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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.

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

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.

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.

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.

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.

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.

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.

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.

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.