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Background: Although the mental health burden in healthcare workers caused by COVID-19 has gained increasing attention both within the profession and through public opinion, there has been a lack of data describing their experience; specifically, the mental wellbeing of healthcare workers in the intensive care unit (ICU), including those redeployed. Aims: The authors aimed to compare the mental health status of ICU healthcare workers (physicians, nurses and allied health professionals) affected by various factors during the COVID-19 pandemic; and highlight to policymakers areas of staff vulnerabilities in order to improve wellbeing strategies within healthcare systems. Methods: An online survey using three validated scales was conducted in France, the UK, Italy, Mainland China, Taiwan, Egypt and Belgium. Findings: The proportion of respondents who screened positive on the three scales across the countries was 16–49% for depression, 60–86% for insomnia and 17–35% for post-traumatic stress disorder. The authors also identified an increase in the scores with longer time spent in personal protective equipment, female gender, advancing age and redeployed status. Conclusion: The high prevalence of mental disorders among ICU staff during the COVID-19 crisis should inform local and national wellbeing policies.

Rationale: Whether critical care improvements over the last 10 years extend to all hospitals has not been described. Objectives: To examine the temporal trends of critical care outcomes in minority and non–minority-serving hospitals using an inception cohort of critically ill patients. Measurements and Main Results: Using the Philips Health Care electronic ICU Research Institute Database, we identified minority-serving hospitals as those with an African American or Hispanic ICU census more than twice its regional mean. We examined almost 1.1 million critical illness admissions among 208 ICUs from across the United States admitted between 2006 and 2016. Adjusted hospital mortality (primary) and length of hospitalization (secondary) were the main outcomes. Large pluralities of African American (25%, n = 27,242) and Hispanic individuals (48%, n = 26,743) were cared for in minority-serving hospitals, compared with only 5.2% (n = 42,941) of white individuals. Over the last 10 years, although the risk of critical illness mortality steadily decreased by 2% per year (95% confidence interval [CI], 0.97–0.98) in non–minority-serving hospitals, outcomes within minority-serving hospitals did not improve comparably. This disparity in temporal trends was particularly noticeable among African American individuals, where each additional calendar year was associated with a 3% (95% CI, 0.96–0.97) lower adjusted critical illness mortality within a non–minority-serving hospital, but no change within minority-serving hospitals (hazard ratio, 0.99; 95% CI, 0.97–1.01). Similarly, although ICU and hospital lengths of stay decreased by 0.08 (95% CI, −0.08 to −0.07) and 0.16 (95% CI, −0.16 to −0.15) days per additional calendar year, respectively, in non–minority-serving hospitals, there was little temporal change for African American individuals in minority-serving hospitals. Conclusions: Critically ill African American individuals are disproportionately cared for in minority-serving hospitals, which have shown significantly less improvement than non–minority-serving hospitals over the last 10 years.

OBJECTIVES: Although one third or more of critically ill patients in the United States are obese, obesity is not incorporated as a contributing factor in any of the commonly used severity of illness scores. We hypothesize that selected severity of illness scores would perform differently if body mass index categorization was incorporated and that the performance of these score models would improve after consideration of body mass index as an additional model feature. DESIGN: Retrospective cohort analysis from a multicenter ICU database which contains deidentified data for more than 200,000 ICU admissions from 208 distinct ICUs across the United States between 2014 and 2015. SETTING: First ICU admission of patients with documented height and weight. PATIENTS: One-hundred eight-thousand four-hundred two patients from 189 different ICUs across United States were included in the analyses, of whom 4,661 (4%) were classified as underweight, 32,134 (30%) as normal weight, 32,278 (30%) as overweight, 30,259 (28%) as obese, and 9,070 (8%) as morbidly obese.None. MEASUREMENTS AND MAIN RESULTS: To assess the effect of adding body mass index as a risk adjustment element to the Acute Physiology and Chronic Health Evaluation IV and Oxford Acute Severity of Illness scoring systems, we examined the impact of this addition on both discrimination and calibration. We performed three assessments based upon 1) the original scoring systems, 2) a recalibrated version of the systems, and 3) a recalibrated version incorporating body mass index as a covariate. We also performed a subgroup analysis in groups defined using World Health Organization guidelines for obesity. Incorporating body mass index into the models provided a minor improvement in both discrimination and calibration. In a subgroup analysis, model discrimination was higher in groups with higher body mass index, but calibration worsened. CONCLUSIONS: The performance of ICU prognostic models utilizing body mass index category as a scoring element was inconsistent across body mass index categories. Overall, adding body mass index as a risk adjustment variable led only to a minor improvement in scoring system performance.

This focused review summarizes the medical, logistical and environmental challenges that would be associated with dealing with a traumatic surgical case during an interplanetary space mission in the near future.

Purpose The trajectory of mechanically ventilated patients with coronavirus disease 2019 (COVID-19) is essential for clinical decisions, yet the focus so far has been on admission characteristics without consideration of the dynamic course of the disease in the context of applied therapeutic interventions. Methods We included adult patients undergoing invasive mechanical ventilation (IMV) within 48 h of intensive care unit (ICU) admission with complete clinical data until ICU death or discharge. We examined the importance of factors associated with disease progression over the first week, implementation and responsiveness to interventions used in acute respiratory distress syndrome (ARDS), and ICU outcome. We used machine learning (ML) and Explainable Artificial Intelligence (XAI) methods to characterise the evolution of clinical parameters and our ICU data visualisation tool is available as a web-based widget (https://www.CovidUK.ICU). Results Data for 633 adults with COVID-19 who underwent IMV between 01 March 2020 and 31 August 2020 were analysed. Overall mortality was 43.3% and highest with non-resolution of hypoxaemia [60.4% vs17.6%; P

We follow with interest the unprecedented shift towards virtual healthcare during the COVID-19 pandemic. We echo concerns reported by colleagues of the fine balance between a need for global initiatives in cutting traditional red tape to enable rapid deployment of virtual health infrastructures

Air travel during the COVID-19 pandemic is challenging for travellers, airlines, airports, health authorities, and governments. We reviewed multiple aspects of COVID peri-pandemic air travel, including data on traveller numbers, peri-flight prevention, and testing recommendations and in-flight SARS-CoV-2 transmission, photo-epidemiology of mask use, the pausing of air travel to mass gathering events, and quarantine measures and their effectiveness. Flights are reduced by 43% compared to 2019. Hygiene measures, mask use, and distancing are effective, while temperature screening has been shown to be unreliable. Although the risk of in-flight transmission is considered to be very low, estimated at one case per 27 million travellers, confirmed in-flight cases have been published. Some models exist and predict minimal risk but fail to consider human behavior and airline procedures variations. Despite aircraft high-efficiency filtering, there is some evidence that passengers within two rows of an index case are at higher risk. Air travel to mass gatherings should be avoided. Antigen testing is useful but impaired by time lag to results. Widespread application of solutions such as saliva-based, rapid testing or even detection with the help of "sniffer dogs" might be the way forward. The "traffic light system" for traveling, recently introduced by the Council of the European Union is a first step towards normalization of air travel. Quarantine of travellers may delay introduction or re-introduction of the virus, or may delay the peak of transmission, but the effect is small and there is limited evidence. New protocols detailing on-arrival, rapid testing and tracing are indicated to ensure that restricted movement is pragmatically implemented. Guidelines from airlines are non-transparent. Most airlines disinfect their flights and enforce wearing masks and social distancing to a certain degree. A layered approach of non-pharmaceutical interventions, screening and testing procedures, implementation and adherence to distancing, hygiene measures and mask use at airports, in-flight and throughout the entire journey together with pragmatic post-flight testing and tracing are all effective measures that can be implemented. Ongoing research and systematic review are indicated to provide evidence on the utility of preventive measures and to help answer the question "is it safe to fly?".

BackgroundTo date the description of mechanically ventilated patients with Coronavirus Disease 2019 (COVID-19) has focussed on admission characteristics with no consideration of the dynamic course of the disease. Here, we present a data-driven analysis of granular, daily data from a representative proportion of patients undergoing invasive mechanical ventilation (IMV) within the United Kingdom (UK) to evaluate the complete natural history of COVID-19.MethodsWe included adult patients undergoing IMV within 48 hours of ICU admission with complete clinical data until intensive care unit (ICU) death or discharge. We examined factors and trajectories that determined disease progression and responsiveness to interventions used in acute respiratory distress syndrome (ARDS). Our data visualisation tool is available as a web-based widget (https://www.CovidUK.ICU).FindingsData for 633 adults with COVID-19 who were mechanically ventilated between 01 March 2020 and 31 August 2020 were analysed. Mortality, intensity of mechanical ventilation and severity of organ injury increased with severity of hypoxaemia. Median PaO2/FiO2 in non-survivors on the day of death was 12.3(8.9-18.4) kPa suggesting severe refractory hypoxaemia as a major contributor to mortality. Non-resolution of hypoxaemia over the first week of IMV was associated with higher ICU mortality (60.4% versus 17.6%; PInterpretationA sizeable proportion of patients with progressive worsening of hypoxaemia had no application of and were refractory to evidence based ARDS strategies and showed a higher mortality. Strategies for early recognition and management of COVID-19 patients refractory to conventional management strategies will be critical to improving future outcomes.Research in contextEvidence before this studyBeyond the regular literature expertise of our consortium, we enhanced our literature review - due to the fast-evolving Covid-19 publication situation-by searching PubMed for articles published in English or with English language abstracts on October 26, 2020 (and before), with the terms “mechanical ventilation”, “prone position”, “AND (“coronavirus” OR “COVID-19”). Studies including patients not receiving ventilation were excluded, as were those reporting on paediatric and single-centre populations. Note, that neither of those studies analysed the data with respect to the temporal evolution of patients and at our level of granularity. Only four multicentre studies reported detailed ventilator settings and outcomes in ventilated patients with COVID-19. All studies showed only ventilator settings with restricted time points either on admission or the first 4 days of admission. None enabled granular visualisation and analysis of longitudinal ICU trajectory and management.Added value of this studyThis study provides a comprehensive analysis and visualisation of routine clinical measurements tracking the whole ICU time course of patients undergoing invasive mechanical ventilation for COVID-19. Mechanically ventilated patients with COVID-19 have a different natural history and trajectory from descriptions of non-COVID ARDS patients, not predictable from admission physiology. Refractory hypoxaemia is an attributable factor associated with poor outcomes in Covid-19 and hence, understanding of use and utility of evidence-based ARDS interventions is clinically crucial. Opportunities to apply prone positioning appropriately are frequently missed, application of high levels of PEEP, and higher tidal volume delivery than planned is common. Lack of responsiveness to advanced ARDS management is associated with hypercoagulation and cardiovascular instability. These data may help homogenise future clinical management protocols and suggest change-of-practice trials.Implications of all the available evidenceThis study shows that disease progression in Covid-19 during the first surge occurred more frequently and for longer than other forms of respiratory failure from pre-Covid19 studies. Furthermore, variations in clinical practise occur across sites which may benefit from standardisation of evidence-based practise. Patients that do not resolve hypoxaemia over the first week have a significantly higher mortality, and, crucially, that a significant proportion are refractory to prone interventions and show variability in responses to PEEP changes. Opportunities to implement prone position were missed in many patients and this was compounded with its reduced effect on oxygenation with delayed application. This lack of responsiveness is related to indices of inflammation, thrombosis, and cardiac dysfunction suggesting that pulmonary thrombosis could influence prone responsiveness and should be pro-actively investigated in the setting of refractory Covid-19 ARDS. Prediction of failure to resolve or respond to ARDS interventions could further focus research on this group with worse outcome.

The use of artificial intelligence (AI) in a variety of research fields is speeding up multiple digital revolutions, from shifting paradigms in healthcare, precision medicine and wearable sensing, to public services and education offered to the masses around the world, to future cities made optimally efficient by autonomous driving. When a revolution happens, the consequences are not obvious straight away, and to date, there is no uniformly adapted framework to guide AI research to ensure a sustainable societal transition. To answer this need, here we analyze three key challenges to interdisciplinary AI research, and deliver three broad conclusions: 1) future development of AI should not only impact other scientific domains but should also take inspiration and benefit from other fields of science, 2) AI research must be accompanied by decision explainability, dataset bias transparency as well as development of evaluation methodologies and creation of regulatory agencies to ensure responsibility, and 3) AI education should receive more attention, efforts and innovation from the educational and scientific communities. Our analysis is of interest not only to AI practitioners but also to other researchers and the general public as it offers ways to guide the emerging collaborations and interactions toward the most fruitful outcomes.

BackgroundWith the “Artemis”-mission mankind will return to the Moon by 2024. Prolonged periods in space will not only present physical and psychological challenges to the astronauts, but also pose risks concerning the medical treatment capabilities of the crew. So far, no guideline exists for the treatment of severe medical emergencies in microgravity. We, as a international group of researchers related to the field of aerospace medicine and critical care, took on the challenge and developed a an evidence-based guideline for the arguably most severe medical emergency – cardiac arrest.MethodsAfter the creation of said international group, PICO questions regarding the topic cardiopulmonary resuscitation in microgravity were developed to guide the systematic literature research. Afterwards a precise search strategy was compiled which was then applied to “MEDLINE”. Four thousand one hundred sixty-five findings were retrieved and consecutively screened by at least 2 reviewers. This led to 88 original publications that were acquired in full-text version and then critically appraised using the GRADE methodology. Those studies formed to basis for the guideline recommendations that were designed by at least 2 experts on the given field. Afterwards those recommendations were subject to a consensus finding process according to the DELPHI-methodology.ResultsWe recommend a differentiated approach to CPR in microgravity with a division into basic life support (BLS) and advanced life support (ALS) similar to the Earth-based guidelines. In immediate BLS, the chest compression method of choice is the Evetts-Russomano method (ER), whereas in an ALS scenario, with the patient being restrained on the Crew Medical Restraint System, the handstand method (HS) should be applied. Airway management should only be performed if at least two rescuers are present and the patient has been restrained. A supraglottic airway device should be used for airway management where crew members untrained in tracheal intubation (TI) are involved.DiscussionCPR in microgravity is feasible and should be applied according to the Earth-based guidelines of the AHA/ERC in relation to fundamental statements, like urgent recognition and action, focus on high-quality chest compressions, compression depth and compression-ventilation ratio. However, the special circumstances presented by microgravity and spaceflight must be considered concerning central points such as rescuer position and methods for the performance of chest compressions, airway management and defibrillation.

Background The risk of severe medical and surgical events during long-duration spaceflight is significant. In space, many environmental and psychological factors may make tracheal intubation more difficult than on Earth. We hypothesised that, in microgravity, tracheal intubation may be facilitated by the use of a videolaryngoscope compared with direct laryngoscopy. Methods In a non-randomised, controlled, cross-over simulation study, we compared intubation performance of novice operators and experts, using either a direct laryngoscope or a videolaryngoscope, in weightlessness and in normogravity. The primary outcome was the success rate of tracheal intubation. Time to intubation and the confidence score into the success of tube placement were also recorded. Results When novices attempted to intubate the trachea in microgravity, the success rate of tracheal intubation using a videolaryngoscope was significantly higher (20/25 [80%]; 95% confidence interval [CI], 64.3–95.7 vs eight/20 [40%]; 95% CI, 18.5–61.5; P=0.006), and intubation time was shorter, compared with using a direct laryngoscope. In normogravity, the success rate of tracheal intubation by experts was significantly higher than that by novices (16/20 [80%]; 95% CI, 62.5–97.5 vs seven/25 [28%]; 95% CI, 10.4–45.6; P=0.001), but in microgravity, there was no significant difference between the experts and novices (19/20 [95%]; 95% CI, 85.4–100 vs 20/25 [80%]; 95% CI, 64.3–95.7; P=0.113). Higher confidence scores were achieved with videolaryngoscopy compared with direct laryngoscopy by both experts and novices in both microgravity and normogravity. Conclusions Videolaryngoscopy was associated with higher intubation success rate and speed, and higher confidence for correct tube placement by novice operators in microgravity, and as such may represent the best technique for advanced airway management during long-duration spaceflight.

OBJECTIVES: To investigate patients' characteristics, management, and outcomes in the critically ill population admitted to the ICU for severe acute respiratory syndrome coronavirus disease 2019 pneumonia causing an acute respiratory distress syndrome. DESIGN: Retrospective case-control study. SETTING: A 34-bed ICU of a tertiary hospital. PATIENTS: The first 44 coronavirus disease 2019 acute respiratory distress syndrome patients were compared with a historical control group of 39 consecutive acute respiratory distress syndrome patients admitted to the ICU just before the coronavirus disease 2019 crisis. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Obesity was the most frequent comorbidity exhibited by coronavirus disease 2019 patients (n = 32, 73% vs n = 11, 28% in controls; p < 0.001). Despite the same severity of illness and level of hypoxemia at admission, coronavirus disease 2019 patients failed more high flow oxygen via nasal cannula challenges (n = 16, 100% vs n = 5, 45% in controls; p = 0.002), were more often intubated (n = 44, 100% vs n = 22, 56% in controls; p < 0.001) and paralyzed (n = 34, 77% vs n = 3, 14% in controls; p < 0.001), required higher level of positive end-expiratory pressure (15 vs 8 cm H2O in controls; p < 0.001), more prone positioning (n = 33, 75% vs n = 6, 27% in controls; p < 0.001), more dialysis (n = 16, 36% vs n = 3, 8% in controls; p = 0.003), more hemodynamic support by vasopressors (n = 36, 82% vs n = 22, 56% in controls; p = 0.001), and had more often a prolonged weaning from mechanical ventilation (n = 28, 64% vs n = 10, 26% in controls; p < 0.01) resulting in a more frequent resort to tracheostomy (n = 18, 40.9% vs n = 2, 9% in controls; p = 0.01). However, an intensive management requiring more staff per patient for positioning coronavirus disease 2019 subjects (6 [5-7] vs 5 [4-5] in controls; p < 0.001) yielded the same ICU survival rate in the two groups (n = 34, 77% vs n = 29, 74% in controls; p = 0.23). CONCLUSIONS: In its most severe form, coronavirus disease 2019 pneumonia striked preferentially the vulnerable obese population, evolved toward a multiple organ failure, required prolonged mechanical ventilatory support, and resulted in a high workload for the caregivers.

Objective To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. Design Systematic review. Data sources Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019. Eligibility criteria for selecting studies Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning research. The main distinguishing feature of convolutional neural networks (CNNs) in deep learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition. The algorithm learns for itself the features of an image that are important for classification rather than being told by humans which features to use. The selected studies aimed to use medical imaging for predicting absolute risk of existing disease or classification into diagnostic groups (eg, disease or non-disease). For example, raw chest radiographs tagged with a label such as pneumothorax or no pneumothorax and the CNN learning which pixel patterns suggest pneumothorax. Review methods Adherence to reporting standards was assessed by using CONSORT (consolidated standards of reporting trials) for randomised studies and TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) for non-randomised studies. Risk of bias was assessed by using the Cochrane risk of bias tool for randomised studies and PROBAST (prediction model risk of bias assessment tool) for non-randomised studies. Results Only 10 records were found for deep learning randomised clinical trials, two of which have been published (with low risk of bias, except for lack of blinding, and high adherence to reporting standards) and eight are ongoing. Of 81 non-randomised clinical trials identified, only nine were prospective and just six were tested in a real world clinical setting. The median number of experts in the comparator group was only four (interquartile range 2-9). Full access to all datasets and code was severely limited (unavailable in 95% and 93% of studies, respectively). The overall risk of bias was high in 58 of 81 studies and adherence to reporting standards was suboptimal ( Conclusions Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions. Study registration PROSPERO CRD42019123605.

Zusammenfassung Hintergrund Im Jahr 2017 wurden weltweit etwa 4 Milliarden Menschen mit einem Flugzeug transportiert. Die International Air Transport Association (IATA) prognostiziert für Europa bis zum Jahr 2034 eine durchschnittliche jährliche Steigerung der Passagierzahlen von 2,7%. In der zivilen Luftfahrt treten Notfälle an Bord (sogenannte In-Flight Medical Emergencies, IFME) mit einer Häufigkeit von 1 pro 14 000 bis 1 pro 50 000 transportierte Passagiere auf. Belastbare Daten über die exakte Häufigkeit und Art von medizinischen Notfällen an Bord von Flugzeugen existieren nicht. Ein Herz-Kreislauf-Stillstand tritt statistisch gesehen nur in 0,3% der Notfälle an Bord auf. Dieses Ereignis ist jedoch mit einer Mortalität von 86% assoziiert und stellt daher Passagiere und Crew zugleich vor eine große Herausforderung. Mit der Leitlinie zum In-Flight Cardiac Arrest (IFCA) wurden 28 spezifische Empfehlungen erarbeitet. Ziel der Leitlinie war es, spezifische Handlungsempfehlungen für mitfliegende Ärzte, Kabinenpersonal (Cabin Crew) und auch Fluglinien zur Behandlung des Herz-Kreislauf-Stillstands an Bord von Luftfahrzeugen zu generieren.

ZusammenfassungIm Lichte zukünftiger Langzeitraumfahrtmissionen fernab der Erdumlaufbahn besitzt die Fähigkeit der Crew zur autonomen, medizinischen Versorgung einen besonderen Stellenwert. Hier ist insbesondere die Durchführung einer Anästhesie bei einer dringend notwendigen Operation von entscheidender Bedeutung, um unter Umständen die Mission weiter fortsetzen zu können. Bisher musste noch keine Allgemeinanästhesie in der Raumfahrt durchgeführt werden, sodass die praktische Erfahrung entsprechend limitiert ist. Es erscheint sinnvoll, in Umgebungen mit vergleichbaren medizinischen Versorgungssituationen nach Erfahrungswerten und Behandlungsstrategien zu suchen. Diese Umgebungen sollten über eine oder mehrere der Eigenschaften von Raumfahrtmissionen verfügen. Beispielsweise könnten dies Limitationen bei medizinischem Personal und Ausstattung oder die Unmöglichkeit einer zeitnahen Evakuierung zu einer höherwertigen Versorgungseinrichtung sein. Die vorliegende Übersichtsarbeit fasst die bisher zu diesem Thema publizierte Literatur zusammen und gibt einen Überblick über wichtige und bisher bekannte medizinische Maßnahmen in Schwerelosigkeit.

Objective: Severity of illness scores rest on the assumption that patients have normal physiologic values at baseline and that patients with similar severity of illness scores have the same degree of deviation from their usual state. Prior studies have reported differences in baseline physiology, including laboratory markers, between obese and normal weight individuals, but these differences have not been analyzed in the ICU. We compared deviation from baseline of pertinent ICU laboratory test results between obese and normal weight patients, adjusted for the severity of illness. Design: Retrospective cohort study in a large ICU database. Setting: Tertiary teaching hospital. Patients: Obese and normal weight patients who had laboratory results documented between 3 days and 1 year prior to hospital admission. Interventions: None. Measurements and Main Results: Seven hundred sixty-nine normal weight patients were compared with 1,258 obese patients. After adjusting for the severity of illness score, age, comorbidity index, baseline laboratory result, and ICU type, the following deviations were found to be statistically significant: WBC 0.80 (95% CI, 0.27–1.33) × 109/L; p = 0.003; log (blood urea nitrogen) 0.01 (95% CI, 0.00–0.02); p = 0.014; log (creatinine) 0.03 (95% CI, 0.02–0.05), p < 0.001; with all deviations higher in obese patients. A logistic regression analysis suggested that after adjusting for age and severity of illness at least one of these deviations had a statistically significant effect on hospital mortality (p = 0.009). Conclusions: Among patients with the same severity of illness score, we detected clinically small but significant deviations in WBC, creatinine, and blood urea nitrogen from baseline in obese compared with normal weight patients. These small deviations are likely to be increasingly important as bigger data are analyzed in increasingly precise ways. Recognition of the extent to which all critically ill patients may deviate from their own baseline may improve the objectivity, precision, and generalizability of ICU mortality prediction and severity adjustment models.

Artificial Intelligence (AI) as a novel tool to advance the field ofmedicine has rapidly become a subject of great interest and intense debate, lea ding many stakeholders to prospect on the future role of these technologies and relative responsibilities in decision-making [1–5].Furthermore, the complexity of some AI algorithms, theirlack of transparency and a widespread lack of prospective validation may serve to dishearten physicians. In this short piece, I will attempt to define what are some of the most common AI techniques with applications to intensive care, then I’ll discuss what barriers to implementation exist and why us, doctors, have not been superseded by robot-physicians.

In this Comment, we provide guidelines for reinforcement learning for decisions about patient treatment that we hope will accelerate the rate at which observational cohorts can inform healthcare practice in a safe, risk-conscious manner.

Pro Jahr reisen weltweit uber 3 Mrd. Menschen mit einem Luftfahrzeug. Bezogen auf die Anzahl transportierter Passagiere tritt ein medizinischer Notfall mit einer Haufigkeit von 1/14 000 bis 1/50 000 transportierten Passagieren auf. Der Herz-Kreislauf-Stillstand wahrend eines Fluges (in-flight cardiac arrest, IFCA) hat einen Anteil von 0,3 % an den Notfallen an Bord, besitzt aber andererseits eine Mortalitat von 86 %. Bisher existierte zum IFCA keine evidenzbasierte Leitlinie. Fur die Erstellung einer diesbezuglichen Leitlinie wurde eine Taskforce gegrundet, die publizierte Studien und die daraus abzuleitende Evidenz analysierte. Mittels systematischer Literaturanalyse und der Verwendung der Grade-, Rand- und Delphi-Methodik wurden spezifische Empfehlungen fur die Therapie des IFCA erstellt. Fur verschiedene Bereiche wurden spezifische Empfehlungen durch die Taskforce erarbeitet: Notfallausstattung an Bord, EKG, Reanimationstechniken, (Wiederholungs-)Training fur Besatzungen, Vorgehen nach „Return of Spontaneous Circulation“ (ROSC) und auserplanmasige Zwischenlandungen. Die Empfehlungen geben Arzten an Bord Hinweise, wie auf evidenzbasiertem Niveau mit IFCA-Patienten im Idealfall zu verfahren ist.

Purpose of review Missions to the Moon or more distant planets are planned in the next future, and will push back the limits of our experience in providing medical support in remote environments. Medical preparedness is ongoing, and involves planning for emergency surgical interventions and anaesthetic procedures. This review will summarize what principles of ambulatory anaesthesia on Earth could benefit the environment of a space mission with its unique constraints. Recent findings Ambulatory anaesthesia relies on several principles such as improved patient pathway, correct patient selection, optimized procedural strategies to hasten recovery and active prevention of postoperative complications. Severe limitations in the equipment available and the skills of the crew members represent the key factors to be taken into account when designing the on-board medical system for future interplanetary space missions. Summary The application of some of the key principles of ambulatory anaesthesia, as well as recent advances in anaesthetic techniques and better understanding of human adaptation to the space environment might allow nonanaesthesiologist physicians to perform common anaesthetic procedures, whilst maximizing crew safety and minimizing the impact of medical events on the mission.

Background and objective To develop a machine learning model to predict urine output (UO) in sepsis patients after fluid resuscitation. Methods We identified sepsis patients in the Multiparameter Intelligent Monitoring in Intensive Care-III v1.4 database according to the Sepsis-3 criteria. We focused on two outcomes: whether the UO decreased after fluid administration and whether oliguria (defined as UO less than the threshold of 0.5 mL/kg/h) developed. A gradient tree-based machine learning model implemented with an eXtreme Gradient Boosting algorithm was used to integrate relevant physiological parameters for predicting the aforementioned outcomes. A confusion matrix was computed. Results A total of 232,929 events in 19,275 patients were included. Using decreased UO as the outcome measure, the optimal model achieved an area under the curve (AUC) of 0.86; for predicting oliguria, most models achieved an AUC greater than 0.86, and the highest sensitivity was 92.2% when the model was applied to patients with baseline oliguria. Conclusions Machine learning could help clinicians evaluate fluid status in sepsis patients after fluid administration, thus preventing fluid overload-related complications.

Air travel during the COVID-19 pandemic is challenging for travellers, airlines, airports, health authorities, and governments. We reviewed multiple aspects of COVID peri-pandemic air travel, including data on traveller numbers, peri-flight prevention, and testing recommendations and in-flight SARS-CoV-2 transmission, photo-epidemiology of mask use, the pausing of air travel to mass gathering events, and quarantine measures and their effectiveness. Flights are reduced by 43% compared to 2019. Hygiene measures, mask use, and distancing are effective, while temperature screening has been shown to be unreliable. Although the risk of in-flight transmission is considered to be very low, estimated at one case per 27 million travellers, confirmed in-flight cases have been published. Some models exist and predict minimal risk but fail to consider human behavior and airline procedures variations. Despite aircraft high-efficiency filtering, there is some evidence that passengers within two rows of an index case are at higher risk. Air travel to mass gatherings should be avoided. Antigen testing is useful but impaired by time lag to results. Widespread application of solutions such as saliva-based, rapid testing or even detection with the help of "sniffer dogs" might be the way forward. The "traffic light system" for traveling, recently introduced by the Council of the European Union is a first step towards normalization of air travel. Quarantine of travellers may delay introduction or re-introduction of the virus, or may delay the peak of transmission, but the effect is small and there is limited evidence. New protocols detailing on-arrival, rapid testing and tracing are indicated to ensure that restricted movement is pragmatically implemented. Guidelines from airlines are non-transparent. Most airlines disinfect their flights and enforce wearing masks and social distancing to a certain degree. A layered approach of non-pharmaceutical interventions, screening and testing procedures, implementation and adherence to distancing, hygiene measures and mask use at airports, in-flight and throughout the entire journey together with pragmatic post-flight testing and tracing are all effective measures that can be implemented. Ongoing research and systematic review are indicated to provide evidence on the utility of preventive measures and to help answer the question "is it safe to fly?".

Simulation-based approaches to disease progression allow us to make counterfactual predictions about the effects of an untried series of treatment choices. However, building accurate simulators of disease progression is challenging, limiting the utility of these approaches for real world treatment planning. In this work, we present a novel simulation-based reinforcement learning approach that mixes between models and kernel-based approaches to make its forward predictions. On two real world tasks, managing sepsis and treating HIV, we demonstrate that our approach both learns state-of-the-art treatment policies and can make accurate forward predictions about the effects of treatments on unseen patients.

Travelling to the stars is a dream nearly as old as mankind itself. Nowadays, spaceflight is in many ways common business, becoming even accessible to space tourists. However, many problems remain to be solved before humanity can venture into deep space with an acceptable level of risk, and medical preparedness is one of them. The management of any severe medical emergency will be extremely challenging in this extreme environment, with limited resources in crew and equipment. Here, we discuss the case of a cardiac arrest occurring during spaceflight, and present challenges around the recognition, immediate management including delivering cardio-pulmonary resuscitation and secondary measures such as organ support or evacuation. Given the current treatment capabilities in space, the survivability of cardiac arrest is expected to be lower than on the ground.

Introduction In the near future, space programs will shift their focus toward long-duration interplanetary missions, in particular to the Moon and Mars. These exploration missions will be associated with an increased risk of acute medical problems, which will need to be handled by an autonomous crew operating in extreme isolation. An important skill in emergencies is represented by airway management. Many airway devices are available and it is unclear which one would be the most suitable in the context of a space mission. The aim of this systematic review was to analyze the existing literature on airway management in the special situation of weightlessness during space missions. Material and methods We performed a standardized review of published literature on airway management in spaceflight and analogue environments using the database PubMed. Results We identified a total of 3111 publications of which 3039 were initially excluded after evaluation. The screening identified three randomized comparative manikin studies, two of them in parabolic flights, one in a submerged setup. Under free-floating conditions, the insertion success rate of supraglottic airway devices (SGA) was excellent (91%-97%). The administration of artificial ventilation could be successfully achieved in weightlessness with SGA. The success rate of conventional laryngoscopy under free-floating conditions fluctuated between 15% and 86%. Conclusion It appears possible to safely manage the airway in weightlessness, provided that certain conditions are ensured, such as restraining the patient and operator for conventional orotracheal intubation. If airway protection is required with endotracheal intubation, both the operator and the patient should be restrained.

By the end of the year 2016, approximately 3 billion people worldwide travelled by commercial air transport. Between 1 out of 14,000 and 1 out of 50,000 passengers will experience acute medical problems/emergencies during a flight (i.e., in-flight medical emergency). Cardiac arrest accounts for 0.3% of all in-flight medical emergencies. So far, no specific guideline exists for the management and treatment of in-flight cardiac arrest (IFCA). A task force with clinical and investigational expertise in aviation, aviation medicine, and emergency medicine was created to develop a consensus based on scientific evidence and compiled a guideline for the management and treatment of in-flight cardiac arrests. Using the GRADE, RAND, and DELPHI methods, a systematic literature search was performed in PubMed. Specific recommendations have been developed for the treatment of IFCA. A total of 29 specific recommendations for the treatment and management of in-flight cardiac arrests were generated. The main recommendations included emergency equipments as well as communication of the emergency. Training of the crew is of utmost importance, and should ideally have a focus on CPR in aircraft. The decision for a diversion should be considered very carefully.

The management of severe medical events during spaceflight represents an exceptional challenge. Thanks to the intense medical screening performed during astronaut selection, significant medical problems have remained uncommon during training and space missions, despite some 125 person-years of cumulated presence in space. The risk of severe medical events, particularly during long-term missions, e.g. to Mars, is nevertheless significant. Many gaps still remain in our understanding of the human response to the space environment and several critical risks associated with interplanetary space travel—such as protection against radiation exposure or delivery of autonomous medical care—remain unresolved. Both the expected medical conditions and the acute care capabilities differ greatly between Earth and space. While the options in modern medical facilities on the ground are potentially limitless, the equipment and immediate expertise available in space are very limited. For this reason, an emphasis is put on prevention of emergencies. Even after a successful initial stabilisation in low Earth orbit, postresuscitation care would prove problematic. The International Space Station (ISS) does not have the capability to deliver prolonged life support. Furthermore, evacuation from low Earth orbit with current capabilities may not be possible for an unstable, critically ill or intubated and ventilated patient, because of limited volume in the Soyuz capsule and the high deceleration profile. Future space exploration missions will push back the limits of human experience in maintaining health and performance of crew members. Many issues will have to be addressed before humanity can venture beyond low Earth orbit with an acceptable level of risk.

Background Pneumonia is responsible for approximately 230,000 deaths in Europe, annually. Comprehensive and comparable reports on pneumonia mortality trends across the European Union (EU) are lacking. Methods A temporal analysis of national mortality statistics to compare trends in pneumonia age-standardised death rates (ASDR) of EU countries between 2001 and 2014 was performed. International Classification of Diseases version 10 (ICD-10) codes were used to extract data from the World Health Organisation European Detailed Mortality Database and trends were analysed using Joinpoint regression. Results Median pneumonia mortality across the EU for the last recorded observation was 19.8 / 100,000 and 6.9 / 100,000 for males and females, respectively. Mortality was higher in males across all EU countries, most notably in Estonia and Lithuania where the ratio of male to female ASDR was 4.0 and 3.7, respectively. Gender mortality differences were lowest in the UK and Demark with ASDR ratios of 1.1 and 1.5, respectively. Pneumonia mortality across all countries decreased by a median of 31.0% over the observation period. Countries that demonstrated an increase in pneumonia mortality were Poland (males + 33.1%, females + 10.2%), and Lithuania (males + 6.0%). Conclusions Mortality from pneumonia is improving in most EU countries, however substantial variation in trends remains between countries and between genders. Electronic supplementary material The online version of this article (10.1186/s12931-018-0781-4) contains supplementary material, which is available to authorized users.

Supplemental Digital Content is available in the text., Objective: To determine the association of new-onset atrial fibrillation with outcomes, including ICU length of stay and survival. Design: Retrospective cohort of ICU admissions. We found atrial fibrillation using automated detection (≥ 90 s in 30 min) and classed as new-onset if there was no prior diagnosis of atrial fibrillation. We identified determinants of new-onset atrial fibrillation and, using propensity matching, characterized its impact on outcomes. Setting: Tertiary care academic center. Patients: A total of 8,356 consecutive adult admissions to either the medical or surgical/trauma/burn ICU with available continuous electrocardiogram data. Interventions: None. Measurements and Main Results: From 74 patient-years of every 15-minute observations, we detected atrial fibrillation in 1,610 admissions (19%), with median burden less than 2%. Most atrial fibrillation was paroxysmal; less than 2% of admissions were always in atrial fibrillation. New-onset atrial fibrillation was subclinical or went undocumented in 626, or 8% of all ICU admissions. Advanced age, acute respiratory failure, and sepsis were the strongest predictors of new-onset atrial fibrillation. In propensity-adjusted regression analyses, clinical new-onset atrial fibrillation was associated with increased hospital mortality (odds ratio, 1.63; 95% CI, 1.01–2.63) and longer length of stay (2.25 d; CI, 0.58–3.92). New-onset atrial fibrillation was not associated with survival after hospital discharge (hazard ratio, 0.99; 95% CI, 0.76–1.28 and hazard ratio, 1.11; 95% CI, 0.67–1.83, respectively, for subclinical and clinical new-onset atrial fibrillation). Conclusions: Automated analysis of continuous electrocardiogram heart rate dynamics detects new-onset atrial fibrillation in many ICU patients. Though often transient and frequently unrecognized, new-onset atrial fibrillation is associated with poor hospital outcomes.

During future space exploration missions, the risk of medical events requiring surgery is significant, and will likely rely on anesthetic techniques. Available options during spaceflight include local, regional (nerve block) and general anesthesia. No actual invasive anesthesia was ever performed on humans in space or immediately after landing, and the safe delivery of such advanced medical care in this context is challenging. In the first section of this review, Human adaptation to the space environment is detailed, with a focus on the cardiovascular system, along with a discussion regarding which medical conditions may arise. The second part of the study focuses on discussing the extensive list of challenges associated with delivering an anesthetic procedure in space or on a foreign planetary surface. They schematically fall into two categories: missing technologies (generation of intravenous fluid, specific medical equipment, preservation of drugs…) and missing knowledge (human physiology in partial gravity, use of vasopressors, cardiovascular tolerance of general anesthesia and blood loss, choice of the most appropriate anesthetic technique, medical training). Future space exploration mis¬sions will push back the limits of human expe¬rience in maintaining health and performance of crew members in extreme settings. After more than five decades of research, our understanding of human physiology in weightlessness is advanced. Despite a number of challenges, the safe delivery of an anesthetic procedure on previously healthy individuals and given our current knowledge and technologies remains risky but could be possible even by non-anesthesiologists, and should not represent a showstopper for future space exploration missions.

As informatisation of hospitals continues to spread, increasing amounts of healthcare related data are being collected, and the ICU is no exception. Large datasets are being made available to the scientific community, and offer the potential to answer clinical questions and to develop the next generation of clinical tools. A demonstration of such a tool is presented here, built using data from the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) open database.