Inhaled Steroids, Sex and Emphysema Index - Clinical Parameters with Impact on the Effectiveness of Valve Implantation in Patients with Severe Emphysema.

Aims and Objectives: Many patients with chronic obstructive lung disease suffer from emphysema. Valve implantation may be a reasonable method in patients presenting advanced emphysema and absent interlobar collateral ventilation (CV). However, other clinical parameters influencing the effectiveness of endoscopic lung volume reduction (ELVR) are not well known. Methods: COPD patients with advanced emphysema who received valve implantation in 2016 were retrospectively analyzed. The following characteristics were collected prior to valve implantation: age, sex, body mass index, presence of allergies, use of inhaled corticosteroids (ICS), lung function parameters, diffusion capacity, 6-minute walk distance (6-MWD), blood gases, COHb, smoking history, and emphysema index (quantitative multi-detector computed tomography). Three months following valve implantation, lung function parameters, diffusion capacity, 6-MWD and blood gases were measured. In this analysis, we evaluated the impact of these variables on an increase in FEV1 and 6-MWT as well as a decrease in RV three months after valve implantation. Results: Overall, 77 COPD patients (57% male, mean age 66, mean FEV1 32%, mean RV 259%) who underwent valve therapy were enrolled. At 3-month follow-up, patients experienced a mean FEV1 increase of 0.09 ± 0.21 L, a mean RV decrease of 0.42 ± 1.80 L and a mean improvement of 8.3 ± 57 m in the 6-MWT. Overall, ICS, sex and emphysema index had an impact on the outcome following ELVR: ICS medication was associated with inferior FEV1 outcome. The higher the emphysema index, the less the RV reduction. Sex was a predictor for change of FEV1 (%), RV (L), and 6-MWT: male patients seem to benefit less than female patients from valve implantation. Conclusion: These findings suggest that ICS, emphysema index and sex are clinical parameters that may be associated with inferior outcome following ELVR. Further studies have to confirm these results to improve patient selection and clinical outcome of ELVR.

The Influence of Distance and Lateral Offset of Follow Me Robots on User Perception.

Robots that are designed to work in close proximity to humans are required to move and act in a way that ensures social acceptance by their users. Hence, a robot's proximal behavior toward a human is a main concern, especially in human-robot interaction that relies on relatively close proximity. This study investigated how the distance and lateral offset of "Follow Me" robots influences how they are perceived by humans. To this end, a Follow Me robot was built and tested in a user study for a number of subjective variables. A total of 18 participants interacted with the robot, with the robot's lateral offset and distance varied in a within-subject design. After each interaction, participants were asked to rate the movement of the robot on the dimensions of comfort, expectancy conformity, human likeness, safety, trust, and unobtrusiveness. Results show that users generally prefer robot following distances in the social space, without a lateral offset. However, we found a main influence of affinity for technology, as those participants with a high affinity for technology preferred closer following distances than participants with low affinity for technology. The results of this study show the importance of user-adaptiveness in human-robot-interaction.

Long-term follow up after endoscopic valve therapy in patients with severe emphysema.

BACKGROUND AND OBJECTIVE: Endoscopic valve therapy is a treatment modality in patients with advanced emphysema and absent interlobar collateral ventilation (CV). So far, long-term outcome following valve implantation has been insufficiently evaluated. The aim of this study was to investigate the real-world efficacy of this interventional therapy over 3 years. METHODS: From 2006 to 2013, 256 patients with severe emphysema in whom absent CV was confirmed underwent valve therapy. The 3-year effectiveness was evaluated by pulmonary function testing (VC, FEV1, RV, TLC), 6-minute-walk test (6-MWT) and dyspnea questionnaire (mMRC). Long-term outcome was also assessed according to the radiological outcome following valve placement. RESULTS: Of 256 patients treated with valves, 220, 200, 187, 100 and 66 patients completed the 3-month, 6-month, 1-year, 2-year and 3-year follow-up (FU) visit, respectively. All lung function parameters, 6-MWT and mMRC were significantly improved at 3- and 6-month FU. At 1-year FU, patients still experienced a significant improvement of all outcome parameters expect VC (L) and TLC (%). At 2 years, RV (L and %) and TLC (L and %) remained significantly improved compared to baseline. Three years after valve therapy, sustained significant improvement in mMRC was observed and the proportion of patients achieving a minimal clinically important difference from baseline in RV and 6-MWT was still 71% and 46%, respectively. Overall, patients with complete lobar atelectasis exhibited superior treatment outcome with 3-year responder rates to FEV1, RV and 6-MWT of 10%, 79% and 53%, respectively. CONCLUSIONS: Patients treated by valves experienced clinical improvement over 1 year following valve therapy. Afterwards, clinical benefit gradually declines more likely due to COPD progression.

Interventional therapy in patients with severe emphysema: evaluation of contraindications and their incidence.

BACKGROUND: Endoscopic and surgical interventions may be beneficial for selected patients with emphysema. Rates of treatment failure decrease when the predictors for successful therapy are known. The aim of the study was to evaluate the number of patients with severe emphysema who were not eligible for any intervention, and the reasons for their exclusion. METHODS: The study was a retrospective analysis of 231 consecutive patients with advanced emphysema who were considered for interventional therapy in 2016 at the Thoraxklinik, Heidelberg, Germany. The reasons for not receiving valve or coil therapy were assessed for all patients who did not receive any therapy. RESULTS: Of the 231 patients, 50% received an interventional therapy for lung volume reduction (LVR) (82% valve therapy, 6% coil therapy, 4.3% polymeric LVR or bronchial thermal vapour ablation, 4.3% total lung denervation, and 3.4% lung volume reduction surgery [LVRS]). A total of 115 patients did not undergo LVR. Out of these, valve or coil therapy was not performed due to one or more of the following reasons: incomplete fissure in 37% and 0%; missing target lobe in 31% and 30%; personal decision in 18% and 28%; pulmonary function test results in 8% and 15%; ventilatory failure in 4% and 4%; missing optimal standard medical care and/or continued nicotine abuse in 4% and 3%; general condition too good in less than 1% and 3%; cardiovascular comorbidities in 0% and 3%; age of patient in 0% and less than 1%. Both techniques were not performed due to one or more of the following reasons: solitary pulmonary nodule(s)/consolidation in 27%; bronchopathy in 7%; neoplasia in 2%; destroyed lung in 2%; prior LVRS in less than 1%. CONCLUSIONS: The main reason for not placing valves was an incomplete fissure and for coils a missing target lobe. Numerous additional contraindications that may exclude a patient from interventional emphysema therapy should be respected.

A public data hub for benchmarking common brain-computer interface algorithms.

Methods of statistical machine learning have recently proven to be very useful in contemporary brain-computer interface (BCI) research based on the discrimination of electroencephalogram (EEG) patterns. Because of this, many research groups develop new algorithms for both feature extraction and classification. However, until now, no large-scale comparison of these algorithms has been accomplished due to the fact that little EEG data is publicly available. Therefore, we at Team PhyPA recorded 32-channel EEGs, electromyograms and electrooculograms of 36 participants during a simple finger movement task. The data are published on our website www.phypa.org and are freely available for downloading. We encourage BCI researchers to test their algorithms on these data and share their results. This work also presents exemplary benchmarking procedures of common feature extraction methods for slow cortical potentials and event-related desynchronization as well as for classification algorithms based on these features.

A data-driven algorithm for offline pupil signal preprocessing and eyeblink detection in low-speed eye-tracking protocols.

Event detection is the conversion of raw eye-tracking data into events--such as fixations, saccades, glissades, blinks, and so forth--that are relevant for researchers. In eye-tracking studies, event detection algorithms can have a serious impact on higher level analyses, although most studies do not accurately report their settings. We developed a data-driven eyeblink detection algorithm (Identification-Artifact Correction [I-AC]) for 50-Hz eye-tracking protocols. I-AC works by first correcting blink-related artifacts within pupil diameter values and then estimating blink onset and offset. Artifact correction is achieved with data-driven thresholds, and more reliable pupil data are output. Blink parameters are defined according to previous studies on blink-related visual suppression. Blink detection performance was tested with experimental data by visually checking the actual correspondence between I-AC output and participants' eye images, recorded by the eyetracker simultaneously with gaze data. Results showed a 97% correct detection percentage.