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Abstract Background Protective measures applied during the Covid-19 pandemic had a marked impact on the incidence of pneumonia. However, systematic data are lacking for hospitalizations for pneumonia and acute exacerbations of chronic obstructive lung diseases (AECOPD) not caused by SARS-CoV-2 in Switzerland. We aimed to compare the incidences of hospitalization for these entities between 2020/2021 and prepandemic years. Methods This retrospective study examined all nationwide hospitalizations for non-Covid-19-pneumonia and AECOPD listed as primary diagnoses based on ICD-10 codes between 2015 and 2021 in a publicly available hospitalization database of the Swiss Federal Statistical Office. Hospitalizations for acute coronary syndrome (ACS) and stroke were used as controls. Changes of monthly incidences of hospitalizations, length of stay (LOS) and mortality were compared between 2020/2021 and the average of 2015–2019. Results The incidences of hospitalizations for AECOPD and for pneumonia showed seasonal variations from 2015 to 2019 followed by significant and almost identical decreases in 2020/2021 (incidence rate ratio [IRR] 0.59, 95% CI: 0.45–0.77, p

Objectives To develop a statistical approach that provides a quantitative index measuring the magnitude of the irregularity of the breathing response to exercise for the diagnosis of dysfunctional breathing.Design Cross-sectional, retrospective, real-world study.Setting Single-centre study.Participants A population of 209 patients investigated with cardiopulmonary exercise testing in our institution for unexplained or disproportionate exertional dyspnoea between January and July 2022.Primary and secondary outcome measures A novel statistical approach providing a quantitative index—proportional tidal volume variation (PTVV)—was developed to measure the magnitude of the irregularity of the breathing response to exercise.Results PTVV provided a reliable statistical readout for the objective assessment of DB with a prediction accuracy of 78% (95% CI: 72 to 83%). The prevalence of DB in the investigated population was high with more than half of the patients affected by moderate-to-severe DB.Conclusions PTVV can easily be implemented in the clinical routine. Our study suggests a possible further simplification for the diagnosis of DB with two objective criteria including PTVV and one single criterion for hyperventilation.

Sleep apnea (SA) is a prevalent disorder characterized by recurrent events of nocturnal apnea. Polysomnography (PSG) represents the gold standard for SA diagnosis. This laboratory-based procedure is complex and costly, and less cumbersome wearable devices have been proposed for SA detection and monitoring. A novel textile multi-sensor monitoring belt recording electrocardiogram (ECG) and breathing frequency (BF) measured by thorax excursion was developed and tested in a sleep laboratory for validation purposes. The aim of the current study was to evaluate the diagnostic performance of ECG-derived heart rate variability and BF-derived breathing rate variability and their combination for the detection of sleep apnea in a population of patients with a suspicion of SA. Fifty-one patients with a suspicion of SA were recruited in the sleep laboratory of the Cantonal Hospital St. Gallen. Patients were equipped with the monitoring belt and underwent a single overnight laboratory-based PSG. In addition, some patients further tested the monitoring belt at home. The ECG and BF signals from the belt were compared to PSG signals using the Bland-Altman methodology. Heart rate and breathing rate variability analyses were performed. Features derived from these analyses were used to build a support vector machine (SVM) classifier for the prediction of SA severity. Model performance was assessed using receiver operating characteristics (ROC) curves. Patients included 35 males and 16 females with a median age of 49 years (range: 21 to 65) and a median apnea-hypopnea index (AHI) of 33 (IQR: 16 to 58). Belt-derived data provided ECG and BF signals with a low bias and in good agreement with PSG-derived signals. The combined ECG and BF signals improved the classification accuracy for SA (area under the ROC curve: 0.98; sensitivity and specificity greater than 90%) compared to single parameter classification based on either ECG or BF alone. This novel wearable device combining ECG and BF provided accurate signals in good agreement with the gold standard PSG. Due to its unobtrusive nature, it is potentially interesting for multi-night assessments and home-based patient follow-up.

The objectives of the present study were to evaluate the discriminating power of spirometric and plethysmographic lung function parameters to differenciate the diagnosis of asthma, ACO, COPD, and to define functional characteristics for more precise classification of obstructive lung diseases. From the databases of 4 centers, a total of 756 lung function tests (194 healthy subjects, 175 with asthma, 71 with ACO, 78 with COPD and 238 with CF) were collected, and gradients among combinations of target parameters from spirometry (forced expiratory volume one second: FEV1; FEV1/forced vital capacity: FEV1/FVC; forced expiratory flow between 25-75% FVC: FEF25-75), and plethysmography (effective, resistive airway resistance: sReff; aerodynamic work of breathing at rest: sWOB), separately for in- and expiration (sReffIN, sReffEX, sWOBin, sWOBex) as well as static lung volumes (total lung capacity: TLC; functional residual capacity: FRCpleth; residual volume: RV), the control of breathing (mouth occlusion pressure: P0.1; mean inspiratory flow: VT/TI; the inspiratory to total time ratio: TI/Ttot) and the inspiratory impedance (Zinpleth = P0.1/VT/TI) were explored. Linear discriminant analyses (LDA) were applied to identify discriminant functions and classification rules using recursive partitioning decision trees. LDA showed a high classification accuracy (sensitivity and specificity > 90%) for healthy subjects, COPD and CF. The accuracy dropped for asthma (~70%) and even more for ACO (~60%). The decision tree revealed that P0.1, sRtot, and VT/TI differentiate most between healthy and asthma (68.9%), COPD (82.1%), and CF (60.6%). Moreover, using sWOBex and Zinpleth ACO can be discriminated from asthma and COPD (60%). Thus, the functional complexity of obstructive lung diseases can be understood, if specific spirometric and plethysmographic parameters are used. Moreover, the newly described parameters of airway dynamics and the central control of breathing including Zinpleth may well serve as promising functional marker in the field of precision medicine.

ABSTRACTThe traditional picture of cancer patients as weak individuals requiring maximum rest and protection is beginning to dissolve. Too much focus on the medical side and one’s own vulnerability and mortality might be counterproductive and not doing justice to the complexity of human nature. Unlike cytotoxic and lympho-depleting treatments, immune-engaging therapies strengthen the immune system and are typically less harmful for patients. Thus, cancer patients receiving checkpoint inhibitors are not viewed as being vulnerable per se, at least not in immunological and physical terms. This perspective article advocates a holistic approach to cancer immunotherapy, with an empowered patient in the center, focusing on personal resources and receiving domain-specific support from healthcare professionals. It summarizes recent evidence on non-pharmaceutical interventions to enhance the efficacy of immune checkpoint blockade and improve quality of life. These interventions target behavioral factors such as diet, physical activity, stress management, circadian timing of checkpoint inhibitor infusion, and waiving unnecessary co-medication curtailing immunotherapy efficacy. Non-pharmaceutical interventions are universally accessible, broadly applicable, instantly actionable, scalable, and economically sustainable, creating value for all stakeholders involved. Most importantly, this holistic framework re-emphasizes the patient as a whole and harnesses the full potential of anticancer immunity and checkpoint blockade, potentially leading to survival benefits. Digital therapeutics are proposed to accompany the patients on their mission toward change in lifestyle-related behaviors for creating optimal conditions for treatment efficacy and personal growth.

Introduction The rate of relapse in smokers attempting to quit is generally high. In order to maximize the chances of success, it is of interest to better understand the dynamic of lapse and relapse during smoking cessation. We hypothesized that specific behavioral patterns in tobacco consumption could predict the probability of quitting success and could open the possibility for a more targeted approach. The aim of the current study was to characterize clusters of quitting trajectories among participants involved in a smoking cessation program. Methods In a retrospective real-world cohort study, data from 843 consecutive participants between March 2012 and December 2014 were collected. Data consisted of baseline information on demographics, smoking history and dependence level, as well as longitudinal data about tobacco consumption. The correlations among time series were characterized using principal coordinates analysis. Clusters were identified using k -means clustering and the average profile associated with each cluster was computed. The association between the participant’s baseline characteristics and clusters of tobacco consumption was assessed. Results Four distinct clusters of transition phenotypes were identified based on tobacco consumption during the cessation phase: the long-term quitters (30%), the persistent smokers/reducers (44%), the short-term returners (16%) and the repeated try and failers (10%). Significant between-cluster differences were found in terms of baseline characteristics and smoking behavior during follow-up. Conclusions Meaningful clusters of quitting trajectories could be identified. Such specific behavioral patterns were useful for the application of personalized assistance needed to achieve successful and long-term cessation.

Background Cough represents a cardinal symptom of acute respiratory tract infections. Generally associated with disease activity, cough holds biomarker potential and might be harnessed for prognosis and personalised treatment decisions. Here, we tested the suitability of cough as a digital biomarker for disease activity in coronavirus disease 2019 (COVID-19) and other lower respiratory tract infections. Methods We conducted a single-centre, exploratory, observational cohort study on automated cough detection in patients hospitalised for COVID-19 (n=32) and non-COVID-19 pneumonia (n=14) between April and November 2020 at the Cantonal Hospital St Gallen, Switzerland. Cough detection was achieved using smartphone-based audio recordings coupled to an ensemble of convolutional neural networks. Cough levels were correlated to established markers of inflammation and oxygenation. Measurements and main results Cough frequency was highest upon hospital admission and declined steadily with recovery. There was a characteristic pattern of daily cough fluctuations, with little activity during the night and two coughing peaks during the day. Hourly cough counts were strongly correlated with clinical markers of disease activity and laboratory markers of inflammation, suggesting cough as a surrogate of disease in acute respiratory tract infections. No apparent differences in cough evolution were observed between COVID-19 and non-COVID-19 pneumonia. Conclusions Automated, quantitative, smartphone-based detection of cough is feasible in hospitalised patients and correlates with disease activity in lower respiratory tract infections. Our approach allows for near real-time telemonitoring of individuals in aerosol isolation. Larger trials are warranted to decipher the use of cough as a digital biomarker for prognosis and tailored treatment in lower respiratory tract infections.

Abstract Background Text mining can be applied to automate knowledge extraction from unstructured data included in medical reports and generate quality indicators applicable for medical documentation. The primary objective of this study was to apply text mining methodology for the analysis of polysomnographic medical reports in order to quantify sources of variation – here the diagnostic precision vs. the inter-rater variability – in the work-up of sleep-disordered breathing. The secondary objective was to assess the impact of a text block standardization on the diagnostic precision of polysomnography reports in an independent test set. Results Polysomnography reports of 243 laboratory-based overnight sleep investigations scored by 9 trained sleep specialists of the Sleep Center St. Gallen were analyzed using a text-mining methodology. Patterns in the usage of discriminating terms allowed for the characterization of type and severity of disease and inter-rater homogeneity. The variation introduced by the inter-rater (technician/physician) heterogeneity was found to be twice as high compared to the variation introduced by effective diagnostic information. A simple text block standardization could significantly reduce the inter-rater variability by 44%, enhance the predictive value and ultimately improve the diagnostic accuracy of polysomnography reports. Conclusions Text mining was successfully used to assess and optimize the quality, as well as the precision and homogeneity of medical reporting of diagnostic procedures – here exemplified with sleep studies. Text mining methodology could lay the ground for objective and systematic qualitative assessment of medical reports.

Abstract Background Diagnostic precision and the identification of rare diseases is a daily challenge, which needs specialized expertise. We hypothesized, that there is a correlation between the distance of residence to the next tertiary medical facility with highly specialized care and the diagnostic precision, especially for rare diseases. Results Using a nation-wide hospitalization database, we found a negative association between diagnostic diversity and travel time to the next tertiary referral hospital when including all cases throughout the overall International Classification of Diseases version 10 German Modification (ICD-10-GM) diagnosis codes. This was paralleled with a negative association of standardized incidence rates in all groups of rare diseases defined by the Orphanet rare disease nomenclature, except for rare teratologic and rare allergic diseases. Conclusion Our findings indicate a higher risk of being mis-, under- or late diagnosed especially in rare diseases when living more distant to a tertiary medical facility. Greater distance to the next tertiary medical facility basically increases the chance for hospitalization in a non-comprehensive regional hospital with less diagnostic capacity, and, thus, impacts on adapted health care access. Therefore, solutions for overcoming the distance to specialized care as an indicator of health care access are a major goal in the future.

Abstract Background Idiopathic pulmonary fibrosis (IPF) is an incurable disease characterized by progressive lung fibrosis ultimately resulting in respiratory failure and death. Recurrent micro-injuries to the alveolar epithelium and aberrant alveolar wound healing with impaired re-epithelialization define the initial steps of the pathogenic trajectory. Failure of timely alveolar epithelial repair triggers hyper-proliferation of mesenchymal cells accompanied by increased deposition of extracellular matrix into the lung interstitium. Methods We previously isolated fibrosis-specific mesenchymal stem cell (MSC)-like cells from lung tissue of patients with interstitial lung diseases. These cells produced factors bearing anti-fibrotic potential and changed their morphology from mesenchymal to epithelial upon culture in an epithelial cell (EC)-specific growth medium. Here, we set out to molecularly characterize these MSC-like cell-derived ECs using global gene expression profiling by RNA-sequencing. Moreover, we aimed at characterizing disease-specific differences by comparing the transcriptomes of ECs from IPF and non-IPF sources. Results Our results suggest that differentially expressed genes are enriched for factors related to fibrosis, hypoxia, bacterial colonization and metabolism, thus reflecting many of the hallmark characteristics of pulmonary fibrosis. IPF-ECs showed enrichment of both pro- and anti-fibrotic genes, consistent with the notion of adaptive, compensatory regulation. Conclusions Our findings support the hypothesis of a functional impairment of IPF-ECs, which could possibly explain the poor clinical outcome of IPF that roughly compares to those of advanced-stage cancers. Our study provides a valuable resource for downstream mechanistic investigation and the quest for novel therapeutic IPF targets.

BackgroundSleep apnea (SA) is a prevalent disorder characterized by recurrent events of nocturnal apnea originating from obstructive and/or central mechanisms. SA disrupts normal sleep and can lead to a series of complications when left untreated. SA results in intermittent hypoxia which has an impact on the cardio- and cerebrovascular system. Hospitalized patients with SA typically have a greater burden of comorbidity, a longer length of hospital stay, but may show an improvement of in-hospital mortality compared to patients without diagnosed SA. The reason for this survival benefit is controversial and we aimed to clarify this protective effect in the light of predictive factors including SA-associated comorbidities using a nation-wide hospitalization database.Methods and findingsData were extracted from a nation-wide hospitalization database provided by the Swiss Federal Office for Statistics. Hospitalized patients with a SA co-diagnosis were extracted from the database together with a 1:1-matched control population without SA. Overall, 212'581 patients with SA were hospitalized in Switzerland between 2002 and 2018. Compared to the controls, SA cases had a longer median length of hospital stay (7 days; 95% CI: 3 to 15 vs. 4 days; 95% CI: 2 to 10) (p < 0.001) and a higher median number of comorbidities (8 comorbidities; IQR: 5 to 11 vs. 3 comorbidities; IQR: 1 to 6) (p < 0.001). The risk of in-hospital mortality was lower in the SA cases compared to the controls (OR: 0.73; 95% CI: 0.7 to 0.76; p < 0.001). SA was associated with a survival benefit in hospitalizations related to 28 of 47 conditions with the highest rate of in-hospital death. Sixty-three comorbidities were significantly over-represented in SA cases among which obesity, hypertension and anatomic nasal deviations were associated with a significant decrease of in-hospital mortality.ConclusionsCompared to matched controls, SA was associated with significant and relevant inpatient survival benefit in a number of most deadly conditions. Within SA-patients, associated comorbidities mostly correlated with a poorer prognosis, whereas obesity and hypertension were associated with an improved in-hospital mortality.

AIMS OF THE STUDY Noninvasive ventilation (NIV) is a well-established treatment option for hypercapnic respiratory failure; however, the best mode of ventilation remains unknown. The aim of this retrospective study was to compare patients’ adherence to NIV using either pressure support ventilation (PSV) or intelligent volume-assured pressure support (iVAPS). PATIENTS AND METHODS In this retrospective cohort study, we assessed in- and outpatients suffering from hypercapnic respiratory failure of various aetiologies (chronic obstructive pulmonary disease [COPD], obese COPD [body mass index >30 kg/m2], obesity hypoventilation syndrome and other diseases such as amyotrophic lateral sclerosis or interstitial lung disease) after NIV initiation with PSV or iVAPS. Adherence to treatment was compared between these modes using the Wilcoxon test. Within-group differences were tested using linear regression models. Mortality and emergency hospital readmission rates were modelled using Kaplan-Meier estimates and Cox proportional hazards models. RESULTS Adherence to treatment was similar in both groups throughout the observation period – after 6 weeks: PSV 363 min/night (interquartile range [IQR] 200–448), iVAPS 369 min/night (IQR 310–468) (p = 0.619); after 1 year: PSV 423 (323–500), iVAPS 429 (298–475) (p = 0.901); at the last follow up: PSV 481 (395–586), iVAPS 426 (391–565) (p = 0.284). NIV reduced PaCO2 significantly compared with baseline at all follow-ups: PSV −1.29/−1.49/−1.49 kPa, iVAPS −1.47/−1.23/−1.24 kPa, p 4 h/night and >80% nightly usage) was associated with a lower mortality rate (HR 0.34, 95% CI 0.15–0.77; p = 0.010). CONCLUSION In a real-world setting of a mixed population with hypercapnic respiratory failure, iVAPS and PSV seem to be similarly effective in improving gas exchange and demonstrate excellent adherence to treatment. A longer survival was noted in NIV-adherent patients. Randomised controlled studies are necessary to identify patients who might benefit more from hybrid ventilation modes.

Cystic fibrosis patients frequently suffer from recurring respiratory infections caused by colonizing pathogenic and commensal bacteria. Although modern therapies can sometimes alleviate respiratory symptoms by ameliorating residual function of the protein responsible for the disorder, management of chronic respiratory infections remains an issue.

In cancer patients, the clinical response to checkpoint-based immunotherapy is associated with the composition and functional quality of the host microbiome. While the relevance of the gut microbiome for checkpoint immunotherapy outcome has been addressed intensively, data on the role of the local tumor microbiome are missing. Here, we set out to molecularly characterize the local non-small cell lung cancer microbiome using 16S rRNA gene amplicon sequencing of bronchoscopic tumor biopsies from patients treated with PD-1/PD-L1-targeted checkpoint inhibitors. Our analyses showed significant diversity of the tumor microbiome with high proportions of Firmicutes, Bacteroidetes and Proteobacteria. Correlations with clinical data revealed that high microbial diversity was associated with improved patient survival irrespective of radiology-based treatment response. Moreover, we found that the presence of Gammaproteobacteria correlated with low PD-L1 expression and poor response to checkpoint-based immunotherapy, translating into poor survival. Our study suggests novel microbiome-specific/derived biomarkers for checkpoint immunotherapy response prediction and prognosis in lung cancer. In a broader sense, our data draw attention to the local tumor microbial habitat as an important addition to the spatially separated microbiome of the gut compartment.

Background: Pneumococcal pneumonia is a disease of the extremes of age. However, as other traditional risk factors for pneumococcal pneumonia also increase with older age, it is unclear if older age itself should be an indication for pneumococcal vaccination. Therefore, we assessed the effect of age on risk for hospitalization for pneumonia and for pneumococcal pneumonia.Methods: Using a national hospitalization dataset, all patients ≥16 years hospitalized in a Swiss hospital with a diagnosis of pneumonia or pneumococcal pneumonia between 2002 and 2015 were included. Multivariable logistic regression analysis was used to test the association between age (≥50 or ≥65 years) and hospitalization for pneumonia or pneumococcal pneumonia after adjusting for pneumococcal vaccine indications. Similar analyses were performed for effect of age on length of stay (LOS) and mortality.Results: Among a total of 17,619,016 hospitalizations a diagnosis of pneumonia was present in 421,760 (2.4%) and a diagnosis of pneumococcal pneumonia in 21,610 (0.12%). Age ≥50 years (OR: 3.52 and 2.12, respectively; p for both

Abstract Background The SAKK 19/05 trial investigated the safety and efficacy of the combined targeted therapy bevacizumab and erlotinib (BE) in unselected patients with advanced non-squamous non-small cell lung cancer (NSCLC). Although activating EGFR mutations were the strongest predictors of the response to BE, some patients not harboring driver mutations could benefit from the combined therapy. The identification of predictive biomarkers before or short after initiation of therapy is therefore paramount for proper patient selection, especially among EGFR wild-types. The first aim of this study was to investigate the early change in blood gene expression in unselected patients with advanced non-squamous NSCLC treated by BE. The second aim was to assess the predictive value of blood gene expression levels at baseline and 24h after BE therapy. Methods Blood samples from 43 advanced non-squamous NSCLC patients taken at baseline and 24h after initiation of therapy were profiled using Affymetrix’ exon arrays. The 24h gene dysregulation was investigated in the light of gene functional annotations using gene set enrichment analysis. The predictive value of blood gene expression levels was assessed and validated using an independent dataset. Results Significant gene dysregulations associated with the 24h-effect of BE were detected from blood-based whole-genome profiling. BE had a direct effect on “Pathways in cancer”, by significantly down-regulating genes involved in cytokine–cytokine receptor interaction, MAPK signaling pathway and mTOR signaling pathway. These pathways contribute to phenomena of evasion of apoptosis, proliferation and sustained angiogenesis. Other signaling pathways specifically reflecting the mechanisms of action of erlotinib and the anti-angiogenesis effect of bevacizumab were activated. The magnitude of change of the most dysregulated genes at 24h did not have a predictive value regarding the patients’ response to BE. However, predictive markers were identified from the gene expression levels at 24h regarding time to progression under BE. Conclusions The 24h-effect of the combined targeted therapy BE could be accurately monitored in advanced non-squamous NSCLC blood samples using whole-genome exon arrays. Putative predictive markers at 24h could reflect patients’ response to BE after adjusting for their mutational status. Trial registration ClinicalTrials.gov: NCT00354549

AIM Our aim was to estimate the diagnostic performance of institutions and healthcare regions from a nationwide hospitalisation database. METHODS The Shannon diversity index was used as an indicator of diagnostic performance based on the International Classification of Disease, 10th revision, German Modification (ICD-10-GM codes). The dataset included a total of 9,325,326 hospitalisation cases from 2009 to 2015 and was provided by the Swiss Federal Office for Statistics. A total of 16,435 diagnostic items from the ICD-10-GM codes were taken as the basis for the calculation of the diagnostic diversity index (DDI). Numerical simulations were performed to evaluate the effect of misdiagnoses in the DDI. We arbitrarily defined the minimum clinically important difference (MCID) as 10% misdiagnoses. The R statistical software was used for all analyses. RESULTS Diagnostic performance of institutions and healthcare regions as measured by the DDI were strongly associated with caseload and number of inhabitants, respectively. A caseload of >7217 hospitalisations per year for institutions and a population size >363,522 for healthcare regions were indicators of an acceptable diagnostic performance. Among hospitals, there was notable heterogeneity of diagnostic diversity, which was strongly associated with caseload. Application of misdiagnosis-thresholds within each ICD-10-GM category allowed classification of hospitals in four distinct groups: high-volume hospitals with an all-over comprehensive diagnostic performance; high- to mid-volume hospitals with extensive to relevant basic diagnostic performance in most categories; low-volume specialised hospitals with a high diagnostic performance in a single category; and low-volume hospitals with inadequate diagnostic performance in all categories. The diagnostic diversity observed in the 26 Swiss healthcare regions showed relevant heterogeneity, an association with ICD-10-GM code utilisation, and was strongly associated with the size of the healthcare region. The limited diagnostic performance in small healthcare regions was partially, but not fully, compensated for by consumption of health services outside of their own healthcare region. CONCLUSION Calculation of the DDI from ICD-10 codes is easy and complements the information derived from other quality indicators as it sheds a light on the fitness of the institutionalised interplay between primary and specialised medical inpatient care.

Freezing of gait (FOG) in Parkinson’s disease (PD) occurs frequently in situations with high environmental complexity. The supplementary motor cortex (SMC) is regarded as a major network node that exerts cortical input for motor control in these situations. We aimed at assessing the impact of single-session (excitatory) intermittent theta burst stimulation (iTBS) of the SMC on established walking during FOG provoking situations such as passing through narrow spaces and turning for directional changes. Twelve PD patients with FOG underwent two visits in the off-medication state with either iTBS or sham stimulation. At each visit, spatiotemporal gait parameters were measured during walking without obstacles and in FOG-provoking situations before and after stimulation. When patients passed through narrow spaces, decreased stride time along with increased stride length and walking speed (i.e., improved gait) was observed after both sham stimulation and iTBS. These effects, particularly on stride time, were attenuated by real iTBS. During turning, iTBS resulted in decreased stride time along with unchanged stride length, a constellation compatible with increased stepping frequency. The observed iTBS effects are regarded as relative gait deterioration. We conclude that iTBS over the SMC increases stepping frequency in PD patients with FOG, particularly in FOG provoking situations.

This editorial of the Special Issue “ECG Monitoring System” provides a short overview of the 13 contributed articles published in this issue [...]

Abstract Background The six-minute walk test (6MWT) is commonly used to quantify exercise capacity in patients with several cardio-pulmonary diseases. Oxygen uptake ( V ̇ $\dot {\mathrm {V}}$ O2) kinetics during 6MWT typically follow 3 distinct phases (rest, exercise, recovery) that can be modeled by nonlinear regression. Simultaneous modeling of multiple kinetics requires nonlinear mixed models methodology. To the best of our knowledge, no such curve-fitting approach has been used to analyze multiple V ̇ $\dot {\mathrm {V}}$ O2 kinetics in both research and clinical practice so far. Methods In the present study, we describe functionality of the R package medrc that extends the framework of the commonly used packages drc and nlme and allows fitting nonlinear mixed effects models for automated nonlinear regression modeling. The methodology was applied to a data set including 6MWT V ̇ $\dot {\mathrm {V}}$ O2 kinetics from 61 patients with chronic obstructive pulmonary disease (disease severity stage II to IV). The mixed effects approach was compared to a traditional curve-by-curve approach. Results A six-parameter nonlinear regression model was jointly fitted to the set of V ̇ $\dot {\mathrm {V}}$ O2 kinetics. Significant differences between disease stages were found regarding steady state V ̇ $\dot {\mathrm {V}}$ O2 during exercise, V ̇ $\dot {\mathrm {V}}$ O2 level after recovery and V ̇ $\dot {\mathrm {V}}$ O2 inflection point in the recovery phase. Estimates obtained by the mixed effects approach showed standard errors that were consistently lower as compared to the curve-by-curve approach. Conclusions Hereby we demonstrate the novelty and usefulness of this methodology in the context of physiological exercise testing.

Sleep apnea (SA) is a prevalent disorder diagnosed by polysomnography (PSG) based on the number of apnea−hypopnea events per hour of sleep (apnea−hypopnea index, AHI). PSG is expensive and technically complex; therefore, its use is rather limited to the initial diagnostic phase and simpler devices are required for long-term follow-up. The validity of single-parameter wearable devices for the assessment of sleep apnea severity is still debated. In this context, a wearable electrocardiogram (ECG) acquisition system (ECG belt) was developed and its suitability for the classification of sleep apnea severity was investigated using heart rate variability analysis with or without data pre-filtering. Several classification algorithms were compared and support vector machine was preferred due to its simplicity and overall performance. Whole-night ECG signals from 241 patients with a suspicion of sleep apnea were recorded using both the ECG belt and patched ECG during PSG recordings. 65% of patients had an obstructive sleep apnea and the median AHI was 21 [IQR: 7−40] h − 1 . The classification accuracy obtained from the ECG belt (accuracy: 72%, sensitivity: 70%, specificity: 74%) was comparable to the patched ECG (accuracy: 74%, sensitivity: 88%, specificity: 61%). The highest classification accuracy was obtained for the discrimination between individuals with no or mild SA vs. moderate to severe SA. In conclusion, the ECG belt provided signals comparable to patched ECG and could be used for the assessment of sleep apnea severity, especially during follow-up.

BACKGROUND Sarcoidosis is a disorder of unknown aetiology. Most patients have steroid-responsive disease, but side effects and steroid resistance may necessitate alternative treatments. Endothelin has in-vitro fibrogenic activity and the endothelin system is activated in sarcoidosis. OBJECTIVES We studied the efficacy and safety of the endothelin receptor antagonist bosentan in sarcoidosis patients. METHODS In a prospective 12-month, double-blind, 1:1-randomised, placebo-controlled phase II trial, we assessed the effect of bosentan in patients with steroid-resistant sarcoidosis and with impaired exercise capacity and/or resting lung function. Primary endpoints were safety and overall response rate of total lung capacity, diffusion capacity, peak oxygen uptake, 6-minute walking distance and chest computed tomography score. Secondary endpoints included adverse events and quality of life. MAIN RESULTS Twenty patients were randomised. Three patients discontinued the study medication prematurely. No serious drug-related adverse events occurred. At 12 months no statistically significant differences were observed in the primary endpoints including total lung capacity, diffusion capacity, 6-minute walking distance, peak oxygen uptake, and computed tomography-score. Sixty-three percent of the patients treated with bosentan showed an increase of 10% in at least one of the primary endpoints, compared with 67% in the placebo group (p = 1). CONCLUSIONS There is no evidence to support efficacy of bosentan as an antifibrotic treatment for patients with steroid-resistant pulmonary sarcoidosis. Bosentan was well tolerated and no drug-related adverse effects were observed within the study population. Trial registration ISRCTN registry, ISRCTN73579020.

Nonlinear regression models are applied in a broad variety of scientific fields. Various R functions are already dedicated to fitting such models, among which the function nls() has a prominent position. Unlike linear regression fitting of nonlinear models relies on non-trivial assumptions and therefore users are required to carefully ensure and validate the entire modeling. Parameter estimation is carried out using some variant of the least- squares criterion involving an iterative process that ideally leads to the determination of the optimal parameter estimates. Therefore, users need to have a clear understanding of the model and its parameterization in the context of the application and data considered, an a priori idea about plausible values for parameter estimates, knowledge of model diagnostics procedures available for checking crucial assumptions, and, finally, an under- standing of the limitations in the validity of the underlying hypotheses of the fitted model and its implication for the precision of parameter estimates. Current nonlinear regression modules lack dedicated diagnostic functionality. So there is a need to provide users with an extended toolbox of functions enabling a careful evaluation of nonlinear regression fits. To this end, we introduce a unified diagnostic framework with the R package nlstools. In this paper, the various features of the package are presented and exemplified using a worked example from pulmonary medicine.

6-min walk tests (6MWT) are routinely performed in patients with chronic obstructive pulmonary disease (COPD). Oxygen uptake ([Formula: see text]) kinetics during 6MWT can be modeled and derived parameters provide indicators of patients' exercise capacity. Post-exercise [Formula: see text] recovery also provides important parameters of patients' fitness which has not been extensively investigated in COPD. Several nonlinear regression models with different underlying biological assumptions may be suitable for describing recovery kinetics. Multimodel inference (model averaging) can then be used to capture the uncertainty in considering several models. Our aim was to apply multimodel inference in order to better understand the physiological underpinnings of [Formula: see text] recovery after 6MWT in patients with COPD. 61 patients with COPD (stages 2 to 4) were included in this study. Oxygen kinetics during 6MWT were modeled using nonlinear regression. Three statistical approaches (mixed-effects, meta-analysis and weighted regression) were compared in order to summarize estimates obtained from multiple kinetics. The recovery phase was modeled using 3 distinct equations (log-logistic, Weibull 1 and Weibull 2). Three models were fitted to the set of 61 kinetics. A significant model-averaged difference of 40.39 sec (SE = 17.1) in the time to half decrease of [Formula: see text] level ([Formula: see text]) was found between stage 2 and 4 (p = 0.0178). In addition, the Weibull 1 model characterized by a steeper decrease at the beginning of the recovery phase showed some improvement of goodness of fit when fitted to the kinetics of patients with stage 2 COPD in comparison with the 2 other models. Multimodel inference was successfully used to model [Formula: see text] recovery after 6MWT in patients with COPD. Significant model-averaged differences in [Formula: see text] were found between moderate and very severe COPD patients. Furthermore, specific patterns of [Formula: see text] recovery could be identified across COPD stages.

Sleep monitoring in an unattended home setting provides important information complementing and extending the clinical polysomnography findings. The validity of a wearable textile electrocardiography (ECG)-belt has been proven in a clinical setting. For evaluation in a home setting, ECG signals and features were acquired from 12 patients (10 males and 2 females, showing an interquartile range for age of 48−59 years and for body mass indexes (BMIs) of 28.0−35.5) over 28 nights. The signal quality was assessed by artefacts detection, signal-to-noise ratio, and Poincaré plots. To assess the validity, the data were compared to previously reported data from the clinical setting. It was found that the artefact percentage was slightly reduced for the ECG-belt from 9.7% ± 14.7% in the clinical setting, to 7.5% ± 10.8% in the home setting. The signal-to-noise ratio was improved in the home setting and reached similar values to the gel electrodes in the clinical setting. Finally, it was found that for artefact percentages above 3%, Poincaré plots are instrumental to evaluate the origin of artefacts. In conclusion, the application of the ECG-belt in a home setting did not result in a reduction in signal quality compared to the ECG-belt used in the clinical setting, and thus provides new opportunities for patient pre-screening or follow-up.

Even for 1-lead electrocardiography (ECG), single-use gel conductive electrodes are employed in a clinical setting. However, gel electrodes show limited applicability for long-term monitoring due to skin irritation and detachment. In the present study, we investigated the validity of a textile ECG-belt suitable for long-term measurements in clinical use. In order to assess the signal quality and validity of the ECG-belt during sleep, 242 patients (186 males and 56 females, age 52 (interquartile range 42−60) years, body mass index 29 (interquartile range 26−33) kg·m−2) with suspected sleep apnoea underwent overnight polysomnography including standard 1-lead ECG recording. The single intervals between R-peaks (RR-intervals) were calculated from the ECG-signals. We found a mean difference for average RR-intervals of −2.9 ms, a standard error of estimate of 0.39%, as well as a Pearson r of 0.91. Furthermore, we found that the validity of the ECG-belt decreases when lying on the side, which was potentially due to the fitting of the belt. In conclusion, the validity of RR-interval measurements using the ECG-belt is high and it may be further improved for future applications by optimizing wear fitting.

QUESTIONS UNDER STUDY: Transcutaneous measurement of carbon dioxide (PtCO2) has been suggested as an alternative to invasively obtained PaCO2 for the monitoring of patients with hypercapnic respiratory failure during noninvasive ventilation (NIV). Current data on monitoring in hypoxaemic respiratory failure are scarce and show conflicting results in hypercapnic patients in the emergency department. METHODS AND SETTING: We performed a retrospective comparison of real-time PtCO2 (SenTec Digital Monitor) and arterial/venous carbon dioxide tension (PaCO2/PvCO2) measurements in patients with severe hypoxaemic and/or hypercapnic respiratory failure during NIV. Agreement between PtCO2 and PaCO2/PvCO2 was the primary endpoint. Bland-Altman analysis and linear regression were used. RESULTS: 102 patients had at least one matched measurement of PtCO2 and PaCO2/PvCO2. For patients with arterial blood gas analysis, the mean difference was 0.46 kPa at baseline (95% confidence interval [CI] 0.23 to 0.60, limits of agreement 95% CI –0.54 to 1.45) and 0.12 kPa after NIV (95% CI –0.04 to 0.29, limits of agreement 95% CI: –0.61 to 0.86). The linear regression analysis found a correlation R 2 of 0.88 (p 8 kPa was associated with a lesser degree of agreement between the levels of PtCO2 and PaCO2/PvCO2 (p

Sarcoidosis is a multisystem disease with an unpredictable and sometimes fatal course while the underlying pathomechanism is still unclear. Reasons of the increasing hospitalization rate and mortality in the United States remain in dispute but incriminated are a number of distinct comorbidities and risk factors as well as the application of more aggressive therapeutic agents. Studies reflecting the recent development in central Europe are lacking. Our aim was to investigate the recent mortality and hospitalization rates as well as the underlying comorbidities of hospitalized sarcoidosis patients in Switzerland. In this longitudinal, nested case-control study, a nation-wide database provided by the Swiss Federal Office for Statistics enclosing every hospital entry covering the years 2002-2012 (n = 15,627,573) was analyzed. There were 8,385 cases with a diagnosis of sarcoidosis representing 0.054% (8,385 / 15,627,573) of all hospitalizations in Switzerland. These cases were compared with age- and sex-matched controls without the diagnosis of sarcoidosis. Hospitalization and mortality rates in Switzerland remained stable over the observed time period. Comorbidity analysis revealed that sarcoidosis patients had significantly higher medication-related comorbidities compared to matched controls, probably due to systemic corticosteroids and immunosuppressive therapy. Sarcoidosis patients were also more frequently re-hospitalized (median annual hospitalization rate 0.28 [IQR 0.15-0.65] vs. 0.19 [IQR 0.13-0.36] per year; p < 0.001), had a longer hospital stay (6 [IQR 2-13] vs. 4 [IQR 1-8] days; p < 0.001), had more comorbidities (4 [IQR 2-7] vs. 2 [IQR 1-5]; p < 0.001), and had a significantly higher in-hospital mortality (2.6% [95% CI 2.3%-2.9%] vs. 1.8% [95% CI 1.5%-2.1%] (p < 0.001). A worse outcome was observed among sarcoidosis patients having co-occurrence of associated respiratory diseases. Moreover, age was an important risk factor for re-hospitalization.

Dose-response analysis can be carried out using multi-purpose commercial statistical software, but except for a few special cases the analysis easily becomes cumbersome as relevant, non-standard output requires manual programming. The extension package drc for the statistical environment R provides a flexible and versatile infrastructure for dose-response analyses in general. The present version of the package, reflecting extensions and modifications over the last decade, provides a user-friendly interface to specify the model assumptions about the dose-response relationship and comes with a number of extractors for summarizing fitted models and carrying out inference on derived parameters. The aim of the present paper is to provide an overview of state-of-the-art dose-response analysis, both in terms of general concepts that have evolved and matured over the years and by means of concrete examples.

BACKGROUND: The impact of chronic lung diseases on outcome in community-acquired pneumonia (CAP) is not well established. We aimed to investigate the outcome of adult CAP-patients with underlying chronic obstructive pulmonary disease (COPD), asthma or interstitial lung disease (ILD) in a case-control study. METHODS: We used a nationwide database including all hospitalisations in Switzerland from 2002 to 2010. Endpoints were the incidence of lung abscess, parapneumonic pleural effusion, empyema, acute respiratory distress syndrome, in-hospital mortality and length of stay. RESULTS: We found less disease-related complications of CAP in COPD (n = 17,075) and asthma (n = 2700) patients compared with their controls. This difference was mainly related to a lower incidence of pleural effusion (COPD: 4.3% vs 4.9%, p = 0.011; asthma: 3.4% vs 5.2%, p

Activating epidermal growth factor receptor (EGFR) mutations are recognized biomarkers for patients with metastatic non-small cell lung cancer (NSCLC) treated with EGFR tyrosine kinase inhibitors (TKIs). EGFR TKIs can also have activity against NSCLC without EGFR mutations, requiring the identification of additional relevant biomarkers. Previous studies on tumor EGFR protein levels and EGFR gene copy number revealed inconsistent results. The aim of the study was to identify novel biomarkers of the response to TKIs in NSCLC by investigating whole genome expression at the exon-level. We used exon arrays and clinical samples from a previous trial (SAKK19/05) to investigate the expression variations at the exon-level of 3 genes potentially playing a key role in modulating treatment response: EGFR, V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) and vascular endothelial growth factor (VEGFA). We identified the expression of EGFR exon 18 as a new predictive marker for patients with untreated metastatic NSCLC treated with bevacizumab and erlotinib in the first line setting. The overexpression of EGFR exon 18 in tumor was significantly associated with tumor shrinkage, independently of EGFR mutation status. A similar significant association could be found in blood samples. In conclusion, exonic EGFR expression particularly in exon 18 was found to be a relevant predictive biomarker for response to bevacizumab and erlotinib. Based on these results, we propose a new model of EGFR testing in tumor and blood.

COPD is associated with a relevant burden of disease and a high mortality worldwide. Only recently, the importance of comorbidities of COPD has been recognized. Studies postulated an association with inflammatory conditions potentially sharing pathogenic pathways and worsening overall prognosis. More evidence is required to estimate the role of comorbidities of COPD. Our aim was to investigate the prevalence and clustering of comorbidities associated with COPD, and to estimate their impact on clinically relevant outcomes. In this population-based case-control study, a nation-wide database provided by the Swiss Federal Office for Statistics enclosing every hospital entry covering the years 2002-2010 (n = 12'888'075) was analyzed using MySQL and R statistical software. Statistical methods included non-parametric hypothesis testing by means of Fisher's exact test and Wilcoxon rank sum test, as well as linear models with generalized estimating equation to account for intra-patient variability. Exploratory multivariate approaches were also used for the identification of clusters of comorbidities in COPD patients. In 2.6% (6.3% in patients aged >70 years) of all hospitalization cases an active diagnosis of COPD was recorded. In 21% of these cases, COPD was the main reason for hospitalization. Patients with a diagnosis of COPD had more comorbidities (7 [IQR 4-9] vs. 3 [IQR 1-6]; [Formula: see text]), were more frequently rehospitalized (annual hospitalization rate 0.33 [IQR 0.20-0.67] vs. 0.25 [IQR 0.14-0.43]/year; [Formula: see text]), had a longer hospital stay (9 [IQR 4-15] vs. 5 [IQR 2-11] days; [Formula: see text]), and had higher in-hospital mortality (5.9% [95% CI 5.8%-5.9%] vs. 3.4% [95% CI 3.3%-3.5%]; [Formula: see text]) compared to matched controls. A set of comorbidities was associated with worse outcome. We could identify COPD-related clusters of COPD-comorbidities.

BACKGROUND: Bone metastasis is a main cause of morbidity in breast cancer. Since breast cancer is a heterogeneous disease, the interactions of cancer cells with the skeletal host cells might also be diverse. We hypothesized that gene expression signatures induced by heterotypic interaction of breast cancer cells and osteoblasts might be of clinical relevance. METHODOLOGY/PRINCIPAL FINDINGS: We established an ex vivo co-culture model using benign breast epithelial cells or a panel of 5 malignant breast epithelial cells in combination with primary human osteoblasts and determined associated gene expression changes with HEEBO microarrays. Pretreatment gene expression profiles of 295 early stage breast cancers published from the Netherlands Cancer Institute with a median follow up of 12.6 years allowed evaluating in vitro effects in the in vivo situation.The effects of the interaction between osteoblasts and breast cancer cell lines of different origin were very heterogeneous. Hs578T cells started to proliferate in co-culture with osteoblasts, SKBR-3 induced a TGF-β response and MDA-MB231 cells showed two distinct sets of up-regulated genes: A set of interferon response genes associated with an up-regulation of STAT1 was in vivo remarkably coherent providing a basis for segregation of tumors into two groups. In a uni-variate analysis, early stage tumors with high expression levels (n = 136) of this gene set had a significantly lower overall survival rate (p = 0.005) (63% at 10 years) than tumors with low expression levels (n = 159) (overall survival: 77% at 10 years). The second gene set was associated with IL-6 and did not significantly change the overall survival rate (p = 0.165), but was significantly associated with a shorter time to bone metastasis (p = 0.049; 74% vs. 83% at 10 years). CONCLUSION/SIGNIFICANCE: An IL-6 gene expression pattern induced by heterotypic interaction of breast cancer cells with osteoblasts in vitro is associated with a higher rate of bone metastasis in vivo.