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.

Pulmonary Surfactant Proteins are Inhibited by IgA 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 bronchoalveolar lavage 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 bronchoalveolar lavage 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 against pulmonary surfactant proteins B and C and that these antibodies block the function of lung surfactant, potentially contributing to alveolar collapse and poor oxygenation. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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.

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.

Inhaled aviptadil for the possible treatment of COVID-19 in patients at high risk for ARDS: study protocol for a randomized, placebo-controlled, and multicenter trial.

BACKGROUND: Despite the fast establishment of new therapeutic agents in the management of COVID-19 and large-scale vaccination campaigns since the beginning of the SARS-CoV-2 pandemic in early 2020, severe disease courses still represent a threat, especially to patients with risk factors. This indicates the need for alternative strategies to prevent respiratory complications like acute respiratory distress syndrome (ARDS) associated with COVID-19. Aviptadil, a synthetic form of human vasoactive intestinal peptide, might be beneficial for COVID-19 patients at high risk of developing ARDS because of its ability to influence the regulation of exaggerated pro-inflammatory proteins and orchestrate the lung homeostasis. Aviptadil has recently been shown to considerably improve the prognosis of ARDS in COVID-19 when applied intravenously. An inhaled application of aviptadil has the advantages of achieving a higher concentration in the lung tissue, fast onset of activity, avoiding the hepatic first-pass metabolism, and the reduction of adverse effects. The overall objective of this project is to assess the efficacy and safety of inhaled aviptadil in patients hospitalized for COVID-19 at high risk of developing ARDS. METHODS: This multicenter, placebo-controlled, double-blinded, randomized trial with 132 adult patients hospitalized for COVID-19 and at high risk for ARDS (adapted early acute lung injury score ≥ 2 points) is conducted in five public hospitals in Europe. Key exclusion criteria are mechanical ventilation at baseline, need for intensive care at baseline, and severe hemodynamic instability. Patients are randomly allocated to either inhale 67 µg aviptadil or normal saline (three times a day for 10 days), in addition to standard care, stratified by center. The primary endpoint is time from hospitalization to clinical improvement, defined as either hospital discharge, or improvement of at least two levels on the nine-level scale for clinical status suggested by the World Health Organization. DISCUSSION: Treatment strategies for COVID-19 are still limited. In the context of upcoming new variants of SARS-CoV-2 and possible inefficacy of the available vaccines and antibody therapies, the investigation of alternative therapy options plays a crucial role in decreasing associated mortality and improving prognosis. Due to its unique immunomodulating properties also targeting the SARS-CoV-2 pathways, inhaled aviptadil may have the potential to prevent ARDS in COVID-19. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04536350 . Registered 02 September 2020.

Frailty assessment for COVID-19 follow-up: a prospective cohort study.

BACKGROUND: The Clinical Frailty Scale (CFS) is increasingly used for clinical decision making in acute care but little is known about frailty after COVID-19. OBJECTIVES: To investigate frailty and the CFS for post-COVID-19 follow-up. METHODS: This prospective multicentre cohort study included COVID-19 survivors aged ≥50 years presenting for a follow-up visit ≥3 months after the acute illness. Nine centres retrospectively collected pre-COVID-19 CFS and prospectively CFS at follow-up. Three centres completed the Frailty Index (FI), the short physical performance battery (SPPB), 30 s sit-to-stand test and handgrip strength measurements. Mixed effect logistic regression models accounting for repeated measurements and potential confounders were used to investigate factors associated with post-COVID-19 CFS. Criterion and construct validity were determined by correlating the CFS to other concurrently assessed frailty measurements and measures of respiratory impairment, respectively. RESULTS: Of the 288 participants 65% were men, mean (SD) age was 65.1 (9) years. Median (IQR) CFS at follow-up was 3 (2-3), 21% were vulnerable or frail (CFS ≥4). The CFS was responsive to change, correlated with the FI (r=0.69, p<0.001), the SPPB score (r=-0.48, p<0.001) (criterion validity) and with the St George's Respiratory Questionnaire score (r=0.59, p<0.001), forced vital capacity %-predicted (r=-0.25, p<0.001), 6 min walk distance (r=-0.39, p<0.001) and modified Medical Research Council (mMRC) (r=0.59, p<0.001). Dyspnoea was significantly associated with a higher odds for vulnerability/frailty (per one mMRC adjusted OR 2.01 (95% CI 1.13 to 3.58), p=0.02). CONCLUSIONS: The CFS significantly increases with COVID-19, and dyspnoea is an important risk factor for post-COVID-19 frailty and should be addressed thoroughly.