophil levels seem relatively stable over time in stable state, but little is known whether this is also true in subsequent severe acute exacerbations of COPD (AECOPD). Aims and objectives To determine the stability in eosinophil categorisation between two subsequent severe AECOPDs employing frequently used cut-off levels. Methods During two subsequent severe AECOPDs, blood eosinophil counts were determined at admission to the hospital in 237 patients in the Cohort of Mortality and Inflammation in COPD Study. The following four cut-off levels were analysed: absolute counts of eosinophils ≥0.2×10⁹/L (200 cells/µL) and ≥0.3×10⁹/L (300 cells/µL) and relative eosinophil percentage of ≥2% and ≥3% of total leucocyte count. Categorisations were considered stable if during the second AECOPD their blood eosinophil status led to the same classification: eosinophilic or not. Results Depending on the used cut-off, the overall stability in eosinophil categorisation varied between 70% and 85% during two subsequent AECOPDs. From patients who were eosinophilic at the first AECOPD, 34%–45% remained eosinophilic at the subsequent AECOPD, while 9%–21% of patients being non-eosinophilic at the first AECOPD became eosinophilic at the subsequent AECOPD. Conclusions The eosinophil variability leads to category changes in subsequent AECOPDs, which limits the eosinophil categorisation stability. Therefore, measurement of eosinophils at each new exacerbation seems warranted.

Introduction: Exhaled-breath analysis of volatile organic compounds (VOCs) has shown the potential to detect lung cancer. Reproducibility of prediction models, especially based on artificial intelligence (AI) algorithms is essential. However, external validation is often lacking as this is time-consuming and meanwhile improved AI models often outperform the “older” model, based on a training set. We aim to simultaneously validate and improve a training model to distinguish non-small cell lung cancer (NSCLC) patients from healthy controls based on AI algorithms. Methods: We obtained exhaled-breath data of > 800 subjects. This new cohort will be used to externally validate our original prediction model to distinguish between NSCLC patients and healthy controls (N=290, AUC-ROC 0.76). In a step-wise design, a set of 50 subjects will be first predicted by the original model, whereupon these data are added to the unblinded data, and a new prediction model will be created based on an increased sample size. This will be repeated 6 times. The remaining 500 subjects will be used to validate the final extended model. Performance will be assessed by Area under the Curve. Results: Despite finishing the inclusions, due to the COVID pandemic, we have not yet been able to validate data of all included subjects in all 7 centres. This will be done before September 2020. Conclusion: We propose a design to simultaneously externally validate an original prediction model based on exhaled-breath data to distinguish NSCLC patients from healthy controls and develop new prediction models based on improved AI techniques.

Objective: Simulators allow trainees to acquire basic skills for flexible bronchoscopy (FB) before practice on a patient. The aim of the current study was to gain insight into the required skills for FB, their underlying cognitive aspects and to what extent they can be trained on a virtual-reality (VR) simulator. Method: Four established FB guidelines and a simulation-based FB curriculum were analyzed to identify essential steps in FB and used as input for a semi-structured interview. A retrospective think-aloud study was performed combined with a semi-structured interview. Six experts performed a diagnostic FB on a VR simulator which was video-recorded. Participants engaged in retrospective think-aloud while watching their performance on video. A semi-structured interview helped clarify concepts and opinions about simulation-based training. Results: Experts agreed that two types of skills can be trained on the VR simulator: handling of the bronchoscope and inspecting the airway. For other FB related skills, such as taking biopsies or dealing with complications, the VR simulator was too inaccurate for either training or assessment. The results from this study provide detailed descriptions of relevant cues, training goals, and experienced difficulties. Discussion: Simulation-based training for flexible bronchoscopy should combine different types of simulators to effectively train all relevant cognitive and psychomotor skills. Technical limitations and disagreements among experts about the level of competency required in FB need to be resolved before implementing the VR simulator in training and assessment.

Background: The blood eosinophil count has been shown to be a promising biomarker for establishing personalised treatment strategies to reduce corticosteroid use, either inhaled or systemic, in COPD. Eosinophil levels seem relatively persistent over time in stable state, but whether this is also true in subsequent severe AECOPD is unknown. Aims and Objectives: To determine the eosinophil persistency between two subsequent severe AECOPDs employing frequently used cut-off levels. Methods: During two subsequent severe AECOPDs blood eosinophil counts were determined at admission in 237 patients in the COMIC study. The following 4 cut-off levels were analysed: absolute counts of eosinophils ≥0.2x10⁹/L (200cells/µL) and ≥ 0.3x10⁹/L (300cells/µL) and relative eosinophil count of ≥2% of total leukocyte count and ≥ 3% of total leukocyte count. Patients were considered eosinophil persistent if during the second AECOPD their blood eosinophil status was categorised the same. Results: Depending upon the used cut-off, of the patients who were eosinophilic at the first AECOPD only 34%-45% were also eosinophilic at the subsequent AECOPD. From the patients who were non-eosinophilic at the first AECOPD 9%-21% became eosinophilic during the subsequent AECOPD (Figure). Conclusions: Eosinophil persistency in a subsequent severe AECOPD within the same patient is low and depends on the chosen cut-off. Therefore, eosinophils need to be assessed at each AECOPD.

Introduction Exhaled-breath analysis of volatile organic compounds could detect lung cancer earlier, possibly leading to improved outcomes. Combining exhaled-breath data with clinical parameters may improve lung cancer diagnosis. Methods Based on data from a previous multi-centre study, this article reports additional analyses. 138 subjects with non-small cell lung cancer (NSCLC) and 143 controls without NSCLC breathed into the Aeonose. The diagnostic accuracy, presented as area under the receiver operating characteristic curve (AUC-ROC), of the Aeonose itself was compared with 1) performing a multivariate logistic regression analysis of the distinct clinical parameters obtained, and 2) using this clinical information beforehand in the training process of the artificial neural network (ANN) for the breath analysis. Results NSCLC patients (mean± sd age 67.1±9.1 years, 58% male) were compared with controls (62.1±7.0 years, 40.6% male). The AUC-ROC of the classification value of the Aeonose itself was 0.75 (95% CI 0.69–0.81). Adding age, number of pack-years and presence of COPD to this value in a multivariate regression analysis resulted in an improved performance with an AUC-ROC of 0.86 (95% CI 0.81–0.90). Adding these clinical variables beforehand to the ANN for classifying the breath print also led to an improved performance with an AUC-ROC of 0.84 (95% CI 0.79–0.89). Conclusions Adding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose, either post hoc in a multivariate regression analysis or a priori to the ANN, significantly improves the diagnostic accuracy to detect the presence or absence of lung cancer.

Introduction: Lung cancer is a leading cause of cancer mortality. Exhaled-breath analysis of volatile organic compounds (VOC’s) may detect lung cancer at an early stage, possibly leading to better outcomes. Artificial neural networks (ANN) have proven to be accurate tools to diagnose lung cancer by distinguishing between breath profiles of healthy and sick individuals. Adding readily available clinical information to the ANN may improve the diagnostic accuracy. Methods: Subjects with non-small cell lung cancer (NSCLC) and healthy controls breathed into the Aeonose™ (The eNose Company, Zutphen, Netherlands). Diagnostic accuracy, presented as Area under the Curve (AUC) was studied in a prospective, multi-center study in 282 individuals of whom 140 had confirmed NSCLC. We compared a 6-element breath-vector (Aeonose-only) with a 10-element vector (Aeonose plus age, pack years, COPD-presence, and gender). Results: Confirmed NSCLC patients (67.1 (9.0) years; 57.6% male) were compared with non-NSCLC controls (62.1 (7.1) years; 40.4% male). The AUC based on the Aeonose-only classification was 0.75 (95% CI: 0.69-0.81). Adding age, number of pack years, presence of COPD, and gender to the ANN resulted in an improved performance with an AUC of 0.84 (95% CI: 0.79-0.88). By choosing an appropriate threshold value in the ROC-diagram of the multivariate model, we observed a sensitivity of 92.9%, a specificity of 53.3%, and a positive and negative predictive value of 66.3% and 88.4%, respectively. Conclusion: The diagnostic accuracy to predict presence or absence of lung cancer can be improved significantly by adding readily available clinical information to the ANN.

BACKGROUND:Elevated levels of midrange proadrenomedullin (MR-proADM) are associated with worse outcome in different diseases, including COPD. The association of stable-state MR-proADM with severe acute exacerbations of COPD (AECOPDs) requiring hospitalization, or with community-acquired pneumonia (CAP) in patients with COPD, has not been studied yet. The aim of this study was to evaluate the association of stable-state MR-proADM with severe AECOPD and CAP in patients with COPD. METHODS:This study pooled data of 1,285 patients from the Cohort of Mortality and Inflammation in COPD (COMIC) and PRedicting Outcome using systemic Markers In Severe Exacerbations of Chronic Obstructive Pulmonary Disease (PROMISE-COPD) cohort studies. Time until first severe AECOPD was compared between patients with high (≥ 0.87 nmol/L) or low (< 0.87 nmol/L) levels of plasma MR-proADM in stable state as previously defined. For time until first CAP, only COMIC data (n = 795) were available. RESULTS:Patients with COPD with high-level stable-state MR-proADM have a significantly higher risk for severe AECOPD compared with those with low-level MR-proADM with a corrected hazard ratio (HR) of 1.30 (95% CI, 1.01-1.68). Patients with high-level stable-state MR-proADM had a significantly higher risk for CAP compared with patients with COPD with low-level MR-proADM in univariate analysis (HR, 1.93; 95% CI, 1.24-3.01), but after correction for age, lung function, and previous AECOPD, the association was no longer significant (corrected HR, 1.10; 95% CI, 0.68-1.80). CONCLUSIONS:Stable-state high-level MR-proADM in patients with COPD is associated with severe AECOPD but not with CAP.

Introduction: Lung cancer is the leading cause of global cancer mortality. Exhaled-breath analysis of volatile organic compounds (VOC’s), reflecting pathological processes, has the potential to detect non-small cell lung cancer (NSCLC) early in the course of the disease in a non-invasive way, which may improve outcome. Analyses into subtypes of NSCLC, such as adenocarcinoma (AC) and squamous cell carcinoma (SCC) have not been performed extensively yet. Methods: Subjects diagnosed with AC or SCC and healthy subjects breathed into the Aeonose™ (The eNose Company, Zutphen, Netherlands) for 5 minutes. The diagnostic accuracy was studied in a prospective multicenter study in 81 patients with confirmed AC and 26 patients with confirmed SCC. In the 2 analysis, respectively, 109 and 91 healthy subjects were included. The results were compared with the accuracy to diagnose NSCLC as 1 group. Limited case sample sizes resulted in different group sizes for healthy subjects. Data compression and artificial neural networks were used for the statistical analysis of VOC data. Results: AC patients had a mean age of 63.0 years and SCC patients had a mean age of 63.5 years. Table 1 shows the diagnostic performance of the Aeonose™ in terms of sensitivity, specificity, negative predictive value and area under the curve for the different groups. Conclusion: The data suggest that the Aeonose™ can contribute to the early diagnostic workup of lung cancer. When differentiating between subtypes of NSCLC, the diagnostic performance improves.

Background: In patients hospitalised for an exacerbation of COPD (severe AECOPD), elevated blood eosinophil count was associated with a shortened length of stay. Less is known about its association with survival. Aims and objectives: The aim of this study was to evaluate the association of elevated blood eosinophil count at admission for a severe AECOPD with all-cause mortality. Methods: 450 patients of the COMIC cohort study who were admitted for a severe AECOPD were divided in normal or elevated blood eosinophil count, defined as eosinophils ≥2% of the total leukocyte count on day of admission. Outcome parameter was all-cause mortality. Minimum follow-up was 3 years. Results: Elevated blood eosinophil count (N=97) was associated with a significantly lower risk for all-cause mortality in univariate analysis (Figure 1; logrank p=0.004) (HR 1.53; 95%CI 1.15-2.04). [figure1] Patients with elevated blood eosinophil count were younger than patients with normal blood eosinophil count (66.3 (SD 10.4) versus 70.6 (SD 9.6) years, respectively (p<0.001)). Corrected for age this association disappeared (adjusted Hazard Ratio of 1.25; 95% CI, 0.93-1.66). Conclusion: The strong association of elevated blood eosinophils on admission for a severe AECOPD with lower risk for mortality in univariate analysis disappeared after correction for age. The association of age with eosinophil count needs further investigation.

Background: Midrange-proadrenomedullin(MR-proADM), assessed in patients with clinically stable COPD, has been shown to be an independent predictor of all-cause mortality. In patients with community acquired pneumonia(CAP), pro-adrenomedullin has been associated with mortality. However, stable state MR-proADM as a predictor for severe exacerbations of COPD with hospitalisation(AECOPD), or for CAP in COPD patients has not been studied yet. Aims and objectives: The aim of this study was to evaluate stable state MR-proADM as an independent predictor for an AECOPD and CAP in COPD patients. Methods: This study pooled data of 1285 patients from the COMIC and PROMISE-COPD cohort studies. Time till first AECOPD was compared between patients with high or low levels of MR-proADM based on the cut-off level of 0.87 nmol/l in stable state defined in earlier studies. For time till first CAP only COMIC data could be used. Results: COPD patients with high level stable state MR-proADM had a significantly higher risk for an AECOPD compared with those with low level MR-proADM with an adjusted Hazard Ratio (HR) of 1.33 (95% CI, 1.03 - 1.71) in multivariate Cox regression analysis. In the COMIC study COPD patients with high level stable state MR-proADM had a significantly higher risk for a CAP compared with COPD patients with low level MR-proADM (unadjusted HR 1.93; p=0.003). However, corrected for age and lung function in multivariate Cox regression this effect disappeared (corrected HR 1.17; p=0.532). Conclusions: Stable state high level MR-proADM in COPD patients is an independent predictor for a severe AECOPD.

BACKGROUND:Both chronic inflammation and cardiovascular comorbidity play an important role in the morbidity and mortality of patients with chronic obstructive pulmonary disease (COPD). Statins could be a potential adjunct therapy. The additional effects of statins in COPD are, however, still under discussion. The aim of this study is to further investigate the association of statin use with clinical outcomes in a well-described COPD cohort.METHODS:795 patients of the Cohort of Mortality and Inflammation in COPD (COMIC) study were divided into statin users or not. Statin use was defined as having a statin for at least 90 consecutive days after inclusion. Outcome parameters were 3-year survival, based on all-cause mortality, time until first hospitalisation for an acute exacerbation of COPD (AECOPD) and time until first community-acquired pneumonia (CAP). A sensitivity analysis was performed without patients who started a statin 3 months or more after inclusion to exclude immortal time bias.RESULTS:Statin use resulted in a better overall survival (corrected HR 0.70 (95% CI 0.51 to 0.96) in multivariate analysis), but in the sensitivity analysis this association disappeared. Statin use was not associated with time until first hospitalisation for an AECOPD (cHR 0.95, 95% CI 0.74 to 1.22) or time until first CAP (cHR 1.1, 95% CI 0.83 to 1.47).CONCLUSIONS:In the COMIC study, statin use is not associated with a reduced risk of all-cause mortality, time until first hospitalisation for an AECOPD or time until first CAP in patients with COPD.

Purpose: The prognosis of malignant pleural mesothelioma (MPM) is poor and most prognostic factors are inconsistent. PET/CT provides a measure of the metabolic activity of the tumor. Therefore, we evaluated the prognostic value of parameters derived from PET/CT, produced by our new developed tool. Methods: PET/CT images of 38 patients, from three institutions, with MPM who underwent PET/CT prior to treatment were retrospectively semi-automatically analyzed. The measured parameters included; mean, maximum and peak standardized uptake values (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG, the tumor volume multiplied by its mean SUV). Results: Median survival was 9.9 months after diagnosis. Univariate Cox regression showed that SUVmax (HR=1.05), SUVpeak (HR=1.05), TLG (HR=1.15) and MTV (HR=1.13) are significant predictors for overall survival. Kaplan-Meier analyses showed significant difference in survival when MTV and TLG were grouped based on their median value. Conclusion: A new developed software tool showed that SUVmax, SUVpeak, TLG and MTV are all significant PET/CT parameters to predict survival in patients with malignant pleural mesothelioma. A prospective study should be performed to asses the possibility for chemotherapy response monitoring using this tool.