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Background: Cardiotoxicity is frequently monitored when anti-HER2 treatment (Tx) is used and is a potential reason for discontinuation. As patients (pts) live longer, improved surveillance via low-interventional management is critical. Advances in digital tools and the impact of COVID-19 are accelerating opportunities for in-home care. We initiated cardiac monitoring at home in a subset of patients from study NCT04395508, which provides continuity of care during the pandemic and home administration of pertuzumab, trastuzumab and hyaluronidase-zzxf (PH FDC SC) for pts with HER2-positive breast cancer. Methods: Pts must be on/will receive maintenance intravenous pertuzumab + trastuzumab/PH FDC SC/subcutaneous trastuzumab post-chemotherapy completion. Up to 36 pts will be enrolled at Memorial Sloan Kettering Cancer Center and selected Mayo Clinic sites. Pts will undergo remote cardiac surveillance by a Home Health Nursing Provider via Caption Artificial Intelligence (AI)-guided ECHO and ECG (KardiaMobile 6L) after a reference in-clinic ECHO and 12-lead ECG. Images and tracing will be assessed centrally. Key objectives are to evaluate feasibility of LVEF assessment at home based on AI-guided cardiac ultrasound images acquired by novice users without prior ECHO experience and to evaluate feasibility, including recording frequency and signal quality, of an AI-guided ECG algorithm at home by the pt with nurse oversight. Results: Enrollment began Aug 2021. Conclusion: For healthcare systems, HARRIET can improve Tx monitoring to avoid premature discontinuation;is a step toward moving away from specialized sites to flexible healthcare delivery and lower cost of care;and can remove logistical, financial and workload barriers of scheduling in-person ECHO readings. For pts, it can optimize care and increase confidence in anti-HER2 Tx and can provide access to specialty care in the comfort of their own home.
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Background : Infection with SARS-CoV-2 leads to an altered hemostatic system and Covid-19 associated coagulopathy (CAC). Platelet counts remain overall unaltered, but thromboembolic events are frequently reported. Studies on the contribution of platelets to CAC are emerging but still lacking precise cohort comparison and broad analyses of platelet markers. Aims : We aimed to analyze platelet receptor expression and function on platelets and biomarkers in platelet-poor plasma to investigate the role of platelets in the onset of critical progression of CAC. Methods : Extensive platelet function analyses were performed on 34 critically-ill patients with Covid-19 and data was compared to sepsis patients ( n = 24) and non-SARS-CoV-2 acute infection ( n = 18). Tests included PFA-200, aggregometry, flow cytometry and whole mount TEM. Plasma levels of TPO, sCD62P and sGPVI were determined by ELISA. For all patients, relatives, and for healthy controls ( n = 10) informed consent was obtained. Results : While platelet counts in patients of our Covid-19 cohort were expectably unaltered, platelet function was severely impaired in multiple assays. Platelets failed to aggregate in response to ADP or TRAP-6 and could not activate integrin response or release α-granules. The amount of platelet-leukocyte aggregates was markedly elevated, indicating previous platelet activation in line with higher levels of sCD62P and sGPVI. Remarkably, we observed platelet exhaustion in Covid-19 patients using whole mount TEM by means of a lack of dense granules corroborating with impaired uptake of mepacrine. Conclusions : Our data imply that SARS-CoV-2 infection leads to a sub-threshold activation of platelets in a way that they become activated already before critical disease progression, without being cleared from the circulation, which is in striking contrast to sepsis. The platelet pool appears to be exhausted with detrimental consequences for thrombus stability and the risk of thromboembolic events. The mere platelet count in Covid-19 does thus not reflect progression to CAC, whereas platelet function is of high prognostic relevance.
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Rationale: The disease caused by the novel coronavirus (COVID-19) can cause severe hypoxemia even at early stages of disease progression, in some cases without dyspnea or extensive loss of aeration. Early reports and case studies generated several theorized mechanisms of pathophysiological alterations. In this study, we used a mathematical model to investigate the relative effects of lung perfusion abnormalities suspected to produce hypoxemia in early COVID-19: (1) intrapulmonary shunt resulting from vascular dysregulation and virus-related alterations to hypoxic pulmonary vasoconstriction, (2) perfusion defects resulting from thrombosis-mediated microembolism, and (3) venous admixture resulting from ventilation-perfusion mismatching throughout the noninjured lung. Our goal was to quantitatively assess whether the proposed mechanisms of hypoxemia in early COVID-19 are physiologically plausible, particularly in terms of how sensitive oxygenation is to each type of alteration. Materials & Methods: A twelve-compartment mathematical model of the lungs was designed to represent distributed ventilation and perfusion in various lung regions partitioned according to aerated vs. injured status, presence or absence of perfusion defect, and three different height levels. Regional vascular resistance was determined for each compartment, followed by calculation of end-capillary oxygen content. Mixed arterial oxygen content was computed by a perfusion-weighted average of compartmental oxygen contents. Arterial oxygen tension and overall shunt fraction for each scenario were then compared to clinical observations from early reports of COVID-19 patients. Results: In the absence of perfusion defects as well as ventilation-perfusion mismatching throughout the noninjured lung, severe hypoxemia resulting from vascular dysregulation alone required not just impairment of hypoxic pulmonary vasoconstrication, but rather extreme vasodilation in the small region of nonaerated lung (70% reduced vascular resistance). Combined with thrombosis-mediated perfusion defects affecting up to 50% of the normally aerated regions, the requirement for vasodilation in the nonaerated regions was reduced. Finally, accounting for moderate levels of venous admixture resulting from ventilationperfusion mismatching throughout the aerated lung (e.g., due to suboptimal perfusion redistribution), required no vasodilation and only moderate extent of perfusion defect. Conclusion: Evidence for each of the lung perfusion abnormalities investigated in this study can be found in the rapidly evolving body of literature emerging from the COVID-19 pandemic. Individually, our model predicts extreme alterations required for any single mechanism to fully explain observations of severe hypoxemia despite minimal lung involvement at early stages of the disease. By contrast, hypoxemia in early COVID-19 may be explained by relatively small alterations in multiple contributing factors.
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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.
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RATIONALE: Chest computed tomography (CT) has a potential role in the diagnosis, detection of complications, and prognosis of coronavirus disease 2019 (COVID-19). The value of chest CT can be further amplified when associated to physiological variables. Some studies have done efforts to correlate chest CT findings with overall oxygenation and respiratory mechanics, which although they are easily obtained may not be specifically related to COVID-19. Very few studies have tried to correlate chest CT findings with specific biomarkers related to COVID-19. For this purpose, temporal changes of chest CT were evaluated and then correlated with laboratory data in multicenter randomized clinical trial. METHODS: Adult patients who presented chest CT scan features compatible with viral pneumonia were admitted in the hospital and followed during 7 days (NCT: 04561219). CT scans and laboratory data [D-dimer, ferritin, and lactate dehydrogenase (LDH)] in blood were obtained at the moment of admission (Baseline) and on day 7 (Final). Qualitative and quantitative chest CT scan parameters were evaluated in ventral, middle and dorsal regions of interest (ROI) and classified as: hyper-, normal-, poor-, and non-aerated. RESULTS: In this study involving 45 COVID-19 patients no statistically significant differences in the overall Hounsfield Units (HU) ranges and percent of whole lung mass were found overtime. Normally aerated lung tissue reduced from Baseline to Final (p=0.004), mainly associated with a decrease in ventral (p=0.001) and middle (p=0.026) ROIs. At dorsal ROI, a reduction in CT lung mass in poorly aerated areas was observed from Baseline to Final. Poorly aerated and non-aerated lung areas were well correlated only with D-dimer blood levels (r=0.55, p<0.001;and r=0.52, p=0.001, respectively). CONCLUSION: In patients with COVID-19 pneumonia, changes in poor-and non-aerated were associated to changes in D-dimer blood levels, which may be a specific biomarker to be follow in facilities without CT as a way to infer radiologic changes.
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The COVID-19 pandemic has changed many aspects of life and how work is accomplished. Travel restrictions and health concerns have hindered courier trips, making virtual condition reports and deinstallations necessary to retrieve loans. However, transmission pathways of the virus and the related viral attenuation on different materials and surfaces influence employee safety concerns when multiple people interact with surfaces, requiring quarantine periods or disinfection guidelines to be written to address these concerns. This paper illustrates how the National Archives and Records Administration (NARA) and the Victoria and Albert Museum (V&A) worked together virtually to safely return an important 1933 map to the US from England using quarantine periods.
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The current coronavirus disease 2019 (Covid-19) pandemia is a highly dynamic situation characterized by therapeutic and logistic uncertainties. Depending on the effectiveness of social distancing, a shortage of intensive care respirators must be expected. Concomitantly, many physicians and nursing staff are unaware of the capabilities of alternative types of ventilators, hence being unsure if they can be used in intensive care patients. Intensive care respirators were specifically developed for the use in patients with pathological lung mechanics. Nevertheless, modern anesthesia machines offer similar technical capabilities including a number of different modes. However, conceptual differences must be accounted for, requiring close monitoring and the presence of trained personnel. Modern transport ventilators are mainly for bridging purposes as they can only be used with 100% oxygen in contaminated surroundings. Unconventional methods, such as "ventilator-splitting", which have recently received increasing attention on social media, cannot be recommended. This review intends to provide an overview of the conceptual and technical differences of different types of mechanical ventilators.