Effect of the Covid-19 pandemic on hospitalizations for non-Covid-19-pneumonia and exacerbations of chronic obstructive pulmonary diseases in Switzerland: comparison of national data between 2020/2021 and 2015-2019.

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 < 0.001, and IRR: 0.62, 95% CI: 0.52-0.74, p < 0.001, respectively). Hospital-mortality was slightly higher in 2020/2021 for AECOPD (2015-2019: 3.8%; 2020/2021: 4.2%, odds ratio [OR] 1.24, 95% CI: 1.07-1.44, p = 0.004) and for pneumonia (2015-2019: 4.5%, 2020/2021: 4.6%, odds ratio [OR] 1.17, 95% CI: 1.07-1.28, p < 0.001). Median LOS slightly decreased for AECOPD (2015-2019: 8 [IQR: 5-14] days; 2020/2021: 7 [IQR: 4-13] days, Wilcoxon test: p < 0.001) but slightly increased for pneumonia (2015-2019: 7 [IQR: 4-11] days; 2020/2021: 7 [IQR: 4-13] days, Wilcoxon test: p < 0.001). Throughout 2020/2021, there were no significant fluctuations observed in the incidences of ACS and stroke. (IRR: 0.98, 95% CI: 0.83-1.16, p = 0.810, IRR: 0.96, 95% CI: 0.81-1.14, p = 0.636, respectively). CONCLUSION: The first two years of the Covid-19 pandemic showed a marked decrease in incidences in AECOPD and non-Covid-19 pneumonia hospitalizations in Switzerland. It is likely that this effect is associated with the society-based, at first vigorous, social distancing measures.

Validation of a Textile-Based Wearable Measuring Electrocardiogram and Breathing Frequency for Sleep Apnea Monitoring.

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.

Tracking Real-World Physical Activity in Chronic Obstructive Pulmonary Disease Over One Year: Results from a Monocentric, Prospective, Observational Cohort Study.

Introduction: Lung function constraints and comorbidities such as coronary heart disease, sarcopenia, and mood disorders make chronic obstructive pulmonary disease (COPD) patients avoid physical activity (PA). However, PA represents an important pillar of COPD management and is explicitly recommended by professional associations to enhance physical functioning and positively modulate disease progression. Methods: In this monocentric, prospective, observational feasibility study, it was our primary objective to investigate the association between PA and the evolution of the COPD assessment test (CAT) and the occurrence of acute exacerbations of COPD (AECOPD), respectively. To this end, we equipped 42 COPD patients with an activity tracking wearable and telemonitored their daily PA levels over one year using a dedicated web-based interface. Patients additionally provided weekly CAT scores using the same telehealth platform and came in for 3 study visits to assess functional parameters and biochemical markers related to nutrition and inflammation. Results: A principal study finding was that PA was inversely associated with CAT score (drop of 0.21 points associated with an increase of 1000 daily steps, p = 0.004), and that the 50% of patients with higher PA levels showed less CAT score progression over time (0.42 points per year) than the 50% of patients with lower PA levels (3.26 points per year) (p < 0.001). In addition, higher PA levels were significantly associated with a lower likelihood of experiencing a moderate-to-severe AECOPD (31% risk reduction associated with an increase of 1000 daily steps, p = 0.0097). Discussion: Our study demonstrates the relevance of PA for key COPD outcome metrics in a real-world setting and underpins the importance of PA for COPD self-management in everyday life. Our study paves the way for future intervention trials to prospectively identify medically relevant PA thresholds and establish training recommendations for different patient subgroups.

Quantification of breathing irregularity for the diagnosis of dysfunctional breathing using proportional tidal volume variation: a cross-sectional, retrospective real-world study.

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.

Impact of the 2022 pulmonary hypertension definition on haemodynamic classification and mortality in patients with aortic stenosis undergoing valve replacement.

Aims: With the 2022 pulmonary hypertension (PH) definition, the mean pulmonary artery pressure (mPAP) threshold for any PH was lowered from ≥25 to >20 mmHg, and the pulmonary vascular resistance (PVR) value to differentiate between isolated post-capillary PH (IpcPH) and combined pre- and post-capillary PH (CpcPH) was reduced from >3 Wood units (WU) to >2 WU. We assessed the impact of this change in the PH definition in aortic stenosis (AS) patients undergoing aortic valve replacement (AVR). Methods and results: Severe AS patients (n = 503) undergoing pre-AVR cardiac heart catheterization were classified according to both the 2015 and 2022 definitions. The post-AVR mortality [median follow-up 1348 (interquartile range 948-1885) days] was assessed. According to the 2015 definition, 219 (44% of the entire population) patients had PH: 63 (29%) CpcPH, 125 (57%) IpcPH, and 31 (14%) pre-capillary PH. According to the 2022 definition, 321 (+47%) patients were diagnosed with PH, and 156 patients (31%) were re-classified: 26 patients from no PH to IpcPH, 38 from no PH to pre-capillary PH, 38 from no PH to unclassified PH, 4 from pre-capillary PH to unclassified PH, and 50 from IpcPH to CpcPH (CpcPH: +79%). With both definitions, only the CpcPH patients displayed increased mortality (hazard ratios ≈ 4). Among the PH-defining haemodynamic components, PVR was the strongest predictor of death. Conclusion: In severe AS, the application of the 2022 PH definition results in a substantially higher number of patients with any PH as well as CpcPH. With either definition, CpcPH patients have a significantly increased post-AVR mortality.

Autoimmunity Against Surfactant Protein B Is Associated with Pneumonitis During Checkpoint Blockade.

Rationale: Immune checkpoint inhibitor (ICI)-related pneumonitis is a serious autoimmune event affecting as many as 20% of patients with non-small-cell lung cancer (NSCLC), yet the factors underpinning its development in some patients and not others are poorly understood. Objectives: To investigate the role of autoantibodies and autoreactive T cells against surfactant-related proteins in the development of pneumonitis. Methods: The study cohort consisted of patients with NSCLC who provided blood samples before and during ICI treatment. Serum was used for proteomics analyses and to detect autoantibodies present during pneumonitis. T-cell stimulation assays and single-cell RNA sequencing were performed to investigate the specificity and functionality of peripheral autoreactive T cells. The findings were confirmed in a validation cohort comprising patients with NSCLC and patients with melanoma. Measurements and Main Results: Across both cohorts, patients in whom pneumonitis developed had higher pretreatment levels of immunoglobulin G autoantibodies targeting surfactant protein (SP)-B. At the onset of pneumonitis, these patients also exhibited higher frequencies of CD4+ IFN-γ-positive SP-B-specific T cells and expanding T-cell clonotypes recognizing this protein, accompanied by a proinflammatory serum proteomic profile. Conclusions: Our data suggest that the cooccurrence of SP-B-specific immunoglobulin G autoantibodies and CD4+ T cells is associated with the development of pneumonitis during ICI therapy. Pretreatment levels of these antibodies may represent a potential biomarker for an increased risk of developing pneumonitis, and on-treatment levels may provide a diagnostic aid.

Electronic Nicotine-Delivery Systems for Smoking Cessation.

BACKGROUND: Electronic nicotine-delivery systems - also called e-cigarettes - are used by some tobacco smokers to assist with quitting. Evidence regarding the efficacy and safety of these systems is needed. METHODS: In this open-label, controlled trial, we randomly assigned adults who were smoking at least five tobacco cigarettes per day and who wanted to set a quit date to an intervention group, which received free e-cigarettes and e-liquids, standard-of-care smoking-cessation counseling, and optional (not free) nicotine-replacement therapy, or to a control group, which received standard counseling and a voucher, which they could use for any purpose, including nicotine-replacement therapy. The primary outcome was biochemically validated, continuous abstinence from smoking at 6 months. Secondary outcomes included participant-reported abstinence from tobacco and from any nicotine (including smoking, e-cigarettes, and nicotine-replacement therapy) at 6 months, respiratory symptoms, and serious adverse events. RESULTS: A total of 1246 participants underwent randomization; 622 participants were assigned to the intervention group, and 624 to the control group. The percentage of participants with validated continuous abstinence from tobacco smoking was 28.9% in the intervention group and 16.3% in the control group (relative risk, 1.77; 95% confidence interval, 1.43 to 2.20). The percentage of participants who abstained from smoking in the 7 days before the 6-month visit was 59.6% in the intervention group and 38.5% in the control group, but the percentage who abstained from any nicotine use was 20.1% in the intervention group and 33.7% in the control group. Serious adverse events occurred in 25 participants (4.0%) in the intervention group and in 31 (5.0%) in the control group; adverse events occurred in 272 participants (43.7%) and 229 participants (36.7%), respectively. CONCLUSIONS: The addition of e-cigarettes to standard smoking-cessation counseling resulted in greater abstinence from tobacco use among smokers than smoking-cessation counseling alone. (Funded by the Swiss National Science Foundation and others; ESTxENDS ClinicalTrials.gov number, NCT03589989.).

Assessment of functional diversities in patients with Asthma, COPD, Asthma-COPD overlap, and Cystic Fibrosis (CF).

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.

Non-pharmaceutical interventions to optimize cancer immunotherapy.

The 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.

Gender-specific disease trajectories prior to the onset of COPD allow individualized screening and early intervention.

BACKGROUND: Nation-wide hospitalization databases include diagnostic information at the level of an entire population over an extended period of time. Comorbidity network and early disease development can be unveiled. Chronic obstructive pulmonary disease (COPD) is an underdiagnosed condition for which it is crucial to identify early disease indicators. The identification of gender-specific conditions preceding the onset of COPD may reveal disease progression patterns allowing for early diagnosis and intervention. The objective of the study was to investigate the antecedent hospitalization history of patients newly diagnosed with COPD and to retrace a gender-specific trajectory of coded entities prior to the onset of COPD. MATERIAL AND METHODS: A population-wide hospitalization database including information about all hospitalizations in Switzerland between 2002 and 2018 was used. COPD cases were extracted from the database and comorbidities occurring prior to the onset of COPD identified. Comorbidities significantly over-represented in COPD compared with a 1:1, age- and sex-matched control population were identified and their longitudinal evolution was analyzed. RESULTS: Between 2002 and 2018, 697,714 hospitalizations with coded COPD were recorded in Switzerland. Sixty-two diagnoses were significantly over-represented before onset of COPD. These preceding comorbidities included both well-established conditions and novel links to COPD. Early pre-conditions included nicotine and alcohol abuse, obesity and cardiovascular diseases. Later comorbidities included atrial fibrillation, diseases of the genitourinary system and pneumonia. Atherosclerotic heart diseases were more prevalent in males, whereas hypothyroidism, varicose and intestinal disorders were more frequent in females. Disease trajectories were validated using an independent data set. CONCLUSIONS: Gender-specific disease trajectories highlight early indicators and pathogenetic links between COPD and antecedent diseases and could allow for early detection and intervention.

Smartphone-based cough monitoring as a near real-time digital pneumonia biomarker.

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.

Azithromycin alters spatial and temporal dynamics of airway microbiota in idiopathic pulmonary fibrosis.

Background: High bacterial burden in the lung microbiota predicts progression of idiopathic pulmonary fibrosis (IPF). Azithromycin (AZT) is a macrolide antibiotic known to alter the lung microbiota in several chronic pulmonary diseases, and observational studies have shown a positive effect of AZT on mortality and hospitalisation rate in IPF. However, the effect of AZT on the lung microbiota in IPF remains unknown. Methods: We sought to determine the impact of a 3-month course of AZT on the lung microbiota in IPF. We assessed sputum and oropharyngeal swab specimens from 24 adults with IPF included in a randomised controlled crossover trial of oral AZT 500 mg 3 times per week. 16S rRNA gene amplicon sequencing and quantitative PCR (qPCR) were performed to assess bacterial communities. Antibiotic resistance genes (ARGs) were assessed using real-time qPCR. Results: AZT significantly decreased community diversity with a stronger and more persistent effect in the lower airways (sputum). AZT treatment altered the temporal kinetics of the upper (oropharyngeal swab) and lower airway microbiota, increasing community similarity between the two sites for 1 month after macrolide cessation. Patients with an increase in ARG carriage had lower bacterial density and enrichment of the genus Streptococcus. In contrast, patients with more stable ARG carriage had higher bacterial density and enrichment in Prevotella. Conclusions: AZT caused sustained changes in the diversity and composition of the upper and lower airway microbiota in IPF, with effects on the temporal and spatial dynamics between the two sites.

Are Predictors for Overall Mortality in COPD Patients Robust over Time?

(1) Background: Mortality is a major outcome in research on chronic obstructive pulmonary disease (COPD) with various predictors described. However, the dynamic courses of important predictors over time are disregarded. This study evaluates if longitudinal assessment of predictors provides additional information on the mortality risk in COPD when compared with a cross-sectional analysis.; (2) In a longitudinal, prospective, non-interventional cohort study including mild to very severe COPD patients, mortality and its various possible predictors were annually assessed up to seven years.; (3) Results: 297 patients were analysed. Mean (SD) age was 62.5 (7.6) years and 66% males. Mean (SD) FEV1 was 48.8 (21.4)%. A total of 105 events (35.4%) happened with a median (95% CI) survival time of 8.2 (7.2/NA) years. No evidence for a difference between the raw variable and the variable history on the predictive value for all tested variables over each visit was found. There was no evidence for changing effect estimates (coefficients) across the study visits due to the longitudinal assessment; (4) Conclusions: We found no evidence that predictors of mortality in COPD are time dependent. This implies that cross-sectional measured predictors show robust effect estimates over time and multiple assessments seem not to change the predictive value of the measure.

Nighttime Continuous Contactless Smartphone-Based Cough Monitoring for the Ward: Validation Study.

BACKGROUND: Clinical deterioration can go unnoticed in hospital wards for hours. Mobile technologies such as wearables and smartphones enable automated, continuous, noninvasive ward monitoring and allow the detection of subtle changes in vital signs. Cough can be effectively monitored through mobile technologies in the ward, as it is not only a symptom of prevalent respiratory diseases such as asthma, lung cancer, and COVID-19 but also a predictor of acute health deterioration. In past decades, many efforts have been made to develop an automatic cough counting tool. To date, however, there is neither a standardized, sufficiently validated method nor a scalable cough monitor that can be deployed on a consumer-centric device that reports cough counts continuously. These shortcomings limit the tracking of coughing and, consequently, hinder the monitoring of disease progression in prevalent respiratory diseases such as asthma, chronic obstructive pulmonary disease, and COVID-19 in the ward. OBJECTIVE: This exploratory study involved the validation of an automated smartphone-based monitoring system for continuous cough counting in 2 different modes in the ward. Unlike previous studies that focused on evaluating cough detection models on unseen data, the focus of this work is to validate a holistic smartphone-based cough detection system operating in near real time. METHODS: Automated cough counts were measured consistently on devices and on computers and compared with cough and noncough sounds counted manually over 8-hour long nocturnal recordings in 9 patients with pneumonia in the ward. The proposed cough detection system consists primarily of an Android app running on a smartphone that detects coughs and records sounds and secondarily of a backend that continuously receives the cough detection information and displays the hourly cough counts. Cough detection is based on an ensemble convolutional neural network developed and trained on asthmatic cough data. RESULTS: In this validation study, a total of 72 hours of recordings from 9 participants with pneumonia, 4 of whom were infected with SARS-CoV-2, were analyzed. All the recordings were subjected to manual analysis by 2 blinded raters. The proposed system yielded a sensitivity and specificity of 72% and 99% on the device and 82% and 99% on the computer, respectively, for detecting coughs. The mean differences between the automated and human rater cough counts were -1.0 (95% CI -12.3 to 10.2) and -0.9 (95% CI -6.5 to 4.8) coughs per hour within subject for the on-device and on-computer modes, respectively. CONCLUSIONS: The proposed system thus represents a smartphone cough counter that can be used for continuous hourly assessment of cough frequency in the ward.

Pulmonary Recovery 12 Months after Non-Severe and Severe COVID-19: The Prospective Swiss COVID-19 Lung Study.

BACKGROUND: Lung function impairment persists in some patients for months after acute coronavirus disease 2019 (COVID-19). Long-term lung function, radiological features, and their association remain to be clarified. OBJECTIVES: We aimed to prospectively investigate lung function and radiological abnormalities over 12 months after severe and non-severe COVID-19. METHODS: 584 patients were included in the Swiss COVID-19 lung study. We assessed lung function at 3, 6, and 12 months after acute COVID-19 and compared chest computed tomography (CT) imaging to lung functional abnormalities. RESULTS: At 12 months, diffusion capacity for carbon monoxide (DLCOcorr) was lower after severe COVID-19 compared to non-severe COVID-19 (74.9% vs. 85.2% predicted, p < 0.001). Similarly, minimal oxygen saturation on 6-min walk test and total lung capacity were lower after severe COVID-19 (89.6% vs. 92.2%, p = 0.004, respectively, 88.2% vs. 95.1% predicted, p = 0.011). The difference for forced vital capacity (91.6% vs. 96.3% predicted, p = 0.082) was not statistically significant. Between 3 and 12 months, lung function improved in both groups and differences in DLCO between non-severe and severe COVID-19 patients decreased. In patients with chest CT scans at 12 months, we observed a correlation between radiological abnormalities and reduced lung function. While the overall extent of radiological abnormalities diminished over time, the frequency of mosaic attenuation and curvilinear patterns increased. CONCLUSIONS: In this prospective cohort study, patients who had severe COVID-19 had diminished lung function over the first year compared to those after non-severe COVID-19, albeit with a greater extent of recovery in the severe disease group.

Pulmonary Surfactant Proteins Are Inhibited by Immunoglobulin A Autoantibodies in Severe COVID-19.

Rationale: Coronavirus disease 2019 (COVID-19) can lead to acute respiratory distress syndrome with fatal outcomes. Evidence suggests that dysregulated immune responses, including autoimmunity, are key pathogenic factors. Objectives: To assess whether IgA autoantibodies target lung-specific proteins and contribute to disease severity. Methods: We collected 147 blood, 9 lung tissue, and 36 BAL fluid samples from three tertiary hospitals in Switzerland and one in Germany. Severe COVID-19 was defined by the need to administer oxygen. We investigated the presence of IgA autoantibodies and their effects on pulmonary surfactant in COVID-19 using the following methods: immunofluorescence on tissue samples, immunoprecipitations followed by mass spectrometry on BAL fluid samples, enzyme-linked immunosorbent assays on blood samples, and surface tension measurements with medical surfactant. Measurements and Main Results: IgA autoantibodies targeting pulmonary surfactant proteins B and C were elevated in patients with severe COVID-19 but not in patients with influenza or bacterial pneumonia. Notably, pulmonary surfactant failed to reduce surface tension after incubation with either plasma or purified IgA from patients with severe COVID-19. Conclusions: Our data suggest that patients with severe COVID-19 harbor IgA autoantibodies against pulmonary surfactant proteins B and C and that these autoantibodies block the function of lung surfactant, potentially contributing to alveolar collapse and poor oxygenation.

Compartmentalization of the host microbiome: how tumor microbiota shapes checkpoint immunotherapy outcome and offers therapeutic prospects.

The host microbiome is polymorphic, compartmentalized, and composed of distinctive tissue microbiomes. While research in the field of cancer immunotherapy has provided an improved understanding of the interaction with the gastrointestinal microbiome, the significance of the tumor-associated microbiome has only recently been grasped. This article provides a state-of-the-art review about the tumor-associated microbiome and sheds light on how local tumor microbiota shapes anticancer immunity and influences checkpoint immunotherapy outcome. The direct route of interaction between cancer cells, immune cells, and microbiota in the tumor microenvironment is emphasized and advocates a focus on the tumor-associated microbiome in addition to the spatially separated gut compartment. Since the mechanisms underlying checkpoint immunotherapy modulation by tumor-associated microbiota remain largely elusive, future research should dissect the pathways involved and outline strategies to therapeutically modulate microbes and their products within the tumor microenvironment. A more detailed knowledge about the mechanisms governing the composition and functional quality of the tumor microbiome will improve cancer immunotherapy and advance precision medicine for solid tumors.

Identification of smoking cessation phenotypes as a basis for individualized counseling: An explorative real-world cohort study.

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.