ABSTRACT
Non-invasive oxygen saturation (SpO2) is a central vital sign used to shape the management of COVID-19 patients. Yet, there have been no report quantitatively describing SpO2 dynamics and patterns in COVID-19 patients using continuous SpO2 recordings. We performed a retrospective observational analysis of the clinical information and 27 K hours of continuous SpO2 high-resolution (1 Hz) recordings of 367 critical and non-critical COVID-19 patients hospitalised at the Rambam Health Care Campus, Haifa, Israel. An absolute SpO2 threshold of 93% most efficiently discriminated between critical and non-critical patients, regardless of oxygen support. Oximetry-derived digital biomarker (OBMs) computed per 1 h monitoring window showed significant differences between groups, notably the cumulative time below 93% SpO2 (CT93). Patients with CT93 above 60% during the first hour of monitoring, were more likely to require oxygen support. Mechanical ventilation exhibited a strong effect on SpO2 dynamics by significantly reducing the frequency and depth of desaturations. OBMs related to periodicity and hypoxic burden were markedly affected, up to several hours before the initiation of the mechanical ventilation. In summary, OBMs, traditionally used in the field of sleep medicine research, are informative for continuous assessment of disease severity and response to respiratory support of hospitalised COVID-19 patients. In conclusion, OBMs may improve risk stratification and therapy management of critical care patients with respiratory impairment.
Subject(s)
COVID-19 , Humans , COVID-19/therapy , Retrospective Studies , Oximetry , Oxygen , Respiratory RateABSTRACT
Representation of one-dimensional (1D) signals as surfaces and higher-dimensional manifolds reveals geometric structures that can enhance assessment of signal similarity and classification of large sets of signals. Motivated by this observation, we propose a novel robust algorithm for extraction of geometric features, by mapping the obtained geometric objects into a reference domain. This yields a set of highly descriptive features that are instrumental in feature engineering and in analysis of 1D signals. Two examples illustrate applications of our approach to well-structured audio signals: Lung sounds were chosen because of the interest in respiratory pathologies caused by the coronavirus and environmental conditions; accent detection was selected as a challenging speech analysis problem. Our approach outperformed baseline models under all measured metrics. It can be further extended by considering higher-dimensional distortion measures. We provide access to the code for those who are interested in other applications and different setups (Code: https://github.com/jeremy-levy/Classification-of-audio-signals-using-spectrogram-surfaces-and-extrinsic-distortion-measures). Supplementary Information: The online version contains supplementary material available at 10.1186/s13634-022-00933-9.
ABSTRACT
BACKGROUND: The COVID-19 pandemic necessitated changes to the traditional medical ward round to protect staff and patients. This study investigated the value and acceptability of using the Microsoft HoloLens 2 mixed reality headset in a COVID-19 renal medicine ward. METHODS: The HoloLens 2 was used during the height of the COVID-19 pandemic and it was compared with the days prior to its introduction. Staff exposure to COVID-19 and PPE usage were measured, and staff and patients were surveyed on the HoloLens 2 experience. RESULTS: The average ward round was significantly shorter with the use of the HoloLens 2 (94 minutes vs 137 minutes; p=0.006). With the HoloLens 2, only the consultant was in direct contact with COVID-19 patients compared with up to seven staff members on a normal ward round. Personal protective equipment usage was reduced by over 50%. Both staff and patients were positive about its use but raised some important concerns. CONCLUSION: The HoloLens 2 mixed reality technology is an innovative solution to the challenges posed by COVID-19 to the traditional medical ward round.