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Introduction Invasive mechanical ventilation (IMV) is associated with several complications. Placement of a long-term airway (tracheostomy) is also associated with short and long-term risks for patients. Nevertheless, tracheostomies are placed to help reduce the duration of IMV, facilitate weaning and eventually undergo successful decannulation. Methods We performed a narrative review by searching PubMed, Embase and Medline databases to identify relevant citations using the search terms (with synonyms and closely related words) “non-invasive ventilation”, “tracheostomy” and “weaning”. We identified 13 publications comprising retrospective or prospective studies in which non-invasive ventilation (NIV) was one of the strategies used during weaning from IMV and/or tracheostomy decannulation. Results In some studies, patients with tracheostomies represented a subgroup of patients on IMV. Most of the studies involved patients with underlying cardiopulmonary comorbidities and conditions, and primarily involved specialized weaning centres. Not all studies provided data on decannulation, although those which did, report high success rates for weaning and decannulation when using NIV as an adjunct to weaning patient off ventilatory support. However, a significant percentage of patients still needed home NIV after discharge. Conclusions The review supports a potential role for NIV in weaning patients with a tracheostomy either off the ventilator and/or with its decannulation. Additional research is needed to develop weaning protocols and better characterize the role of NIV during weaning.

Background: As the global outbreak of COVID-19 continues to ravage the world, it is important to understand how frontline clinicians manage ventilatory support and the various limiting factors. / Methods: An online survey composed of 32 questions was developed and validated by an international expert panel. / Results: Overall, 502 respondents from 40 countries across six continents completed the survey. The mean number (±SD) of ICU beds was 64 ± 84. The most popular initial diagnostic tools used for treatment initiation were arterial blood gas (48%) and clinical presentation (37.5%), while the national COVID-19 guidelines were the most used (61.2%). High flow nasal cannula (HFNC) (53.8%), non-invasive ventilation (NIV) (47%), and invasive mechanical ventilation (IMV) (92%) were mostly used for mild, moderate, and severe COVID-19 cases, respectively. However, only 38.8%, 56.6% and 82.9% of the respondents had standard protocols for HFNC, NIV, and IMV, respectively. The most frequently used modes of IMV and NIV were volume control (VC) (36.1%) and continuous positive airway pressure/pressure support (CPAP/PS) (40.6%). About 54% of the respondents did not adhere to the recommended, regular ventilator check interval. The majority of the respondents (85.7%) used proning with IMV, with 48.4% using it for 12– 16 hours, and 46.2% had tried awake proning in combination with HFNC or NIV. Increased staff workload (45.02%), lack of trained staff (44.22%) and shortage of personal protective equipment (PPE) (42.63%) were the main barriers to COVID-19 management. / Conclusion: Our results show that general clinical practices involving ventilatory support were highly heterogeneous, with limited use of standard protocols and most frontline clinicians depending on isolated and varied management guidelines. We found increased staff workload, lack of trained staff and shortage of PPE to be the main limiting factors affecting global COVID-19 ventilatory support management.

The prehospital use of noninvasive ventilation (NIV) by emergency medical services is increasing. Applying NIV in the prehospital setting began to gain more attention in the late 1990s when the primary form of noninvasive positive pressure ventilation emerged as a substitute to endotracheal intubation. For the last several years, NIV has become the standard of care for acute cardiogenic pulmonary edema and exacerbation of chronic obstructive pulmonary disease patients in the prehospital setting. A remarkable number of studies demonstrate a reduction in mortality and intubation rates in comparison to standard care when NIV is initiated in the prehospital setting, though there is a lack of evidence to strongly recommend the use of prehospital NIV as a first choice. An in-depth understanding of the science and technological background of NIV machines and interfaces can help attending clinicians in the prehospital setting and thus enhance therapeutic effectiveness by maximizing patient comfort, safety, and stability. Selections of the patients, devices, and interfaces, as well as achieving good patient-ventilator synchrony, are the key aspects of a successful outcome.

The problem of reverse-engineering the evolution of a dynamic network, known broadly as network archaeology, is one of profound importance in diverse application domains. In analysis of infection spread, it reveals the spatial and temporal processes underlying infection. In analysis of biomolecular interaction networks (e.g., protein interaction networks), it reveals early molecules that are known to be differentially implicated in diseases. In economic networks, it reveals flow of capital and associated actors. Beyond these recognized applications, it provides analytical substrates for novel studies – for instance, on the structural and functional evolution of the human brain connectome. In this paper, we model, formulate, and rigorously analyze the problem of inferring the arrival order of nodes in a dynamic network from a single snapshot. We derive limits on solutions to the problem, present methods that approach this limit, and demonstrate the methods on a range of applications, from inferring the evolution of the human brain connectome to conventional citation and social networks, where ground truth is known.

BackgroundClinical and research utility of non-cardiac ultrasound (US) in chronic obstructive pulmonary disease (COPD) has been widely investigated. However, there is no systematic review assessing the clinical values of non-cardiac US techniques in COPD.MethodsWe systematically searched electronic databases from inception to 24 June 2020. Two independent reviewers in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines extracted data. A narrative synthesis of the results was conducted considering non-cardiac US techniques that looked for diaphragm, muscles and bones in patients with COPD.ResultsIn total, 2573 abstracts were screened, and 94 full-text papers were reviewed. A total of 54 studies met the inclusion criteria. Thirty-five studies assessed the diaphragm, while 19 studies evaluated different muscles, including limb muscles and pulmonary lesions in COPD using US. Of the 54 included studies, 30% (16/54) evaluated the changes in either limb muscles or diaphragmatic features before and after physical interventions; 67% (36/54) assessed the correlations between sonographic features and COPD severity. Indeed, 14/15 and 9/13 studies reported a significant reduction in diaphragm excursion and thickness in COPD compared with healthy subjects, respectively; this was correlated significantly with the severity and prognosis of COPD. Three studies reported links between diaphragm length and COPD, where lower diaphragm length correlated with poorer prognosis and outcomes. Quadriceps (rectus femoris), ankle dorsiflexor (tibialis anterior) and vastus lateralis were the most common muscles in COPD assessed by US. More than 70% (12/17) of the studies reported a significant reduction in the cross-sectional area (CSA) of the rectus femoris, rectus femoris and vastus lateralis thickness in COPD compared with healthy subjects. Quadriceps CSA and thickness correlated positively with COPD prognosis, in which patients with reduced quadriceps CSA and thickness have higher risk of exacerbation, readmission and death.ConclusionUS measurements of diaphragm excursion and thickness, as well as lower limb muscles strength, size and thickness, may provide a safe, portable and effective alternative to radiation-based techniques in diagnosis and prognosis as well as tracking improvement postintervention in patients with COPD.

Acute respiratory distress syndrome (ARDS) is considered a frequent and serious lung disease that is continuously linked with an increase in both morbidity and mortality. Mechanical ventilation (MV) is considered as the gold standard therapy in the management of ARDS; although MV support is lifesaving, it is also associated with potentially harmful threats such as ventilator-induced lung injury (VILI). It is understood from the physiological background itself that VILI has a considerable impact on the prognosis of a patient. Therefore, the current studies show that focusing on key therapeutic elements causing over-distension of the available lung units is more imperative for indicating further damage than how much pressure is used to ventilate. In the past two decades, there has been an increasing trend toward using driving pressure (DP) in the management of ARDS patients in intensive care units (ICUs). Recent studies propose that measuring DP in ARDS patients, in addition to the other respiratory mechanics measurements, may support selecting and customizing appropriate ventilator parameters, which, in turn, improves patient outcomes and decreases mortality rate. Therefore, this review is intended to outline the physiological meaning of DP, the clinical measurement and application of DP and factors limiting DP. Furthermore, measuring DP in non-ARDS patients and recent clinical evidence for the use of DP in the ICUs will be discussed in detail.

Respiratory therapy (RT) profession in India is over 25 years old. Although there have been key advancements in the profession, no history and limited evidence are available about its development and transformation. This article provides a viewpoint on the history and development of the RT field, the evolution of RT education in India, its current state, and the way forward. It also describes in brief the establishment of professional organization, scientific journal, and other major events pertinent to the RT profession in India. This article also emphasizes the need to rationalize the sector and to shape its future to suit the needs of over a billion people who utilize the services of related facilities, directly or indirectly in a highly dynamic healthcare environment. Resolutions to address a few challenges have also been considered, which in our view could be a way forward in this profession. A comprehensive review and assessment was conducted through direct communication, personal interviews, and the review of existing documents in professional organization's archives, representative hospitals, academic institutes, and other relevant texts. The data obtained were evaluated for its relevance and grouped on a thematic basis. This is currently the first paper about the comprehensive history and development of the RT profession in India over the last 25 years.

International audience; Function estimation on Online Social Networks (OSN) is an important field of study in complex network analysis. An efficient way to do function estimation on large networks is to use random walks. We can then defer to the extensive theory of Markov chains to do error analysis of these estimators. In this work we compare two existing techniques, Metropolis-Hastings MCMC and Respondent-Driven Sampling, that use random walks to do function estimation and compare them with a new reinforcement learning based technique. We provide both theoretical and empirical analyses for the estimators we consider.

International audience; Are Online Social Network (OSN) A users more likely to form friendships with those with similar attributes? Do users at an OSN B score content more favorably than OSN C users? Such questions frequently arise in the context of Social Network Analysis (SNA) but often crawling an OSN network via its Application Programming Interface (API) is the only way to gather data from a third party. To date, these partial API crawls are the majority of public datasets and the synonym of lack of statistical guarantees in incomplete-data comparisons, severely limiting SNA research progress. Using regenerative properties of the random walks, we propose estimation techniques based on short crawls that have proven statistical guarantees. Moreover, our short crawls can be implemented in massively distributed algorithms. We also provide an adaptive crawler that makes our method parameter-free, significantly improving our statistical guarantees. We then derive the Bayesian approximation of the posterior of the estimates, and in addition, obtain an estima-tor for the expected value of node and edge statistics in an equivalent configuration model or Chung-Lu random graph model of the given network (where nodes are connected randomly) and use it as a basis for testing null hypotheses. The theoretical results are supported with simulations on a variety of real-world networks.