Home monitoring of lung mechanics by oscillometry before, during and after severe COVID-19 disease: a case study.

A patient regularly self-performing home oscillometry developed severe COVID-19 pneumonia and continued testing during and after the disease. COVID-19 suddenly worsened oscillatory reactance, which took almost 1 year to recover to pre-COVID-19 values. https://bit.ly/3WCpWC0.

Home monitoring of lung mechanics by oscillometry before, during and after severe COVID-19 disease: a case study

In November 2020, Europe faced the second Covid-19 pandemic wave. The need to manage patients while reducing potential exposure and healthcare system overload led to renewed interest in home-monitoring of respiratory variables. Together with variables related to COVID-19 infection [1], home-based lung imaging [2] and lung mechanics [3, 4] were used to monitor COVID-19 and chronic respiratory patients during limited access to traditional care. Home monitoring respiratory-specific variables may provide important information about patient health status and clinical course.

An Experimental Apparatus for E-Nose Breath Analysis in Respiratory Failure Patients.

BACKGROUND: Non-invasive, bedside diagnostic tools are extremely important for tailo ring the management of respiratory failure patients. The use of electronic noses (ENs) for exhaled breath analysis has the potential to provide useful information for phenotyping different respiratory disorders and improving diagnosis, but their application in respiratory failure patients remains a challenge. We developed a novel measurement apparatus for analysing exhaled breath in such patients. METHODS: The breath sampling apparatus uses hospital medical air and oxygen pipeline systems to control the fraction of inspired oxygen and prevent contamination of exhaled gas from ambient Volatile Organic Compounds (VOCs) It is designed to minimise the dead space and respiratory load imposed on patients. Breath odour fingerprints were assessed using a commercial EN with custom MOX sensors. We carried out a feasibility study on 33 SARS-CoV-2 patients (25 with respiratory failure and 8 asymptomatic) and 22 controls to gather data on tolerability and for a preliminary assessment of sensitivity and specificity. The most significant features for the discrimination between breath-odour fingerprints from respiratory failure patients and controls were identified using the Boruta algorithm and then implemented in the development of a support vector machine (SVM) classification model. RESULTS: The novel sampling system was well-tolerated by all patients. The SVM differentiated between respiratory failure patients and controls with an accuracy of 0.81 (area under the ROC curve) and a sensitivity and specificity of 0.920 and 0.682, respectively. The selected features were significantly different in SARS-CoV-2 patients with respiratory failure versus controls and asymptomatic SARS-CoV-2 patients (p < 0.001 and 0.046, respectively). CONCLUSIONS: the developed system is suitable for the collection of exhaled breath samples from respiratory failure patients. Our preliminary results suggest that breath-odour fingerprints may be sensitive markers of lung disease severity and aetiology.