ABSTRACT
Introduction: We investigated a novel technique designed to measure tidal volume during non-invasive helmet continuous-flow CPAP, a device for non-invasive respiratory support largely used during the recent COVID-19 pandemic to treat acutely ill hypoxic respiratory failure patients. Method(s): An active lung simulator coupled with a helmet CPAP was used to compare measured and Reference tidal volumes at PEEP 5, 10 and 15 cmH2O and different levels of distress (pMusc 10, 15 20 and 25 cmH2O;respiratory rate 15, 20, 25 breaths per minutes). Tidal volume measurement was based on helmet outflow-trace analysis. Helmet inflow was increased from 60 to 75 and 90 L/min to match patients' inspiratory flow;an additional subset of tests was conducted in condition of purposely insufficient inflow (i.e.: high respiratory distress and 60 L/min inflow). Result(s): Explored tidal volumes ranged from 250 to 910 mL. The Bland-Altman analysis showed a bias of -3.2 +/- 29.3 mL for measured tidal volumes as compared to Reference, corresponding to an average relative error of -1 +/- 4.4% (see Fig. 1). At univariate analyses, tidal volume underestimation correlated with respiratory rate (rho = .411, p = .004) but not with peak inspiratory flow, distress, or PEEP. When the helmet inflow was purposely maintained insufficient as compared to the simulated inspiratory flow, the Bland-Altman analysis showed a significant tidal volume underestimation (bias -93.3 +/- 83.9 mL), corresponding to an error of -14.8 +/- 6.3%. Conclusion(s): We showed that tidal volume measurement is feasible and accurate in a model of bench continuous-flow helmet CPAP therapy by the analysis of the outflow signal, provided that helmet inflow is maintained adequate to match patient's inspiratory efforts. Insufficient inflow resulted in tidal volume underestimation.
ABSTRACT
BACKGROUND: Helmet continuous positive airway pressure (CPAP) has been widely used during the COVID-19 pandemic. Specific filters (i.e. High Efficiency Particulate Air filter: HEPA; Heat & Moisture Exchanger Filter: HMEF) were used to prevent Sars-CoV2 environmental dispersion and were connected to the CPAP helmet. However, HEPA and HMEF filters may act as resistors to expiratory gas flow and increase the levels of pressure within the hood. METHODS: In a bench-top study, we investigated the levels of airway pressure generated by different HEPA and HMEF filters connected to the CPAP helmet in the absence of a Positive End Expiratory Pressure (PEEP) valve and with two levels of PEEP (5 and 10 cmH2O). All steps were performed using 3 increasing levels of gas flow (60, 80, 100 L/min). RESULTS: The use of 8 different commercially available filters significantly increased the pressure within the hood of the CPAP helmet with or without the use of PEEP valves. On average, the increase of pressure above the set PEEP ranged from 3 cmH2O to 10 cmH2O across gas flow rates of 60 to 100 L/min. The measure of airway pressure was highly correlated between the laboratory pressure transducer and the Helmet manometer. Bias with 95% Confidence Interval of Bias between the devices was 0.7 (-2.06; 0.66) cmH2O. CONCLUSIONS: The use of HEPA and HMEF filters placed before the PEEP valve at the expiratory port of the CPAP helmet significantly increase the levels of airway pressure compared to the set level of PEEP. The manometer can detect accurately the airway pressure in the presence of HEPA and HMEF filters in the helmet CPAP and its use should considered.
ABSTRACT
The pathogenesis of COVID-19 appears to be characterized by a dysregulated immune response. During the first pandemic wave in Lombardy, we started to administer glucocorticoids to some patients with severe respiratory failure requiring support with Continuous Positive Airway Pressure (CPAP) therapy. We retrospectively collected data to identify the effect of glucocorticoids in this COVID-19 particular population. With a multidisciplinary consensus, we administered to selected patients with severe COVID-19 disease (PaO2/FiO(2) 159 +/- 71 mmHg) 0,91 mg/kg/die of methylprednisolone equivalent dose after a median of 8 days of hospitalization. In our study we compared 57 patients from the steroid group with 123 from the control group: the event of invasive mechanical ventilation or death was reduced by 43% between steroid group and control group (19.3 % vs. 34.1 % respectively, p=0.001) and mortality was reduced by about 31% between steroid and usual care alone (15.8 % vs. 22.8 % respectively, p=0.011). Corticosteroids in selected COVID-19 patients may have a relevant impact on outcome, better profiling of the heterogeneity of this disease may be essential to guarantee the best treatment choices.
ABSTRACT
Rationale: Lesion segmentation is a critical step in medical image analysis, and methods to identify pathology without time-intensive manual labeling of data are of utmost importance during a pandemic and in resource-constrained healthcare settings. Here, we describe an unsupervised method of automatic lesion segmentation and quantification of COVID-19 lung tissue on chest Computed Tomography (CT) scans. Methods: Anonymized human COVID-19 (n=53), and non-pathologic control (n=87) inspiratory CT scans were used to train a publically available cycle-consistent generative adversarial network (CycleGAN), to convert the COVID-19 CT scans into generated "healthy" equivalents. Difference maps were created by subtracting the Hounsfield Units (HU) value for each voxel in the generated image from that of the original COVID-19 image. We then used these difference maps to construct 3D lesion segmentations to further quantitatively characterize COVID-19 lesions in an automated pipeline. Results: The CycleGAN produced lesion segmentations from COVID-19 CT scans of varying radiologic severity ranging from cases of patchy ground glass opacities to diffuse consolidative lesions. Images of COVID-19 patients showed higher HU intensity in original vs. generated images at sites of pulmonary lesions, while preserving normal parenchyma, fissures, vasculature, and airways (Figure 1, upper panels). The generated images showed larger lung gas volumes and lower tissue masses compared to their corresponding original COVID-19 images (p<0.001). Subtraction of the generated images from their corresponding original COVID-19 CT scans yielded difference maps showing the pathological tissue alone (Figure 1). Control, non-pathologic CT images were given as input to the CycleGAN, resulting in generated images nearly superimposable with the originals with no difference in gas volume or tissue mass (Figure 1, lower panel). Conclusions: To our knowledge, this is the first unsupervised COVID-19 lesion segmentation approach. Our automated lesion model performed well in mild and severe COVID-19 cases without the need for manually labelled lung segmentations as inputs. An automated lesion segmentation model can be used clinically to rapidly and objectively quantify pathologic pulmonary tissue to inform disease prognosis and treatment. Automated radiologic techniques, such as our model, circumvent the traditional bottle-neck of manually labeling data which has limited the scale and thus the impact of quantitative radiologic medical research.
ABSTRACT
Recent estimates suggest that up to 34% of frontline workers in healthcare (FLWs) at the forefront of the COVID-19 pandemic response are reporting elevated symptoms of psychological distress due to resource constraints, ineffective treatments, and concerns about self-contamination. However, little systematic research has been carried out to assess the mental health needs of FLWs in Europe, or the extent of psychological suffering in FLWs within different European countries of varying outbreak severity. Accordingly, this project will employ a mixed-methods approach over three work packages to develop best-practice guidelines for alleviating psychological distress in FLWs during the different phases of the pandemic. Work package 1 will identify the point and long-term prevalence of psychological distress symptoms in a sample of Irish and Italian FLWs, and the predictors of these symptoms. Work package 2 will perform a qualitative needs assessment on a sample of Irish and Italian FLWs to identify sources of stress and resilience, barriers to psychological care, and optimal strategies for alleviating psychological distress in relation to the COVID-19 pandemic. Work package 3 will synthesise the findings from the preceding work packages to draft best practice guidelines, which will be co-created by a multidisciplinary panel of experts using the Delphi method. The guidelines will provide clinicians with a framework for alleviating psychological distress in FLWs, with particular relevance to the COVID-19 pandemic, but may also have relevance for future pandemics and other public health emergencies.